WorldWideScience

Sample records for wireless neural signal

  1. Neural signal sampling via the low power wireless pico system.

    Science.gov (United States)

    Cieslewski, Grzegorz; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2006-01-01

    This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.

  2. Using pulse width modulation for wireless transmission of neural signals in multichannel neural recording systems.

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.

  3. Wireless transmission of neural signals using entropy and mutual information compression.

    Science.gov (United States)

    Craciun, Stefan; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2011-02-01

    Two of the most critical tasks when designing a portable wireless neural recording system are to limit power consumption and to efficiently use the limited bandwidth. It is known that for most wireless devices the majority of power is consumed by the wireless transmitter and it often represents the bottleneck of the overall design. This paper compares two compression techniques that take advantage of the sparseness of the neural spikes in neural recordings using an information theoretic formalism to enhance the well-established vector quantization (VQ) algorithm. The two discriminative VQ algorithms are applied to neuronal recordings proving their ability to accurately reconstruct action potential (AP) regions of the neuronal signal while compressing background activity without using thresholds. The two operational modes presented offer distinct characteristics to lossy compression. The first approach requires no preprocessing or prior knowledge of the signal while the second requires a training set of spikes to obtain AP templates. The compression algorithms are implemented on an on-board digital signal processor (DSP) and results show that power consumption is decreased while the bandwidth is more efficiently utilized. The compression algorithms have been tested in real time on a hardware platform (PICO DSP ) enhanced with the DSP which runs the algorithm before sending the compressed data to a wireless transmitter. The compression ratios obtained range from 70:1 and 40:1 depending on the signal to noise ratio (SNR) of the input signal. The spike sorting accuracy in the reconstructed data is 95% compatible to the original neural data.

  4. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  5. Measurement of neural signals from inexpensive, wireless and dry EEG systems.

    Science.gov (United States)

    Grummett, T S; Leibbrandt, R E; Lewis, T W; DeLosAngeles, D; Powers, D M W; Willoughby, J O; Pope, K J; Fitzgibbon, S P

    2015-07-01

    Electroencephalography (EEG) is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. We compared EEG recordings obtained from three systems that are inexpensive, wireless, and/or dry (no gel), against recordings made with a traditional, research-grade EEG system, in order to investigate the ability of these 'non-traditional' systems to produce recordings of comparable quality to a research-grade system. The systems compared were: Emotiv EPOC (inexpensive and wireless), B-Alert (wireless), g.Sahara (dry) and g.HIamp (research-grade). We compared the ability of the systems to demonstrate five well-studied neural phenomena: (1) enhanced alpha activity with eyes closed versus open; (2) visual steady-state response (VSSR); (3) mismatch negativity; (4) P300; and (5) event-related desynchronization/synchronization. All systems measured significant alpha augmentation with eye closure, and were able to measure VSSRs (although these were smaller with g.Sahara). The B-Alert and g.Sahara were able to measure the three time-locked phenomena equivalently to the g.HIamp. The Emotiv EPOC did not have suitably located electrodes for two of the tasks and synchronization considerations meant that data from the time-locked tasks were not assessed. The results show that inexpensive, wireless, or dry systems may be suitable for experimental studies using EEG, depending on the research paradigm, and within the constraints imposed by their limited electrode placement and number.

  6. A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal.

    Science.gov (United States)

    Bonfanti, A; Ceravolo, M; Zambra, G; Gusmeroli, R; Spinelli, A S; Lacaita, A L; Angotzi, G N; Baranauskas, G; Fadiga, L

    2010-01-01

    This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- εm CMOS process with a power consumption of 105 εW per channel (269 εW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm(2). The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments.

  7. Wireless data signal transmission system

    DEFF Research Database (Denmark)

    2014-01-01

    The present invention relates to a method for providing a radio frequency signal for transmission, a system for providing a radio frequency signal for transmission and a method for wireless data transmission between a transmitter and a receiver.......The present invention relates to a method for providing a radio frequency signal for transmission, a system for providing a radio frequency signal for transmission and a method for wireless data transmission between a transmitter and a receiver....

  8. A 400 MHz Wireless Neural Signal Processing IC With 625 $\\times$ On-Chip Data Reduction and Reconfigurable BFSK/QPSK Transmitter Based on Sequential Injection Locking.

    Science.gov (United States)

    Teng, Kok-Hin; Wu, Tong; Liu, Xiayun; Yang, Zhi; Heng, Chun-Huat

    2017-06-01

    An 8-channel wireless neural signal processing IC, which can perform real-time spike detection, alignment, and feature extraction, and wireless data transmission is proposed. A reconfigurable BFSK/QPSK transmitter (TX) at MICS/MedRadio band is incorporated to support different data rate requirement. By using an Exponential Component-Polynomial Component (EC-PC) spike processing unit with an incremental principal component analysis (IPCA) engine, the detection of neural spikes with poor SNR is possible while achieving 625× data reduction. For the TX, a dual-channel at 401 MHz and 403.8 MHz are supported by applying sequential injection locked techniques while attaining phase noise of -102 dBc/Hz at 100 kHz offset. From the measurement, error vector magnitude (EVM) of 4.60%/9.55% with power amplifier (PA) output power of -15 dBm is achieved for the QPSK at 8 Mbps and the BFSK at 12.5 kbps. Fabricated in 65 nm CMOS with an active area of 1 mm 2, the design consumes a total current of 5  ∼ 5.6 mA with a maximum energy efficiency of 0.7 nJ/b.

  9. NeuralWISP: A Wirelessly Powered Neural Interface With 1-m Range.

    Science.gov (United States)

    Yeager, D J; Holleman, J; Prasad, R; Smith, J R; Otis, B P

    2009-12-01

    We present the NeuralWISP, a wireless neural interface operating from far-field radio-frequency RF energy. The NeuralWISP is compatible with commercial RF identification readers and operates at a range up to 1 m. It includes a custom low-noise, low-power amplifier integrated circuit for processing the neural signal and an analog spike detection circuit for reducing digital computational requirements and communications bandwidth. Our system monitors the neural signal and periodically transmits the spike density in a user-programmable time window. The entire system draws an average 20 muA from the harvested 1.8-V supply.

  10. An optical microsystem for wireless neural recording.

    Science.gov (United States)

    Wei, P; Ziaie, B

    2009-01-01

    In this paper, we describe an optical microsystem for wireless neural recording. The system incorporated recording electrodes, integrated electronics, surface-mount LEDs, and a CCD camera. The components were mounted on a PCB platform having a total dimension of 2.2 x 2.2 cm(2), 4 integrated biopotential amplifiers (IBA) and 16 LEDs. The IBAs having a bandwidth of 0.1-93.5Hz with the midband gain of 38 dB were fabricated using AMI 1.6microm technology. The simulated local field potentials (LFP) were amplified and used to drive the LEDs. A CCD camera with a temporal resolution of 30FPS was used to capture the image and retrieve the signal.

  11. Wireless gigabit data telemetry for large-scale neural recording.

    Science.gov (United States)

    Kuan, Yen-Cheng; Lo, Yi-Kai; Kim, Yanghyo; Chang, Mau-Chung Frank; Liu, Wentai

    2015-05-01

    Implantable wireless neural recording from a large ensemble of simultaneously acting neurons is a critical component to thoroughly investigate neural interactions and brain dynamics from freely moving animals. Recent researches have shown the feasibility of simultaneously recording from hundreds of neurons and suggested that the ability of recording a larger number of neurons results in better signal quality. This massive recording inevitably demands a large amount of data transfer. For example, recording 2000 neurons while keeping the signal fidelity ( > 12 bit, > 40 KS/s per neuron) needs approximately a 1-Gb/s data link. Designing a wireless data telemetry system to support such (or higher) data rate while aiming to lower the power consumption of an implantable device imposes a grand challenge on neuroscience community. In this paper, we present a wireless gigabit data telemetry for future large-scale neural recording interface. This telemetry comprises of a pair of low-power gigabit transmitter and receiver operating at 60 GHz, and establishes a short-distance wireless link to transfer the massive amount of neural signals outward from the implanted device. The transmission distance of the received neural signal can be further extended by an externally rendezvous wireless transceiver, which is less power/heat-constraint since it is not at the immediate proximity of the cortex and its radiated signal is not seriously attenuated by the lossy tissue. The gigabit data link has been demonstrated to achieve a high data rate of 6 Gb/s with a bit-error-rate of 10(-12) at a transmission distance of 6 mm, an applicable separation between transmitter and receiver. This high data rate is able to support thousands of recording channels while ensuring a low energy cost per bit of 2.08 pJ/b.

  12. Wireless channel characterization for mm-size neural implants.

    Science.gov (United States)

    Mark, Michael; Bjorninen, Toni; Chen, Yuhui David; Venkatraman, Subramaniam; Ukkonen, Leena; Sydanheimo, Lauri; Carmena, Jose M; Rabaey, Jan M

    2010-01-01

    This paper discusses an approach to modeling and characterizing wireless channel properties for mm-size neural implants. Full-wave electromagnetic simulation was employed to model signal propagation characteristics in biological materials. Animal tests were carried out, proving the validity of the simulation model over a wide range of frequency from 100MHz to 6GHz. Finally, effects of variability and uncertainty in human anatomy and dielectric properties of tissues on these radio links are explored.

  13. Advanced Signal Processing for Wireless Multimedia Communications

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2000-01-01

    Full Text Available There is at present a worldwide effort to develop next-generation wireless communication systems. It is envisioned that many of the future wireless systems will incorporate considerable signal-processing intelligence in order to provide advanced services such as multimedia transmission. In general, wireless channels can be very hostile media through which to communicate, due to substantial physical impediments, primarily radio-frequency interference and time-arying nature of the channel. The need of providing universal wireless access at high data-rate (which is the aim of many merging wireless applications presents a major technical challenge, and meeting this challenge necessitates the development of advanced signal processing techniques for multiple-access communications in non-stationary interference-rich environments. In this paper, we present some key advanced signal processing methodologies that have been developed in recent years for interference suppression in wireless networks. We will focus primarily on the problem of jointly suppressing multiple-access interference (MAI and intersymbol interference (ISI, which are the limiting sources of interference for the high data-rate wireless systems being proposed for many emerging application areas, such as wireless multimedia. We first present a signal subspace approach to blind joint suppression of MAI and ISI. We then discuss a powerful iterative technique for joint interference suppression and decoding, so-called Turbo multiuser detection, that is especially useful for wireless multimedia packet communications. We also discuss space-time processing methods that employ multiple antennas for interference rejection and signal enhancement. Finally, we touch briefly on the problems of suppressing narrowband interference and impulsive ambient noise, two other sources of radio-frequency interference present in wireless multimedia networks.

  14. Ultra low-power integrated circuit design for wireless neural interfaces

    CERN Document Server

    Holleman, Jeremy; Otis, Brian

    2014-01-01

    Presenting results from real prototype systems, this volume provides an overview of ultra low-power integrated circuits and systems for neural signal processing and wireless communication. Topics include analog, radio, and signal processing theory and design for ultra low-power circuits.

  15. Signal Processing for Improved Wireless Receiver Performance

    DEFF Research Database (Denmark)

    Christensen, Lars P.B.

    2007-01-01

    This thesis is concerned with signal processing for improving the performance of wireless communication receivers for well-established cellular networks such as the GSM/EDGE and WCDMA/HSPA systems. The goal of doing so, is to improve the end-user experience and/or provide a higher system capacity...

  16. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates.

    Science.gov (United States)

    Borton, David A; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of

  17. A wireless transmission neural interface system for unconstrained non-human primates

    Science.gov (United States)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  18. Neural Membrane Signaling Platforms

    Directory of Open Access Journals (Sweden)

    Ron Wallace

    2010-06-01

    Full Text Available Throughout much of the history of biology, the cell membrane was functionally defined as a semi-permeable barrier separating aqueous compartments, and an anchoring site for proteins. Little attention was devoted to its possible regulatory role in intracellular molecular processes and neuron electrical signaling. This article reviews the history of membrane studies and the current state of the art. Emphasis is placed on natural and artificial membrane studies of electric field effects on molecular organization, especially as these may relate to impulse propagation in neurons. Implications of these studies for new designs in artificial intelligence are briefly examined.

  19. Neural membrane signaling platforms.

    Science.gov (United States)

    Wallace, Ron

    2010-06-10

    Throughout much of the history of biology, the cell membrane was functionally defined as a semi-permeable barrier separating aqueous compartments, and an anchoring site for proteins. Little attention was devoted to its possible regulatory role in intracellular molecular processes and neuron electrical signaling. This article reviews the history of membrane studies and the current state of the art. Emphasis is placed on natural and artificial membrane studies of electric field effects on molecular organization, especially as these may relate to impulse propagation in neurons. Implications of these studies for new designs in artificial intelligence are briefly examined.

  20. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

    Science.gov (United States)

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile

  1. [Glutamate signaling and neural plasticity].

    Science.gov (United States)

    Watanabe, Masahiko

    2013-07-01

    Proper functioning of the nervous system relies on the precise formation of neural circuits during development. At birth, neurons have redundant synaptic connections not only to their proper targets but also to other neighboring cells. Then, functional neural circuits are formed during early postnatal development by the selective strengthening of necessary synapses and weakening of surplus connections. Synaptic connections are also modified so that projection fields of active afferents expand at the expense of lesser ones. We have studied the molecular mechanisms underlying these activity-dependent prunings and the plasticity of synaptic circuitry using gene-engineered mice defective in the glutamatergic signaling system. NMDA-type glutamate receptors are critically involved in the establishment of the somatosensory pathway ascending from the brainstem trigeminal nucleus to the somatosensory cortex. Without NMDA receptors, whisker-related patterning fails to develop, whereas lesion-induced plasticity occurs normally during the critical period. In contrast, mice lacking the glutamate transporters GLAST or GLT1 are selectively impaired in the lesion-induced critical plasticity of cortical barrels, although whisker-related patterning itself develops normally. In the developing cerebellum, multiple climbing fibers initially innervating given Purkinje cells are eliminated one by one until mono-innervation is achieved. In this pruning process, P/Q-type Ca2+ channels expressed on Purkinje cells are critically involved by the selective strengthening of single main climbing fibers against other lesser afferents. Therefore, the activation of glutamate receptors that leads to an activity-dependent increase in the intracellular Ca2+ concentration plays a key role in the pruning of immature synaptic circuits into functional circuits. On the other hand, glutamate transporters appear to control activity-dependent plasticity among afferent fields, presumably through adjusting

  2. [A telemetery system for neural signal acquiring and processing].

    Science.gov (United States)

    Wang, Min; Song, Yongji; Suen, Jiantao; Zhao, Yiliang; Jia, Aibin; Zhu, Jianping

    2011-02-01

    Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.

  3. High Speed Wireless Signal Generation and Demodulation

    DEFF Research Database (Denmark)

    Caballero Jambrina, Antonio; Sambaraju, Rakesh; Zibar, Darko

    We present the experimental demonstration of high speed wireless generation, up to 40 Gb/s, in the 75-110 GHz wireless band. All-optical OFDM and photonic up-conversion are used for generation and single side-band modulation with digital coherent detection for demodulation.......We present the experimental demonstration of high speed wireless generation, up to 40 Gb/s, in the 75-110 GHz wireless band. All-optical OFDM and photonic up-conversion are used for generation and single side-band modulation with digital coherent detection for demodulation....

  4. Cognitive Control Signals for Neural Prosthetics

    National Research Council Canada - National Science Library

    S. Musallam; B. D. Corneil; B. Greger; H. Scherberger; R. A. Andersen

    2004-01-01

    Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices...

  5. Circuitry for a Wireless Microsystem for Neural Recording Microprobes

    National Research Council Canada - National Science Library

    Yu, Hao

    2001-01-01

    .... Recorded neural signals are amplified, multiplexed, digitized using a 2nd order sigma-delta modulator, and then transmitted to the outside world by an on-chip transmitter, The circuit is designed using a standard...

  6. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal...... understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given......, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data...

  7. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

    Directory of Open Access Journals (Sweden)

    Sun-Il Chang

    2018-01-01

    Full Text Available This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM module. The core integrated circuit (IC consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  8. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems.

    Science.gov (United States)

    Chang, Sun-Il; Park, Sung-Yun; Yoon, Euisik

    2018-01-17

    This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm² and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  9. Studies in RF power communication, SAR, and temperature elevation in wireless implantable neural interfaces.

    Science.gov (United States)

    Zhao, Yujuan; Tang, Lin; Rennaker, Robert; Hutchens, Chris; Ibrahim, Tamer S

    2013-01-01

    Implantable neural interfaces are designed to provide a high spatial and temporal precision control signal implementing high degree of freedom real-time prosthetic systems. The development of a Radio Frequency (RF) wireless neural interface has the potential to expand the number of applications as well as extend the robustness and longevity compared to wired neural interfaces. However, it is well known that RF signal is absorbed by the body and can result in tissue heating. In this work, numerical studies with analytical validations are performed to provide an assessment of power, heating and specific absorption rate (SAR) associated with the wireless RF transmitting within the human head. The receiving antenna on the neural interface is designed with different geometries and modeled at a range of implanted depths within the brain in order to estimate the maximum receiving power without violating SAR and tissue temperature elevation safety regulations. Based on the size of the designed antenna, sets of frequencies between 1 GHz to 4 GHz have been investigated. As expected the simulations demonstrate that longer receiving antennas (dipole) and lower working frequencies result in greater power availability prior to violating SAR regulations. For a 15 mm dipole antenna operating at 1.24 GHz on the surface of the brain, 730 uW of power could be harvested at the Federal Communications Commission (FCC) SAR violation limit. At approximately 5 cm inside the head, this same antenna would receive 190 uW of power prior to violating SAR regulations. Finally, the 3-D bio-heat simulation results show that for all evaluated antennas and frequency combinations we reach FCC SAR limits well before 1 °C. It is clear that powering neural interfaces via RF is possible, but ultra-low power circuit designs combined with advanced simulation will be required to develop a functional antenna that meets all system requirements.

  10. A wireless and batteryless neural headstage with optical stimulation and electrophysiological recording.

    Science.gov (United States)

    Ameli, Reza; Mirbozorgi, Abdollah; Neron, Jean-Luc; Lechasseur, Yoan; Gosselin, Benoit

    2013-01-01

    This paper presents a miniature Optogenetics headstage for wirelessly stimulating the brain of rodents with an implanted LED while recording electrophysiological data from a two-channel custom readout. The headstage is powered wirelessly using an inductive link, and is built using inexpensive commercial off-the-shelf electronic components, including a RF microcontroller and a printed antenna. This device has the capability to drive one light-stimulating LED and, at the same time, capture and send back neural signals recorded from two microelectrode readout channels. Light stimulation uses flexible patterns that allow for easy tuning of light intensity and stimulation periods. For driving the LED, a low-pass filtered digitally-generated PWM signal is employed for providing a flexible pulse generation method that alleviates the need for D/A converters. The proposed device can be powered wirelessly into an animal chamber using inductive energy transfer, which enables compact, light-weight and cost-effective smart animal research systems. The device dimensions are 15×25×17 mm; it weighs 7.4 grams and has a data transmission range of more than 2 meters. Different types of LEDs with different power consumptions can be used for this system. The power consumption of the system without the LED is 94.52 mW.

  11. Hybrid Optical/Wireless Link with Software Defined Receiver for Simultaneous Baseband and Wireless Signal Detection

    DEFF Research Database (Denmark)

    Zibar, Darko; Caballero Jambrina, Antonio; Guerrero Gonzalez, Neil

    2010-01-01

    Simultaneous detection of 5 Gbit/s baseband ASK data and optical phase encoded wireless signal (6 GHz RF carrier with 625 Mbaud 16QAM/QPSK data), on the same optical carrier, is experimentally demonstrated using the same software reconfigurable digital coherent receiver......Simultaneous detection of 5 Gbit/s baseband ASK data and optical phase encoded wireless signal (6 GHz RF carrier with 625 Mbaud 16QAM/QPSK data), on the same optical carrier, is experimentally demonstrated using the same software reconfigurable digital coherent receiver...

  12. Low-cost wireless neural recording system and software.

    Science.gov (United States)

    Gregory, Jeffrey A; Borna, Amir; Roy, Sabyasachi; Wang, Xiaoqin; Lewandowski, Brian; Schmidt, Marc; Najafi, Khalil

    2009-01-01

    We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25microV(rms) in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented.

  13. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  14. Neural networks for error detection and data aggregation in wireless sensor network

    OpenAIRE

    Saeid Bahanfar; Helia Kousha; Ladan Darougaran

    2011-01-01

    Correct information and data aggregation are very important in wireless sensor networks because sending incorrect information by fault sensors make to wrong decision about environment and increasing defective sensor during the time incorrect data decries reliability of wireless sensor networks. Previous methods have Problems such as there are fault sensors in wireless sensor network therefore wrong data are sent to CH by these sensors. In this paper apply the neural network within the sensors...

  15. Ultra low power signal oriented approach for wireless health monitoring.

    Science.gov (United States)

    Marinkovic, Stevan; Popovici, Emanuel

    2012-01-01

    In recent years there is growing pressure on the medical sector to reduce costs while maintaining or even improving the quality of care. A potential solution to this problem is real time and/or remote patient monitoring by using mobile devices. To achieve this, medical sensors with wireless communication, computational and energy harvesting capabilities are networked on, or in, the human body forming what is commonly called a Wireless Body Area Network (WBAN). We present the implementation of a novel Wake Up Receiver (WUR) in the context of standardised wireless protocols, in a signal-oriented WBAN environment and present a novel protocol intended for wireless health monitoring (WhMAC). WhMAC is a TDMA-based protocol with very low power consumption. It utilises WBAN-specific features and a novel ultra low power wake up receiver technology, to achieve flexible and at the same time very low power wireless data transfer of physiological signals. As the main application is in the medical domain, or personal health monitoring, the protocol caters for different types of medical sensors. We define four sensor modes, in which the sensors can transmit data, depending on the sensor type and emergency level. A full power dissipation model is provided for the protocol, with individual hardware and application parameters. Finally, an example application shows the reduction in the power consumption for different data monitoring scenarios.

  16. Wireless sensor networks for monitoring physiological signals of multiple patients.

    Science.gov (United States)

    Dilmaghani, R S; Bobarshad, H; Ghavami, M; Choobkar, S; Wolfe, C

    2011-08-01

    This paper presents the design of a novel wireless sensor network structure to monitor patients with chronic diseases in their own homes through a remote monitoring system of physiological signals. Currently, most of the monitoring systems send patients' data to a hospital with the aid of personal computers (PC) located in the patients' home. Here, we present a new design which eliminates the need for a PC. The proposed remote monitoring system is a wireless sensor network with the nodes of the network installed in the patients' homes. These nodes are then connected to a central node located at a hospital through an Internet connection. The nodes of the proposed wireless sensor network are created by using a combination of ECG sensors, MSP430 microcontrollers, a CC2500 low-power wireless radio, and a network protocol called the SimpliciTI protocol. ECG signals are first sampled by a small portable device which each patient carries. The captured signals are then wirelessly transmitted to an access point located within the patients' home. This connectivity is based on wireless data transmission at 2.4-GHz frequency. The access point is also a small box attached to the Internet through a home asynchronous digital subscriber line router. Afterwards, the data are sent to the hospital via the Internet in real time for analysis and/or storage. The benefits of this remote monitoring are wide ranging: the patients can continue their normal lives, they do not need a PC all of the time, their risk of infection is reduced, costs significantly decrease for the hospital, and clinicians can check data in a short time.

  17. Microcontroller-based wireless recorder for biomedical signals.

    Science.gov (United States)

    Chien, C-N; Hsu, H-W; Jang, J-K; Rau, C-L; Jaw, F-S

    2005-01-01

    A portable multichannel system is described for the recording of biomedical signals wirelessly. Instead of using the conversional time-division analog-modulation method, the technique of digital multiplexing was applied to increase the number of signal channels to 4. Detailed design considerations and functional allocation of the system is discussed. The frontend unit was modularly designed to condition the input signal in an optimal manner. Then, the microcontroller handled the tasks of data conversion, wireless transmission, as well as providing the ability of simple preprocessing such as waveform averaging or rectification. The low-power nature of this microcontroller affords the benefit of battery operation and hence, patient isolation of the system. Finally, a single-chip receiver, which compatible with the RF transmitter of the microcontroller, was used to implement a compact interface with the host computer. An application of this portable recorder for low-back pain studies is shown. This device can simultaneously record one ECG and two surface EMG wirelessly, thus, is helpful in relieving patients' anxiety devising clinical measurement. Such an approach, microcontroller-based wireless measurement, could be an important trend for biomedical instrumentation and we help that this paper could be useful for other colleagues.

  18. Digital signal processing for wireless communication using Matlab

    CERN Document Server

    Gopi, E S

    2016-01-01

    This book examines signal processing techniques used in wireless communication illustrated by using the Matlab program. The author discusses these techniques as they relate to Doppler spread; delay spread; Rayleigh and Rician channel modeling; rake receiver; diversity techniques; MIMO and OFDM -based transmission techniques; and array signal processing. Related topics such as detection theory, link budget, multiple access techniques, and spread spectrum are also covered.   ·         Illustrates signal processing techniques involved in wireless communication using Matlab ·         Discusses multiple access techniques such as Frequency division multiple access, Time division multiple access, and Code division multiple access ·         Covers band pass modulation techniques such as Binary phase shift keying, Differential phase shift keying, Quadrature phase shift keying, Binary frequency shift keying, Minimum shift keying, and Gaussian minimum shift keying.

  19. Distributed Signal Processing for Wireless EEG Sensor Networks.

    Science.gov (United States)

    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

  20. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  1. Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)

    Energy Technology Data Exchange (ETDEWEB)

    Kurt Derr; Milos Manic

    2008-06-01

    Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.

  2. Neural synchronization via potassium signaling

    DEFF Research Database (Denmark)

    Postnov, Dmitry E; Ryazanova, Ludmila S; Mosekilde, Erik

    2006-01-01

    Using a relatively simple model we examine how variations of the extracellular potassium concentration can give rise to synchronization of two nearby pacemaker cells. With the volume of the extracellular space and the rate of potassium diffusion as control parameters, the dual nature...... junctional coupling, potassium signaling gives rise to considerable changes of the cellular response to external stimuli....

  3. Converged wireline and wireless signal transport over optical fibre access links

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Prince, Kamau; Osadchiy, Alexey Vladimirovich

    2009-01-01

    This article reviews emerging trends in converged optical-wireless communication systems and outline the role that photonic technologies are playing in making the vision of a wireline-wireless converged signal transport network a reality.......This article reviews emerging trends in converged optical-wireless communication systems and outline the role that photonic technologies are playing in making the vision of a wireline-wireless converged signal transport network a reality....

  4. Recent developments in wireless recording from the nervous system with ultrasonic neural dust (Conference Presentation)

    Science.gov (United States)

    Maharbiz, Michel M.

    2017-05-01

    The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. We recently demonstrated (Seo et al., arXiV, 2013; Seo et al., Neuron, 2016) "neural dust," a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. There, we showed that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enabled high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. In this talk, I will review recent developments from my group and collaborators in this area.

  5. Wireless Data Acquisition of Transient Signals for Mobile Spectrometry Applications.

    Science.gov (United States)

    Trzcinski, Peter; Weagant, Scott; Karanassios, Vassili

    2016-05-01

    Wireless data acquisition using smartphones or handhelds offers increased mobility, it provides reduced size and weight, it has low electrical power requirements, and (in some cases) it has an ability to access the internet. Thus, it is well suited for mobile spectrometry applications using miniaturized, field-portable spectrometers, or detectors for chemical analysis in the field (i.e., on-site). There are four main wireless communications standards that can be used for wireless data acquisition, namely ZigBee, Bluetooth, Wi-Fi, and UWB (ultra-wide band). These are briefly reviewed and are evaluated for applicability to data acquisition of transient signals (i.e., time-domain) in the field (i.e., on-site) from a miniaturized, field-portable photomultiplier tube detector and from a photodiode array detector installed in a miniaturized, field-portable fiber optic spectrometer. These are two of the most widely used detectors for optical measurements in the ultraviolet-visible range of the spectrum. A miniaturized, 3D-printed, battery-operated microplasma-on-a-chip was used for generation of transient optical emission signals. Elemental analysis from liquid microsamples, a microplasma, and a handheld or a smartphone will be used as examples. Development and potential applicability of wireless data acquisition of transient optical emission signals for taking part of the lab to the sample types of mobile, field-portable spectrometry applications will be discussed. The examples presented are drawn from past and ongoing work in the authors' laboratory. A handheld or a smartphone were used as the mobile computing devices of choice. © The Author(s) 2016.

  6. Reconstruction of periodic signals using neural networks

    Directory of Open Access Journals (Sweden)

    José Danilo Rairán Antolines

    2014-01-01

    Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.

  7. A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

    Directory of Open Access Journals (Sweden)

    Henri Nurminen

    2017-01-01

    Full Text Available Knowledge of deployed transmitters’ (Tx locations in a wireless network improves many aspects of network management. Operators and building administrators are interested in locating unknown Txs for optimizing new Tx placement, detecting and removing unauthorized Txs, selecting the nearest Tx to offload traffic onto it, and constructing radio maps for indoor and outdoor navigation. This survey provides a comprehensive review of existing algorithms that estimate the location of a wireless Tx given a set of observations with the received signal strength indication. Algorithms that require the observations to be location-tagged are suitable for outdoor mapping or small-scale indoor mapping, while algorithms that allow most observations to be unlocated trade off some accuracy to enable large-scale crowdsourcing. This article presents empirical evaluation of the algorithms using numerical simulations and real-world Bluetooth Low Energy data.

  8. Neural basis of multisensory looming signals.

    Science.gov (United States)

    Tyll, Sascha; Bonath, Björn; Schoenfeld, Mircea Ariel; Heinze, Hans-Jochen; Ohl, Frank W; Noesselt, Tömme

    2013-01-15

    Approaching or looming signals are often related to extremely relevant environmental events (e.g. threats or collisions) making these signals critical for survival. However, the neural network underlying multisensory looming processing is not yet fully understood. Using functional magnetic resonance imaging (fMRI) we identified the neural correlates of audiovisual looming processing in humans: audiovisual looming (vs. receding) signals enhance fMRI-responses in low-level visual and auditory areas plus multisensory cortex (superior temporal sulcus; plus parietal and frontal structures). When characterizing the fMRI-response profiles for multisensory looming stimuli, we found significant enhancements relative to the mean and maximum of unisensory responses in looming-sensitive visual and auditory cortex plus STS. Superadditive enhancements were observed in visual cortex. Subject-specific region-of-interest analyses further revealed superadditive response profiles within all sensory-specific looming-sensitive structures plus bilateral STS for audiovisual looming vs. summed unisensory looming conditions. Finally, we observed enhanced connectivity of bilateral STS with low-level visual areas in the context of looming processing. This enhanced coupling of STS with unisensory regions might potentially serve to enhance the salience of unisensory stimulus features and is accompanied by superadditive fMRI-responses. We suggest that this preference in neural signaling for looming stimuli effectively informs animals to avoid potential threats or collisions. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. Security Enhancement of Wireless Sensor Networks Using Signal Intervals.

    Science.gov (United States)

    Moon, Jaegeun; Jung, Im Y; Yoo, Jaesoo

    2017-04-02

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  10. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    Directory of Open Access Journals (Sweden)

    Jaegeun Moon

    2017-04-01

    Full Text Available Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP, the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  11. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    Science.gov (United States)

    Moon, Jaegeun; Jung, Im Y.; Yoo, Jaesoo

    2017-01-01

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users. PMID:28368341

  12. Consensus-based sparse signal reconstruction algorithm for wireless sensor networks

    National Research Council Canada - National Science Library

    Peng, Bao; Zhao, Zhi; Han, Guangjie; Shen, Jian

    2016-01-01

    This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter...

  13. Optical Frequency Upconversion Technique for Transmission of Wireless MIMO-Type Signals over Optical Fiber

    Directory of Open Access Journals (Sweden)

    R. Q. Shaddad

    2014-01-01

    Full Text Available The optical fiber is well adapted to pass multiple wireless signals having different carrier frequencies by using radio-over-fiber (ROF technique. However, multiple wireless signals which have the same carrier frequency cannot propagate over a single optical fiber, such as wireless multi-input multi-output (MIMO signals feeding multiple antennas in the fiber wireless (FiWi system. A novel optical frequency upconversion (OFU technique is proposed to solve this problem. In this paper, the novel OFU approach is used to transmit three wireless MIMO signals over a 20 km standard single mode fiber (SMF. The OFU technique exploits one optical source to produce multiple wavelengths by delivering it to a LiNbO3 external optical modulator. The wireless MIMO signals are then modulated by LiNbO3 optical intensity modulators separately using the generated optical carriers from the OFU process. These modulators use the optical single-sideband with carrier (OSSB+C modulation scheme to optimize the system performance against the fiber dispersion effect. Each wireless MIMO signal is with a 2.4 GHz or 5 GHz carrier frequency, 1 Gb/s data rate, and 16-quadrature amplitude modulation (QAM. The crosstalk between the wireless MIMO signals is highly suppressed, since each wireless MIMO signal is carried on a specific optical wavelength.

  14. Optical frequency upconversion technique for transmission of wireless MIMO-type signals over optical fiber.

    Science.gov (United States)

    Shaddad, R Q; Mohammad, A B; Al-Gailani, S A; Al-Hetar, A M

    2014-01-01

    The optical fiber is well adapted to pass multiple wireless signals having different carrier frequencies by using radio-over-fiber (ROF) technique. However, multiple wireless signals which have the same carrier frequency cannot propagate over a single optical fiber, such as wireless multi-input multi-output (MIMO) signals feeding multiple antennas in the fiber wireless (FiWi) system. A novel optical frequency upconversion (OFU) technique is proposed to solve this problem. In this paper, the novel OFU approach is used to transmit three wireless MIMO signals over a 20 km standard single mode fiber (SMF). The OFU technique exploits one optical source to produce multiple wavelengths by delivering it to a LiNbO3 external optical modulator. The wireless MIMO signals are then modulated by LiNbO3 optical intensity modulators separately using the generated optical carriers from the OFU process. These modulators use the optical single-sideband with carrier (OSSB+C) modulation scheme to optimize the system performance against the fiber dispersion effect. Each wireless MIMO signal is with a 2.4 GHz or 5 GHz carrier frequency, 1 Gb/s data rate, and 16-quadrature amplitude modulation (QAM). The crosstalk between the wireless MIMO signals is highly suppressed, since each wireless MIMO signal is carried on a specific optical wavelength.

  15. Wireless receiver architectures and design antennas, RF, synthesizers, mixed signal, and digital signal processing

    CERN Document Server

    Rouphael, Tony J

    2014-01-01

    Wireless Receiver Architectures and Design presents the various designs and architectures of wireless receivers in the context of modern multi-mode and multi-standard devices. This one-stop reference and guide to designing low-cost low-power multi-mode, multi-standard receivers treats analog and digital signal processing simultaneously, with equal detail given to the chosen architecture and modulating waveform. It provides a complete understanding of the receiver's analog front end and the digital backend, and how each affects the other. The book explains the design process in great detail, s

  16. Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegie gait with machine learning classification by multilayer perceptron neural network.

    Science.gov (United States)

    LeMoyne, Robert; Mastroianni, Timothy

    2016-08-01

    Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.

  17. Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach

    Directory of Open Access Journals (Sweden)

    Matteo Gandetto

    2004-09-01

    Full Text Available The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-defined radio techniques. In particular, a software-defined radio receiver is described aiming at the identification of two coexistent communication modes: frequency hopping code division multiple access and direct sequence code division multiple access. As a case study, two standards, based on the previous modes and operating in the same band (industrial, scientific, and medical, are considered: IEEE WLAN 802.11b (direct sequence and Bluetooth (frequency hopping. Neural classifiers are used to obtain identification results. A comparison between two different neural classifiers is made in terms of relative error frequency.

  18. An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use.

    Science.gov (United States)

    Yin, Ming; Li, Hao; Bull, Christopher; Borton, David A; Aceros, Juan; Larson, Lawrence; Nurmikko, Arto V

    2013-01-01

    In this paper we present a new type of head-mounted wireless neural recording device in a highly compact package, dedicated for untethered laboratory animal research and designed for future mobile human clinical use. The device, which takes its input from an array of intracortical microelectrode arrays (MEA) has ninety-seven broadband parallel neural recording channels and was integrated on to two custom designed printed circuit boards. These house several low power, custom integrated circuits, including a preamplifier ASIC, a controller ASIC, plus two SAR ADCs, a 3-axis accelerometer, a 48MHz clock source, and a Manchester encoder. Another ultralow power RF chip supports an OOK transmitter with the center frequency tunable from 3GHz to 4GHz, mounted on a separate low loss dielectric board together with a 3V LDO, with output fed to a UWB chip antenna. The IC boards were interconnected and packaged in a polyether ether ketone (PEEK) enclosure which is compatible with both animal and human use (e.g. sterilizable). The entire system consumes 17mA from a 1.2Ahr 3.6V Li-SOCl2 1/2AA battery, which operates the device for more than 2 days. The overall system includes a custom RF receiver electronics which are designed to directly interface with any number of commercial (or custom) neural signal processors for multi-channel broadband neural recording. Bench-top measurements and in vivo testing of the device in rhesus macaques are presented to demonstrate the performance of the wireless neural interface.

  19. A low-cost biomedical signal transceiver based on a Bluetooth wireless system.

    Science.gov (United States)

    Fazel-Rezai, Reza; Pauls, Mark; Slawinski, David

    2007-01-01

    Most current wireless biomedical signal transceivers use range-limiting communication. This work presents a low-cost biomedical signal transceiver that uses Bluetooth wireless technology. The design is implemented in a modular form to be adaptable to different types of biomedical signals. The signal front end obtains and processes incoming signals, which are then transmitted via a microcontroller and wireless module. Near real-time receive software in LabVIEW was developed to demonstrate the system capability. The completed transmitter prototype successfully transmits ECG signals, and is able to simultaneously send multiple signals. The sampling rate of the transmitter is fast enough to send up to thirteen ECG signals simultaneously, with an error rate below 0.1% for transmission exceeding 65 meters. A low-cost wireless biomedical transceiver has many applications, such as real-time monitoring of patients with a known condition in non-clinical settings.

  20. Detecting malicious chaotic signals in wireless sensor network

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Kumari, Sangeeta

    2018-02-01

    In this paper, an e-epidemic Susceptible-Infected-Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

  1. Wireless hippocampal neural recording via a multiple input RF receiver to construct place-specific firing fields.

    Science.gov (United States)

    Lee, Seung Bae; Manns, Joseph R; Ghovanloo, Maysam

    2012-01-01

    This paper reports scientifically meaningful in vivo experiments using a 32-channel wireless neural recording system (WINeR). The WINeR system is divided into transmitter (Tx) and receiver (Rx) parts. On the Tx side, we had WINeR-6, a system-on-a-chip (SoC) that operated based on time division multiplexing (TDM) of pulse width modulated (PWM) samples. The chip was fabricated in a 0.5-µm CMOS process, occupying 4.9 × 3.3 mm(2) and consuming 15 mW from ±1.5V supplies. The Rx used two antennas with separate pathways to down-convert the RF signal from a large area. A time-to-digital converter (TDC) in an FPGA converted the PWM pulses into digitized samples. In order to further increase the wireless coverage area and eliminate blind spots within a large experimental arena, two receivers were synchronized. The WINeR system was used to record epileptic activities from a rat that was injected with tetanus toxin (TT) in the dorsal hippocampus. In a different in vivo experiment, place-specific firing fields of place cells, which are parts of the hippocampal-dependent memory, were mapped from a series of behavioral experiments from a rat running in a circular track. Results from the same animal were compared against a commercial hard-wired recording system to evaluate the quality of the wireless recordings.

  2. Active voltammetric microsensors with neural signal processing.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    1998-12-11

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can

  3. A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter.

    Science.gov (United States)

    Chae, Moo Sung; Yang, Zhi; Yuce, Mehmet R; Hoang, Linh; Liu, Wentai

    2009-08-01

    This paper reports a 128-channel neural recording integrated circuit (IC) with on-the-fly spike feature extraction and wireless telemetry. The chip consists of eight 16-channel front-end recording blocks, spike detection and feature extraction digital signal processor (DSP), ultra wideband (UWB) transmitter, and on-chip bias generators. Each recording channel has amplifiers with programmable gain and bandwidth to accommodate different types of biological signals. An analog-to-digital converter (ADC) shared by 16 amplifiers through time-multiplexing results in a balanced trade-off between the power consumption and chip area. A nonlinear energy operator (NEO) based spike detector is implemented for identifying spikes, which are further processed by a digital frequency-shaping filter. The computationally efficient spike detection and feature extraction algorithms attribute to an auspicious DSP implementation on-chip. UWB telemetry is designed to wirelessly transfer raw data from 128 recording channels at a data rate of 90 Mbit/s. The chip is realized in 0.35 mum complementary metal-oxide-semiconductor (CMOS) process with an area of 8.8 x 7.2 mm(2) and consumes 6 mW by employing a sequential turn-on architecture that selectively powers off idle analog circuit blocks. The chip has been tested for electrical specifications and verified in an ex vivo biological environment.

  4. Signal processing approaches to secure physical layer communications in multi-antenna wireless systems

    CERN Document Server

    Hong, Y-W Peter; Kuo, C-C Jay

    2013-01-01

    This book introduces various signal processing approaches to enhance physical layer secrecy in multi-antenna wireless systems. Wireless physical layer secrecy has attracted much attention in recent years due to the broadcast nature of the wireless medium and its inherent vulnerability to eavesdropping. While most articles on physical layer secrecy focus on the information-theoretic aspect, we focus specifically on the signal processing aspects, including beamforming and precoding techniques for data transmission and discriminatory training schemes for channel estimation. The discussions will c

  5. Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics

    Science.gov (United States)

    Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo

    We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

  6. A Wireless Physiological Signal Monitoring System with Integrated Bluetooth and WiFi Technologies.

    Science.gov (United States)

    Yu, Sung-Nien; Cheng, Jen-Chieh

    2005-01-01

    This paper proposes a wireless patient monitoring system which integrates Bluetooth and WiFi wireless technologies. A wireless portable multi-parameter device was designated to acquire physiological signals and transmit them to a local server via Bluetooth wireless technology. Four kinds of monitor units were designed to communicate via the WiFi wireless technology, including a local monitor unit, a control center, mobile devices (personal digital assistant; PDA), and a web page. The use of various monitor units is intending to meet different medical requirements for different medical personnel. This system was demonstrated to promote the mobility and flexibility for both the patients and the medical personnel, which further improves the quality of health care.

  7. Wireless power transfer and data communication for neural implants case study : epilepsy monitoring

    CERN Document Server

    Yilmaz, Gürkan

    2017-01-01

    This book presents new circuits and systems for implantable biomedical applications targeting neural recording. The authors describe a system design adapted to conform to the requirements of an epilepsy monitoring system. Throughout the book, these requirements are reflected in terms of implant size, power consumption, and data rate. In addition to theoretical background which explains the relevant technical challenges, the authors provide practical, step-by-step solutions to these problems. Readers will gain understanding of the numerical values in such a system, enabling projections for feasibility of new projects. Provides complete, system-level perspective for implantable batteryless biomedical system; Extends design example to implementation and long term in-vitro validation; Discusses system design concerns regarding wireless power transmission and wireless data communication, particularly for systems in which both are performed on the same channel/frequency; Presents fully-integrated, implantable syste...

  8. Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer

    Science.gov (United States)

    Yang, Gang; Moghadam, Mohammad R. Vedady; Zhang, Rui

    2017-06-01

    In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) each with one single coil are used to enhance the efficiency of simultaneous power transfer to multiple single-coil receivers (RXs) by constructively combining their induced magnetic fields at the RXs, a technique termed "magnetic beamforming". In this paper, we study the optimal magnetic beamforming design in a multi-user MIMO MRC-WPT system. We introduce the multi-user power region that constitutes all the achievable power tuples for all RXs, subject to the given total power constraint over all TXs as well as their individual peak voltage and current constraints. We characterize each boundary point of the power region by maximizing the sum-power deliverable to all RXs subject to their minimum harvested power constraints. For the special case without the TX peak voltage and current constraints, we derive the optimal TX current allocation for the single-RX setup in closed-form as well as that for the multi-RX setup. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is difficult to solve. For the case of one single RX, we show that the semidefinite relaxation (SDR) of the problem is tight. For the general case with multiple RXs, based on SDR we obtain two approximate solutions by applying time-sharing and randomization, respectively. Moreover, for practical implementation of magnetic beamforming, we propose a novel signal processing method to estimate the magnetic MIMO channel due to the mutual inductances between TXs and RXs. Numerical results show that our proposed magnetic channel estimation and adaptive beamforming schemes are practically effective, and can significantly improve the power transfer efficiency and multi-user performance trade-off in MIMO MRC-WPT systems.

  9. Improved Selectivity From a Wavelength Addressable Device for Wireless Stimulation of Neural Tissue

    Directory of Open Access Journals (Sweden)

    Elif Ç. Seymour

    2014-02-01

    Full Text Available Electrical neural stimulation with micro electrodes is a promising technique for restoring lost functions in the central nervous system as a result of injury or disease. One of the problems related to current neural stimulators is the tissue response due to the connecting wires and the presence of a rigid electrode inside soft neural tissue. We have developed a novel, optically activated, microscale photovoltaic neurostimulator based on a custom layered compound semiconductor heterostructure that is both wireless and has a comparatively small volume. Optical activation provides a wireless means of energy transfer to the neurostimulator, eliminating wires and the associated complications. This neurostimulator was shown to evoke action potentials and a functional motor response in the rat spinal cord. In this work, we extend our design to include wavelength selectivity and thus allowing independent activation of devices. As a proof of concept, we fabricated two different microscale devices with different spectral responsivities in the near-infrared region. We assessed the improved addressability of individual devices via wavelength selectivity as compared to spatial selectivity alone through on-bench optical measurements of the devices in combination with an in vivo light intensity profile in the rat cortex obtained in a previous study. We show that wavelength selectivity improves the individual addressability of the floating stimulators, thus increasing the number of devices that can be implanted in close proximity to each other.

  10. Toward a distributed free-floating wireless implantable neural recording system.

    Science.gov (United States)

    Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

    2016-08-01

    To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

  11. Neural signal registration and analysis of axons grown in microchannels

    Science.gov (United States)

    Pigareva, Y.; Malishev, E.; Gladkov, A.; Kolpakov, V.; Bukatin, A.; Mukhina, I.; Kazantsev, V.; Pimashkin, A.

    2016-08-01

    Registration of neuronal bioelectrical signals remains one of the main physical tools to study fundamental mechanisms of signal processing in the brain. Neurons generate spiking patterns which propagate through complex map of neural network connectivity. Extracellular recording of isolated axons grown in microchannels provides amplification of the signal for detailed study of spike propagation. In this study we used neuronal hippocampal cultures grown in microfluidic devices combined with microelectrode arrays to investigate a changes of electrical activity during neural network development. We found that after 5 days in vitro after culture plating the spiking activity appears first in microchannels and on the next 2-3 days appears on the electrodes of overall neural network. We conclude that such approach provides a convenient method to study neural signal processing and functional structure development on a single cell and network level of the neuronal culture.

  12. Using Artificial Neural Networks for ECG Signals Denoising

    Directory of Open Access Journals (Sweden)

    Zoltán Germán-Salló

    2010-12-01

    Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.

  13. Improvement of wireless transmission system performance for EEG signals based on development of scalar quantization

    Directory of Open Access Journals (Sweden)

    Mohsen Habibnezhad

    2013-12-01

    Full Text Available Advancement of wireless technology leads to some developments in current wireless electroencephalography. Through improving the transmission method of brainwaves, it would be possible to bring more convenience for the patients in need and give this opportunity to others for discovering other aspects of the amazing brainwave. What has been proposed in this study is a new type of adjustable backward quantization method which exploits the nature of the brainwave signal. This method is based on the nature of the captured brainwave and its quantization boundary changes based on the amplitude of each EEG captured signal. The proposed quantization scheme has been analyzed with uniform and Gaussian distribution of quantization level. Consequently, the Backward Gaussian Quantization with Adjustable Boundary and two Word Memories beside the Backward Uniform Quantization with Adjustable Boundary and two Word Memories are introduced by this experiment. In addition, the performance of wireless transmission system and the proposed quantizer’s efficiency for very low frequency (up to 100 Hz and amplitude EEG signal have been noticed. With doing so, we simulated the transmitter and receiver by MATLAB® software. To model the medium, channel was assumed as Additive White Gaussian Noise (AWGN. Meanwhile analysis is done for the whole wireless system performance in terms of transmission range, compared with current available wireless transmission systems on the market. It should be noticed that the transmission range of the proposed wireless transmission system is compared to the transmission range of current wireless EEG systems when there is no obstacle between transmitter and receiver. Furthermore, some relevant parameters to evaluate the quality of the proposed quantization method were examined. To sum up, the proposed quantization schemes show considerable performance in terms of Quantization Rate for constant MSQE and SQNR in comparison with Uniform

  14. Hybrid digital signal processing and neural networks applications in PWRs

    Energy Technology Data Exchange (ETDEWEB)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-12-31

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

  15. A low-cost multichannel wireless neural stimulation system for freely roaming animals

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

    Objectives. Electrical stimulation of nerve tissue and recording of neural activity are the basis of many therapies and neural prostheses. Conventional stimulation systems have a number of practical limitations, especially in experiments involving freely roaming subjects. Our main objective was to develop a modular, versatile and inexpensive multichannel wireless system able to overcome some of these constraints. Approach. We have designed and implemented a new multichannel wireless neural stimulator based on commercial components. The system is small (2 cm × 4 cm × 0.5 cm) and light in weight (9 g) which allows it to be easily carried in a small backpack. To test and validate the performance and reliability of the whole system we conducted several bench tests and in vivo experiments. Main results. The performance and accuracy of the stimulator were comparable to commercial threaded systems. Stimulation sequences can be constructed on-the-fly with 251 selectable current levels (from 0 to 250 µA) with 1 µA step resolution. The pulse widths and intervals can be as long as 65 ms in 2 µs time resolution. The system covers approximately 10 m of transmission range in a regular laboratory environment and 100 m in free space (line of sight). Furthermore it provides great flexibility for experiments since it allows full control of the stimulator and the stimulation parameters in real time. When there is no stimulation, the device automatically goes into low-power sleep mode to preserve battery power. Significance. We introduce the design of a powerful multichannel wireless stimulator assembled from commercial components. Key features of the system are their reliability, robustness and small size. The system has a flexible design that can be modified straightforwardly to tailor it to any specific experimental need. Furthermore it can be effortlessly adapted for use with any kind of multielectrode arrays.

  16. Fabrication and Microassembly of a mm-Sized Floating Probe for a Distributed Wireless Neural Interface

    Directory of Open Access Journals (Sweden)

    Pyungwoo Yeon

    2016-09-01

    Full Text Available A new class of wireless neural interfaces is under development in the form of tens to hundreds of mm-sized untethered implants, distributed across the target brain region(s. Unlike traditional interfaces that are tethered to a centralized control unit and suffer from micromotions that may damage the surrounding neural tissue, the new free-floating wireless implantable neural recording (FF-WINeR probes will be stand-alone, directly communicating with an external interrogator. Towards development of the FF-WINeR, in this paper we describe the micromachining, microassembly, and hermetic packaging of 1-mm3 passive probes, each of which consists of a thinned micromachined silicon die with a centered Ø(diameter 130 μm through-hole, an Ø81 μm sharpened tungsten electrode, a 7-turn gold wire-wound coil wrapped around the die, two 0201 surface mount capacitors on the die, and parylene-C/Polydimethylsiloxane (PDMS coating. The fabricated passive probe is tested under a 3-coil inductive link to evaluate power transfer efficiency (PTE and power delivered to a load (PDL for feasibility assessment. The minimum PTE/PDL at 137 MHz were 0.76%/240 μW and 0.6%/191 μW in the air and lamb head medium, respectively, with coil separation of 2.8 cm and 9 kΩ receiver (Rx loading. Six hermetically sealed probes went through wireless hermeticity testing, using a 2-coil inductive link under accelerated lifetime testing condition of 85 °C, 1 atm, and 100%RH. The mean-time-to-failure (MTTF of the probes at 37 °C is extrapolated to be 28.7 years, which is over their lifetime.

  17. The Design of Wireless Data Acquisition and Remote Transmission Interface in Micro-seismic Signals

    Directory of Open Access Journals (Sweden)

    Huan-Huan BIAN

    2014-02-01

    Full Text Available The micro-seismic signal acquisition and transmission is an important key part in geological prospecting. This paper describes a bran-new solution of micro-seismic signal acquisition and remote transmission using Zigbee technique and wireless data transmission technique. The hardware such as front-end data acquisition interface made up by Zigbee wireless networking technique, remote data transmission solution composed of general packet radio service (or GPRS for short technique and interface between Zigbee and GPRS is designed in detail. Meanwhile the corresponding software of the system is given out. The solution solves the numerous practical problems nagged by complex and terrible environment faced using micro-seismic prospecting. The experimental results demonstrate that the method using Zigbee wireless network communication technique GPRS wireless packet switching technique is efficient, reliable and flexible.

  18. Towards a magnetoresistive platform for neural signal recording

    Science.gov (United States)

    Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.

    2017-05-01

    A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.

  19. A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants.

    Science.gov (United States)

    Yang, Yuning; Kamboh, Awais M; Mason, Andrew J

    2014-04-30

    This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Neural crest specification: tissues, signals, and transcription factors.

    Science.gov (United States)

    Rogers, C D; Jayasena, C S; Nie, S; Bronner, M E

    2012-01-01

    The neural crest is a transient population of multipotent and migratory cells unique to vertebrate embryos. Initially derived from the borders of the neural plate, these cells undergo an epithelial to mesenchymal transition to leave the central nervous system, migrate extensively in the periphery, and differentiate into numerous diverse derivatives. These include but are not limited to craniofacial cartilage, pigment cells, and peripheral neurons and glia. Attractive for their similarities to stem cells and metastatic cancer cells, neural crest cells are a popular model system for studying cell/tissue interactions and signaling factors that influence cell fate decisions and lineage transitions. In this review, we discuss the mechanisms required for neural crest formation in various vertebrate species, focusing on the importance of signaling factors from adjacent tissues and conserved gene regulatory interactions, which are required for induction and specification of the ectodermal tissue that will become neural crest. Copyright © 2011 Wiley Periodicals, Inc.

  1. Towards convergence of wireless and wireline signal transport in broadband access networks

    DEFF Research Database (Denmark)

    Yu, Xianbin; Prince, Kamau; Tafur Monroy, Idelfonso

    2010-01-01

    Hybrid optical wireless access networks are to play an important role in the realization of the vision of delivery of broadband services to the end-user any time, anywhere and at affordable costs. We present results of experiments conducted over a field deployed optical fibre links we successfully...... demonstrated converged wireless and wireline signal transport over a common fibre infrastructure. The type of signal used in this field deployed experiments cover WiMax, Impulse-radio ultra-wideband (UWB) and coherent transmission of baseband QPSK and radio-over-fibre signals....

  2. A Multi-Channel Low-Power System-on-Chip for in Vivo Recording and Wireless Transmission of Neural Spikes

    Directory of Open Access Journals (Sweden)

    Alessandro Sottocornola Spinelli

    2012-09-01

    Full Text Available This paper reports a multi-channel neural spike recording system-on-chip with digital data compression and wireless telemetry. The circuit consists of 16 amplifiers, an analog time-division multiplexer, a single 8 bit analog-to-digital converter, a digital signal compression unit and a wireless transmitter. Although only 16 amplifiers are integrated in our current die version, the whole system is designed to work with 64, demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. Compression of the raw data is achieved by detecting the action potentials (APs and storing 20 samples for each spike waveform. This compression method retains sufficiently high data quality to allow for single neuron identification (spike sorting. The 400 MHz transmitter employs a Manchester-Coded Frequency Shift Keying (MC-FSK modulator with low modulation index. In this way, a 1:25 Mbit/s data rate is delivered within a limited band of about 3 MHz. The chip is realized in a 0:35 m AMS CMOS process featuring a 3 V power supply with an area of 3:1 2:7 mm2. The achieved transmission range is over 10 m with an overall power consumption for 64 channels of 17:2 mW. This figure translates into a power budget of 269 W per channel, in line with published results but allowing a larger transmission distance and more efficient bandwidth occupation of the wireless link. The integrated circuit was mounted on a small and light board to be used during neuroscience experiments with freely-behaving rats. Powered by 2 AAA batteries, the system can continuously work for more than 100 hours allowing for long-lasting neural spike recordings.

  3. A Wireless Real-Time Monitoring Node of the Physiological Signals for Unrestrained Dairy Cattle Using Wireless Sensor Network

    Science.gov (United States)

    Zhang, Xihai; Zhang, Changli; Fang, Junlong; Fan, Yongcun

    A newly developed smart sensor node that can monitor physiological signals for unrestrained dairy cattle is designed through modular design and its advantages are compact structure and small volume. This sensor node is based on a MSP430F133 micro-controller; the digital sensor includes temperature sensor (DS18B20-America) and vibration-displacement sensor (DN series China); transmission of the digital data uses the nRF903. The results show that this node can collect physiological signals for unrestrained dairy cattle and then send it to upper network node. This research can provide better hardware platform for further researching the communication protocols of wireless sensor networks.

  4. Characterization and Modeling of Received Signal Strength and Charging Time for Wireless Energy Transfer

    Directory of Open Access Journals (Sweden)

    Uthman Baroudi

    2015-01-01

    Full Text Available Wireless sensor networks can provide effective means for monitoring and controlling a wide range of applications. Recently, tremendous effort was directed towards devising sensors powered from ambient sources such as heat, wind, and vibration. Wireless energy transfer is another source that has attractive features that make it a promising candidate for supplying power to wireless sensor nodes. This paper is concerned with characterizing and modeling the charging time and received signal strength indicator for wireless energy transfer system. These parameters play a vital role in deciding the geometry of sensor network and the routing protocols to be deployed. The development of communication protocols for wireless-powered wireless sensor networks is also improved with the knowledge of such models. These two quantities were computed from data acquired at various coordinates of the harvester relative to a fixed position of RF energy source. Data was acquired for indoor and outdoor scenarios using the commercially available PowerCast energy harvester and evaluation board. Mathematical models for both indoor and outdoor environments were developed and analyzed. A few guidelines on how to use these models were suggested. Finally, the possibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes was also investigated.

  5. Small-signal neural models and their applications.

    Science.gov (United States)

    Basu, Arindam

    2012-02-01

    This paper introduces the use of the concept of small-signal analysis, commonly used in circuit design, for understanding neural models. We show that neural models, varying in complexity from Hodgkin-Huxley to integrate and fire have similar small-signal models when their corresponding differential equations are close to the same bifurcation with respect to input current. Three applications of small-signal neural models are shown. First, some of the properties of cortical neurons described by Izhikevich are explained intuitively through small-signal analysis. Second, we use small-signal models for deriving parameters for a simple neural model (such as resonate and fire) from a more complicated but biophysically relevant one like Morris-Lecar. We show similarity in the subthreshold behavior of the simple and complicated model when they are close to a Hopf bifurcation and a saddle-node bifurcation. Hence, this is useful to correctly tune simple neural models for large-scale cortical simulations. Finaly, the biasing regime of a silicon ion channel is derived by comparing its small-signal model with a Hodgkin-Huxley-type model.

  6. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    Directory of Open Access Journals (Sweden)

    Nenad Kojić

    2012-06-01

    Full Text Available The networking infrastructure of wireless mesh networks (WMNs is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs. This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission. The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

  7. A neural networks-based hybrid routing protocol for wireless mesh networks.

    Science.gov (United States)

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

  8. Wireless photoplethysmographic device for heart rate variability signal acquisition and analysis.

    Science.gov (United States)

    Reyes, Ivan; Nazeran, Homer; Franco, Mario; Haltiwanger, Emily

    2012-01-01

    The photoplethysmographic (PPG) signal has the potential to aid in the acquisition and analysis of heart rate variability (HRV) signal: a non-invasive quantitative marker of the autonomic nervous system that could be used to assess cardiac health and other physiologic conditions. A low-power wireless PPG device was custom-developed to monitor, acquire and analyze the arterial pulse in the finger. The system consisted of an optical sensor to detect arterial pulse as variations in reflected light intensity, signal conditioning circuitry to process the reflected light signal, a microcontroller to control PPG signal acquisition, digitization and wireless transmission, a receiver to collect the transmitted digital data and convert them back to their analog representations. A personal computer was used to further process the captured PPG signals and display them. A MATLAB program was then developed to capture the PPG data, detect the RR peaks, perform spectral analysis of the PPG data, and extract the HRV signal. A user-friendly graphical user interface (GUI) was developed in LabView to display the PPG data and their spectra. The performance of each module (sensing unit, signal conditioning, wireless transmission/reception units, and graphical user interface) was assessed individually and the device was then tested as a whole. Consequently, PPG data were obtained from five healthy individuals to test the utility of the wireless system. The device was able to reliably acquire the PPG signals from the volunteers. To validate the accuracy of the MATLAB codes, RR peak information from each subject was fed into Kubios software as a text file. Kubios was able to generate a report sheet with the time domain and frequency domain parameters of the acquired data. These features were then compared against those calculated by MATLAB. The preliminary results demonstrate that the prototype wireless device could be used to perform HRV signal acquisition and analysis.

  9. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    . The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....... and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right...

  10. Fully Photonic Wireless Link for Transmission of Synchronization Signals

    Directory of Open Access Journals (Sweden)

    O. Wilfert

    2016-04-01

    Full Text Available Rapid industrialization and increasing demand of business tools for high-speed communications supports the request for optical communications in free space. Copper cables and related technologies such as cable modems and Digital Subscriber Line (DSL are common in existing networks, but do not meet the bandwidth requirement in the future, which opens the door to optical wireless communication technologies. Research in links for optical wireless communication (Infra Red Line of Sight, IR LOS working in the atmosphere is due to the wide support of its development on the world market. Optical wireless communications research is currently focused on increasing the transmission quality of data links. A promising new trend in data connection through IR LOS includes the transfer of accurate time synchronization pulses (time transmission. The article presents problems of modeling and design of a transmitter and receiver with a fully photonic concept. The analysis of the power levels at the link and drawn a model for determining the connection losses at the receiver caused by optical coupling between a Schmidt-Cassegrain telescope and the receiving optical fiber is shown.

  11. Simultaneous transmission of wired and wireless signals based on double sideband carrier suppression

    Science.gov (United States)

    Bitew, Mekuanint Agegnehu; Shiu, Run-Kai; Peng, Peng-Chun; Wang, Cheng-Hao; Chen, Yan-Ming

    2017-11-01

    In this paper, we proposed and experimentally demonstrated simultaneous transmission of wired and wireless signals based on double sideband optical carrier suppression. By properly adjusting the bias point of the dual-output mach-zehnder modulator (MZM), a central carrier in one output port and a pair of first-order sidebands in another output port are generated. The pair of first-order sidebands are fed into a second MZM to generate second-order order sidebands. A wired signal is embedded on the central carrier while a wireless signal is embedded on the second-order sidebands. Unlike other schemes, we did not use optical filter to separate the carrier from the optical sidebands. The measured bit error rate (BER) and eye-diagrams after a 25 km single-mode-fiber (SMF) transmission proved that the proposed scheme is successful for both wired and wireless signals transmission. Moreover, the power penalty at the BER of 10-9 is 0.3 and 0.7 dB for wired and wireless signals, respectively.

  12. Automatic Speech Recognition from Neural Signals: A Focused Review.

    Science.gov (United States)

    Herff, Christian; Schultz, Tanja

    2016-01-01

    Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.

  13. Automatic Speech Recognition from Neural Signals: A Focused Review

    Directory of Open Access Journals (Sweden)

    Christian Herff

    2016-09-01

    Full Text Available Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e.~patients suffering from locked-in syndrome. For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people.This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography. As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the emph{Brain-to-text} system.

  14. Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines

    Directory of Open Access Journals (Sweden)

    Hubert Roth

    2008-11-01

    Full Text Available The specialized hairs and slit sensillae of spiders (Cupiennius salei can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.

  15. Broadband Microwave Wireless Power Transfer for Weak-Signal and Multipath Environments

    Science.gov (United States)

    Barton, Richard J.

    2014-01-01

    In this paper, we study the potential benefits of using relatively broadband wireless power transmission WPT strategies in both weak-signal and multipath environments where traditional narrowband strategies can be very inefficient. The paper is primarily a theoretical and analytical treatment of the problem that attempts to derive results that are widely applicable to many different WPT applications, including space solar power SSP.

  16. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  17. Wireless Body Area Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting

    OpenAIRE

    Joonyoung Jung; Kiryong Ha; Jeonwoo Lee; Youngsung Kim; Daeyoung Kim

    2008-01-01

    We developed a ubiquitous healthcare system consisted of aphysiological signal devices, a mobile system, a device provider system, a healthcare service provider system, a physician system, and a healthcare personal system. In this system, wireless body area network (WBAN) such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We propose a scanning algorithm, dynamic discover...

  18. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

  19. Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

    Science.gov (United States)

    Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

    2017-01-01

    Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

  20. A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.

    Science.gov (United States)

    Yang, Yuning; Boling, Sam; Mason, Andrew J

    2017-08-01

    Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm2 area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm2 electrode array.

  1. Estimating Neural Signal Dynamics in the Human Brain

    Directory of Open Access Journals (Sweden)

    Christopher W Tyler

    2011-06-01

    Full Text Available Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course. Knowledge of the neural signal is critical information if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by noninvasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential application from general cognitive studies to assessment of neuropathologies.

  2. Use of artificial neural networks in biosensor signal classification

    Directory of Open Access Journals (Sweden)

    Vlastimil Dohnal

    2008-01-01

    Full Text Available Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.

  3. Music Signal Processing Using Vector Product Neural Networks

    Science.gov (United States)

    Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

    2017-05-01

    We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

  4. A digital wireless system for closed-loop inhibition of nociceptive signals

    Science.gov (United States)

    Zuo, Chao; Yang, Xiaofei; Wang, Yang; Hagains, Christopher E.; Li, Ai-Ling; Peng, Yuan B.; Chiao, J.-C.

    2012-10-01

    Neurostimulation of the spinal cord or brain has been used to inhibit nociceptive signals in pain management applications. Nevertheless, most of the current neurostimulation models are based on open-loop system designs. There is a lack of closed-loop systems for neurostimulation in research with small freely-moving animals and in future clinical applications. Based on our previously developed analog wireless system for closed-loop neurostimulation, a digital wireless system with real-time feedback between recorder and stimulator modules has been developed to achieve multi-channel communication. The wireless system includes a wearable recording module, a wearable stimulation module and a transceiver connected to a computer for real-time and off-line data processing, display and storage. To validate our system, wide dynamic range neurons in the spinal cord dorsal horn have been recorded from anesthetized rats in response to graded mechanical stimuli (brush, pressure and pinch) applied in the hind paw. The identified nociceptive signals were used to automatically trigger electrical stimulation at the periaqueductal gray in real time to inhibit their own activities by the closed-loop design. Our digital wireless closed-loop system has provided a simplified and efficient method for further study of pain processing in freely-moving animals and potential clinical application in patients. Groups 1, 2 and 3 contributed equally to this project.

  5. Biomedical signal acquisition with streaming wireless communication for recording evoked potentials.

    Science.gov (United States)

    Thie, Johnson; Klistorner, Alexander; Graham, Stuart L

    2012-10-01

    Commercial electrophysiology systems for recording evoked potentials always connect patients to the acquisition unit via long wires. Wires guarantee timely transfer of signals for synchronization with the stimuli, but they are susceptible to electromagnetic and electrostatic interferences. Though wireless solutions are readily available (e.g. Bluetooth), they introduce high delay variability that will distort the evoked potential traces. We developed a complete wireless acquisition system with a fixed delay. The system supports up to 4 bipolar channels; each is amplified by 20,000× and digitized to 24 bits. The system incorporates the "driven-right-leg" circuit to lower the common noise. Data are continuously streamed using radio-frequency transmission operating at 915 MHz and then tagged with the stimulus SYNC signal at the receiver. The delay, noise level and transmission error rate were measured. Flash visual evoked potentials were recorded monocularly from both eyes of six adults with normal vision. The signals were acquired via wireless and wired transmissions simultaneously. The recording was repeated on some participants within 2 weeks. The delay was constant at 20 ms. The system noise was white and Gaussian (2 microvolts RMS). The transmission error rate was about one per million packets. The VEPs recorded with wireless transmission were consistent with those with wired transmission. The VEP amplitudes and shapes showed good intra-session and inter-session reproducibility and were consistent across eyes. The wireless acquisition system can reliably record visual evoked potentials. It has a constant delay of 20 ms and very low error rate.

  6. Reconfigurable Signal Processing and Hardware Architecture for Broadband Wireless Communications

    Directory of Open Access Journals (Sweden)

    Liang Ying-Chang

    2005-01-01

    Full Text Available This paper proposes a broadband wireless transceiver which can be reconfigured to any type of cyclic-prefix (CP -based communication systems, including orthogonal frequency-division multiplexing (OFDM, single-carrier cyclic-prefix (SCCP system, multicarrier (MC code-division multiple access (MC-CDMA, MC direct-sequence CDMA (MC-DS-CDMA, CP-based CDMA (CP-CDMA, and CP-based direct-sequence CDMA (CP-DS-CDMA. A hardware platform is proposed and the reusable common blocks in such a transceiver are identified. The emphasis is on the equalizer design for mobile receivers. It is found that after block despreading operation, MC-DS-CDMA and CP-DS-CDMA have the same equalization blocks as OFDM and SCCP systems, respectively, therefore hardware and software sharing is possible for these systems. An attempt has also been made to map the functional reconfigurable transceiver onto the proposed hardware platform. The different functional entities which will be required to perform the reconfiguration and realize the transceiver are explained.

  7. Neural systems for choice and valuation with counterfactual learning signals.

    Science.gov (United States)

    Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T

    2014-04-01

    The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Determination of outdoor signal propagation via visibility analysis in outdoor wireless networks

    Directory of Open Access Journals (Sweden)

    Mustafa Coşar

    2017-02-01

    Full Text Available Wireless networks on university campuses has gained importance in recent years. These networks in major areas such as university campuses, are faced with many problems during the planning, design and establishment. These problems are among the first that comes to mind, the physical properties of the campus and is selected according to the characteristics of network equipment. There is no doubt at all points of a wireless network set up in order to provide uninterrupted service and quality of the signal is expected to be good. However, it should be understood literally cannot meet these expectations. Therefore, to solve many problems to campus planning and design can be made to have acceptable signal distribution will have the appropriate use of and satisfaction with increasing effect. In this study, due to the start of construction on the North Campus of Hitit University, wireless signal spread using the current spread has been determined with the help of geographic information systems visibility analysis. An area of 56 hectares, with the total of 9 AP the acceptable signal distribution was obtained.

  9. Self-powered wireless carbohydrate/oxygen sensitive biodevice based on radio signal transmission.

    Science.gov (United States)

    Falk, Magnus; Alcalde, Miguel; Bartlett, Philip N; De Lacey, Antonio L; Gorton, Lo; Gutierrez-Sanchez, Cristina; Haddad, Raoudha; Kilburn, Jeremy; Leech, Dónal; Ludwig, Roland; Magner, Edmond; Mate, Diana M; Conghaile, Peter Ó; Ortiz, Roberto; Pita, Marcos; Pöller, Sascha; Ruzgas, Tautgirdas; Salaj-Kosla, Urszula; Schuhmann, Wolfgang; Sebelius, Fredrik; Shao, Minling; Stoica, Leonard; Sygmund, Cristoph; Tilly, Jonas; Toscano, Miguel D; Vivekananthan, Jeevanthi; Wright, Emma; Shleev, Sergey

    2014-01-01

    Here for the first time, we detail self-contained (wireless and self-powered) biodevices with wireless signal transmission. Specifically, we demonstrate the operation of self-sustained carbohydrate and oxygen sensitive biodevices, consisting of a wireless electronic unit, radio transmitter and separate sensing bioelectrodes, supplied with electrical energy from a combined multi-enzyme fuel cell generating sufficient current at required voltage to power the electronics. A carbohydrate/oxygen enzymatic fuel cell was assembled by comparing the performance of a range of different bioelectrodes followed by selection of the most suitable, stable combination. Carbohydrates (viz. lactose for the demonstration) and oxygen were also chosen as bioanalytes, being important biomarkers, to demonstrate the operation of the self-contained biosensing device, employing enzyme-modified bioelectrodes to enable the actual sensing. A wireless electronic unit, consisting of a micropotentiostat, an energy harvesting module (voltage amplifier together with a capacitor), and a radio microchip, were designed to enable the biofuel cell to be used as a power supply for managing the sensing devices and for wireless data transmission. The electronic system used required current and voltages greater than 44 µA and 0.57 V, respectively to operate; which the biofuel cell was capable of providing, when placed in a carbohydrate and oxygen containing buffer. In addition, a USB based receiver and computer software were employed for proof-of concept tests of the developed biodevices. Operation of bench-top prototypes was demonstrated in buffers containing different concentrations of the analytes, showcasing that the variation in response of both carbohydrate and oxygen biosensors could be monitored wirelessly in real-time as analyte concentrations in buffers were changed, using only an enzymatic fuel cell as a power supply.

  10. Converged wireline and wireless signal distribution in optical fiber access networks

    DEFF Research Database (Denmark)

    Prince, Kamau

    This thesis presents results obtained during the course of my doctoral studies into the transport of fixed and wireless signaling over a converged otpical access infrastructure. In the formulation, development and assessment of a converged paradigma for multiple-services delivery via optical access...... networking infrastructure, I have demonstrated increased functionalities with existing optical technologies and commercially available optoelectronic devices. I have developed novel systems for extending the range of optical access systems, and have demonstrated the repurposing of standard digital devices...

  11. Stochastic resonance with colored noise for neural signal detection.

    Science.gov (United States)

    Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

    2014-01-01

    We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity-a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.

  12. Fate Specification of Neural Plate Border by Canonical Wnt Signaling and Grhl3 is Crucial for Neural Tube Closure.

    Science.gov (United States)

    Kimura-Yoshida, Chiharu; Mochida, Kyoko; Ellwanger, Kristina; Niehrs, Christof; Matsuo, Isao

    2015-06-01

    During primary neurulation, the separation of a single-layered ectodermal sheet into the surface ectoderm (SE) and neural tube specifies SE and neural ectoderm (NE) cell fates. The mechanisms underlying fate specification in conjunction with neural tube closure are poorly understood. Here, by comparing expression profiles between SE and NE lineages, we observed that uncommitted progenitor cells, expressing stem cell markers, are present in the neural plate border/neural fold prior to neural tube closure. Our results also demonstrated that canonical Wnt and its antagonists, DKK1/KREMEN1, progressively specify these progenitors into SE or NE fates in accord with the progress of neural tube closure. Additionally, SE specification of the neural plate border via canonical Wnt signaling is directed by the grainyhead-like 3 (Grhl3) transcription factor. Thus, we propose that the fate specification of uncommitted progenitors in the neural plate border by canonical Wnt signaling and its downstream effector Grhl3 is crucial for neural tube closure. This study implicates that failure in critical genetic factors controlling fate specification of progenitor cells in the neural plate border/neural fold coordinated with neural tube closure may be potential causes of human neural tube defects.

  13. A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yongwei Zhang

    2017-01-01

    Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.

  14. An Integrated Signaling-Encryption Mechanism to Reduce Error Propagation in Wireless Communications: Performance Analyses

    Energy Technology Data Exchange (ETDEWEB)

    Olama, Mohammed M [ORNL; Matalgah, Mustafa M [ORNL; Bobrek, Miljko [ORNL

    2015-01-01

    Traditional encryption techniques require packet overhead, produce processing time delay, and suffer from severe quality of service deterioration due to fades and interference in wireless channels. These issues reduce the effective transmission data rate (throughput) considerably in wireless communications, where data rate with limited bandwidth is the main constraint. In this paper, performance evaluation analyses are conducted for an integrated signaling-encryption mechanism that is secure and enables improved throughput and probability of bit-error in wireless channels. This mechanism eliminates the drawbacks stated herein by encrypting only a small portion of an entire transmitted frame, while the rest is not subject to traditional encryption but goes through a signaling process (designed transformation) with the plaintext of the portion selected for encryption. We also propose to incorporate error correction coding solely on the small encrypted portion of the data to drastically improve the overall bit-error rate performance while not noticeably increasing the required bit-rate. We focus on validating the signaling-encryption mechanism utilizing Hamming and convolutional error correction coding by conducting an end-to-end system-level simulation-based study. The average probability of bit-error and throughput of the encryption mechanism are evaluated over standard Gaussian and Rayleigh fading-type channels and compared to the ones of the conventional advanced encryption standard (AES).

  15. A wireless capsule system with ASIC for monitoring the physiological signals of the human gastrointestinal tract.

    Science.gov (United States)

    Xu, Fei; Yan, Guozheng; Zhao, Kai; Lu, Li; Gao, Jinyang; Liu, Gang

    2014-12-01

    This paper presents the design of a wireless capsule system for monitoring the physiological signals of the human gastrointestinal (GI) tract. The primary components of the system include a wireless capsule, a portable data recorder, and a workstation. Temperature, pH, and pressure sensors; an RF transceiver; a controlling and processing application specific integrated circuit (ASIC); and batteries were applied in a wireless capsule. Decreasing capsule size, improving sensor precision, and reducing power needs were the primary challenges; these were resolved by employing micro sensors, optimized architecture, and an ASIC design that include power management, clock management, a programmable gain amplifier (PGA), an A/D converter (ADC), and a serial peripheral interface (SPI) communication unit. The ASIC has been fabricated in 0.18- μm CMOS technology with a die area of 5.0 mm × 5.0 mm. The wireless capsule integrating the ASIC controller measures Φ 11 mm × 26 mm. A data recorder and a workstation were developed, and 20 cases of human experiments were conducted in hospitals. Preprocessing in the workstation can significantly improve the quality of the data, and 76 original features were determined by mathematical statistics. Based on the 13 optimal features achieved in the evaluation of the features, the clustering algorithm can identify the patients who lack GI motility with a recognition rate reaching 83.3%.

  16. An artificial neural network architecture for non-parametric visual odometry in wireless capsule endoscopy

    Science.gov (United States)

    Dimas, George; Iakovidis, Dimitris K.; Karargyris, Alexandros; Ciuti, Gastone; Koulaouzidis, Anastasios

    2017-09-01

    Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup.

  17. A wireless recording system that utilizes Bluetooth technology to transmit neural activity in freely moving animals.

    Science.gov (United States)

    Hampson, Robert E; Collins, Vernell; Deadwyler, Sam A

    2009-09-15

    A new wireless transceiver is described for recording individual neuron firing from behaving rats utilizing Bluetooth transmission technology and a processor onboard for discrimination of neuronal waveforms and associated time stamps. This universal brain activity transmitter (UBAT) is attached to rodents via a backpack and amplifier headstage and can transmit 16 channels of captured neuronal firing data via a Bluetooth transceiver chip over very large and unconstrained distances. The onboard microprocessor of the UBAT allows flexible online control over waveform isolation criteria via transceiver instruction and the two-way communication capacity allows for closed-loop applications between neural events and behavioral or physiological processes which can be modified by transceiver instructions. A detailed description of the multiplexer processing of channel data as well as examples of neuronal recordings in different behavioral testing contexts is provided to demonstrate the capacity for robust transmission within almost any laboratory environment. A major advantage of the UBAT is the long transmission range and lack of object-based line of sight interference afforded by Bluetooth technology, allowing flexible recording capabilities within multiple experimental paradigms without interruption. Continuous recordings over very large distance separations from the monitor station are demonstrated providing experimenters with recording advantages not previously available with other telemetry devices.

  18. Quality of the wireless electrocardiogram signal during physical exercise in different age groups.

    Science.gov (United States)

    Takalokastari, Tiina; Alasaarela, Esko; Kinnunen, Matti; Jämsä, Timo

    2014-05-01

    Electrocardiographic (ECG) recordings are usually obtained at rest. In many cases, real-time ECG monitoring in the home environment during daily life would be useful, but that requires a wireless device. The purpose of this paper is to evaluate the quality of the wireless ECG signals during physical activities. The test data were collected both in a normal exercise environment and in a radio frequency (RF)-shielded and noiseless environment. 30 test persons performed running, biking, or Nordic walking exercises in normal indoor conditions, while electrical activity of the heart and acceleration of the body were measured by a VitalSens VS100 device (InteleSens). The acceleration data were also acquired with a DogIMU movement sensor (Domuset). Six more persons were measured in an RF-shielded environment, while they followed a specific list of exercises to verify the tests of the first group. The list consisted of exercise movements, thought to introduce disturbance in the ECG signals. The collected data were classified into three quality classes, good (3%), moderate (66%), and poor (31%), based on the recognition of the QRS-complex and R-R intervals as well as the amount of disturbance. The accelerometer data were compared to the amount of noise in the ECG data. A clear correlation was found between increased noise and level of activity. Increasing age also appeared to decrease the ECG signal quality. Careful consideration of the quality of the data versus positive and negative features of wirelessness shows great potential for the wireless ECG in future home healthcare and fitness industries.

  19. QoS signaling across heterogeneous wired/wireless networks: resource management in diffserv using the NSIS protocol suite

    NARCIS (Netherlands)

    Bader, Attila; Karagiannis, Georgios; Westberg, Lars; Kappler, Cornelia; Phelan, Tom; Tschofenig, Hannes; Heijenk, Geert; Shen, S.

    2005-01-01

    Reservation-based Quality of Service (QoS) in a mixed wireless and wireline environment requires an end-to-end signaling protocol that is capable of adapting to the idiosyncrasies of the different networks. The QoS NSIS Signaling Protocol (QoSNSLP) has been created by the Next Steps In Signaling

  20. Wireless Network of Collaborative Physiological Signal Devices in a U-Healthcare System

    Science.gov (United States)

    Jung, Joonyoung; Kim, Daeyoung

    We designed and implemented collaborative physiological signal devices in a u-healthcare(ubiquitous healthcare) system. In this system, wireless body area network (WBAN) such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We show several collaborative physiological devices and propose WBAN mechanism such as a fast scanning algorithm, a dynamic discovery and installation mechanism, a reliable data transmission, a device access control for security, and a healthcare profile for u-healthcare system.

  1. Neural crest specification by noncanonical Wnt signaling and PAR-1

    Science.gov (United States)

    Ossipova, Olga; Sokol, Sergei Y.

    2011-01-01

    Neural crest (NC) cells are multipotent progenitors that form at the neural plate border, undergo epithelial-mesenchymal transition and migrate to diverse locations in vertebrate embryos to give rise to many cell types. Multiple signaling factors, including Wnt proteins, operate during early embryonic development to induce the NC cell fate. Whereas the requirement for the Wnt/β-catenin pathway in NC specification has been well established, a similar role for Wnt proteins that do not stabilize β-catenin has remained unclear. Our gain- and loss-of-function experiments implicate Wnt11-like proteins in NC specification in Xenopus embryos. In support of this conclusion, modulation of β-catenin-independent signaling through Dishevelled and Ror2 causes predictable changes in premigratory NC. Morpholino-mediated depletion experiments suggest that Wnt11R, a Wnt protein that is expressed in neuroectoderm adjacent to the NC territory, is required for NC formation. Wnt11-like signals might specify NC by altering the localization and activity of the serine/threonine polarity kinase PAR-1 (also known as microtubule-associated regulatory kinase or MARK), which itself plays an essential role in NC formation. Consistent with this model, PAR-1 RNA rescues NC markers in embryos in which noncanonical Wnt signaling has been blocked. These experiments identify novel roles for Wnt11R and PAR-1 in NC specification and reveal an unexpected connection between morphogenesis and cell fate. PMID:22110058

  2. Wireless Energy Harvesting Using Signals from Multiple Fading Channels

    KAUST Repository

    Chen, Yunfei

    2017-08-01

    In this paper, we study the average, the probability density function and the cumulative distribution function of the harvested power. In the study, the signals are transmitted from multiple sources. The channels are assumed to be either Rician fading or Gamma-shadowed Rician fading. The received signals are then harvested by using either a single harvester for simultaneous transmissions or multiple harvesters for transmissions at different frequencies, antennas or time slots. Both linear and nonlinear models for the energy harvester at the receiver are examined. Numerical results are presented to show that, when a large amount of harvested power is required, a single harvester or the linear range of a practical nonlinear harvester are more efficient, to avoid power outage. Further, the power transfer strategy can be optimized for fixed total power. Specifically, for Rayleigh fading, the optimal strategy is to put the total power at the source with the best channel condition and switch off all other sources, while for general Rician fading, the optimum magnitudes and phases of the transmitting waveforms depend on the channel parameters.

  3. Lossless Compression Schemes for ECG Signals Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    C. Eswaran

    2007-01-01

    Full Text Available This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes.

  4. Real-time synchronization of wireless sensor network by 1-PPS signal

    Science.gov (United States)

    Giammarini, Marco; Pieralisi, Marco; Isidori, Daniela; Concettoni, Enrico; Cristalli, Cristina; Fioravanti, Matteo

    2015-05-01

    The use of wireless sensor networks with different nodes is desirable in a smart environment, because the network setting up and installation on preexisting structures can be done without a fixed cabled infrastructure. The flexibility of the monitoring system is fundamental where the use of a considerable quantity of cables could compromise the normal exercise, could affect the quality of acquired signal and finally increase the cost of the materials and installation. The network is composed of several intelligent "nodes", which acquires data from different kind of sensors, and then store or transmit them to a central elaboration unit. The synchronization of data acquisition is the core of the real-time wireless sensor network (WSN). In this paper, we present a comparison between different methods proposed by literature for the real-time acquisition in a WSN and finally we present our solution based on 1-Pulse-Per-Second (1-PPS) signal generated by GPS systems. The sensor node developed is a small-embedded system based on ARM microcontroller that manages the acquisition, the timing and the post-processing of the data. The communications between the sensors and the master based on IEEE 802.15.4 protocol and managed by dedicated software. Finally, we present the preliminary results obtained on a 3 floor building simulator with the wireless sensors system developed.

  5. A Sub-Space Method to Detect Multiple Wireless Microphone Signals in TV Band White Space

    CERN Document Server

    Dhillon, Harpreet S; Datla, Dinesh; Benonis, Michael; Buehrer, R Michael; Reed, Jeffrey H

    2011-01-01

    The main hurdle in the realization of dynamic spectrum access (DSA) systems from physical layer perspective is the reliable sensing of low power licensed users. One such scenario shows up in the unlicensed use of TV bands where the TV Band Devices (TVBDs) are required to sense extremely low power wireless microphones (WMs). The lack of technical standard among various wireless manufacturers and the resemblance of certain WM signals to narrow-band interference signals, such as spurious emissions, further aggravate the problem. Due to these uncertainties, it is extremely difficult to abstract the features of WM signals and hence develop robust sensing algorithms. To partly counter these challenges, we develop a two-stage sub-space algorithm that detects multiple narrow-band analog frequency-modulated signals generated by WMs. The performance of the algorithm is verified by using experimentally captured low power WM signals with received power ranging from -100 to -105 dBm. The problem of differentiating between...

  6. High-Capacity Wireless Signal Generation and Demodulation in 75- to 110-GHz Band Employing All-Optical OFDM

    DEFF Research Database (Denmark)

    Zibar, Darko; Sambaraju, Rakesh; Caballero Jambrina, Antonio

    2011-01-01

    We present a radio-frequency (RF) and bit-rate scalable technique for multigigabit wireless signal generation based on all-optical orthogonal frequency-division multiplexing (OFDM) and photonic up-conversion. Coherent detection supported by digital signal processing is used for signal demodulation...... and data recovery. In order to demonstrate the RF frequency scalability and bit-rate transparency, the system is tested at 60 GHz and in the 75- to 110-GHz band at the baud rates of 5 and 10 Gbaud. In terms of the bit rate, the proposed system is experimentally tested up to 40 Gb/s for wireless signal...

  7. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    Science.gov (United States)

    Kim, Ji Chul; Large, Edward W

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

  8. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

    Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  9. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  10. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  11. 400-GHz wireless transmission of 60-Gb/s nyquist-QPSK signals using UTC-PD and heterodyne mixer

    DEFF Research Database (Denmark)

    Yu, Xianbin; Asif, Rameez; Piels, Molly

    2016-01-01

    We experimentally demonstrate an optical network compatible high-speed optoelectronics THz wireless transmission system operating at 400-GHz band. In the experiment, optical Nyquist quadrature phase-shift keying signals in a 12.5-GHz ultradense wavelength-division multiplexing grid is converted...... to the THz wireless radiation by photomixing in an antenna integrated unitravelling photodiode. The photomixing is transparent to optical modulation formats. We also demonstrate in the experiment the scalability of our system by applying single to four channels, as well as mixed three channels. Wireless...... next generation 100 Gigabit Ethernet wired data stream for very high data rate indoor applications....

  12. The Software Design of SiBCN Temperature Sensor Wireless Sweep Signal Receiving and Dispatching System Based on FPGA

    Directory of Open Access Journals (Sweden)

    Jiang Chengzhi

    2017-01-01

    Full Text Available SiBCN (silicon boron carbon nitrogen wireless passive microwave resonant cavity temperature sensor is a new type of sensor under the development trend of radio frequency microwave technology. Based on the working principle of the sensor, the software design of SiBCN temperature sensor wireless sweep transceiver system based on FPGA is carried out on the basis of the existing wireless sweep signal transceiver system hardware. Let the signal source send the 11.0GHz ~ 11.6GHz range sweep signal to the sensor. The feedback signal of the sensor is filtered and the resonant frequency is obtained. The detection of temperature is based on the correspondence between the resonant frequency and the temperature.Through the analysis of the measured results, the system software design meets the requirements, and the temperature and frequency change rate is about 421.2KHz / °C.

  13. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  14. Characterization of a Class of Error Correcting Frames for Robust Signal Transmission over Wireless Communication Channels

    Directory of Open Access Journals (Sweden)

    Christine Guillemot

    2005-02-01

    Full Text Available Joint source-channel coding has been introduced recently as an element of QoS support for IP-based wired and wireless multimedia. Indeed, QoS provisioning in a global mobility context with highly varying channel characteristics is all the most challenging and requires a loosening of the layer and source-channel separation principle. Overcomplete frame expansions have been introduced as joint source-channel codes for erasure channels, that is, to allow for a signal representation that would be resilient to erasures in wired and wireless channels. In this paper, we characterize a class of frames for error correction besides erasure recovery in such channels. We associate the frames with complex number codes and characterize them based on the BCH-like property of the parity check matrices of the associated codes. We show that, in addition to the BCH-type decoding, subspace-based algorithms can also be used to localize errors over such frame expansion coefficients. When the frame expansion coefficients are quantized, we modify these algorithms suitably and compare their performances in terms of the accuracy of error localization and the signal-to-noise ratio of the reconstructed signal. In particular, we compare the frames associated with lowpass DFT, DCT, and DST codes, which belong to the defined class, in terms of their error correction efficiency.

  15. Design and implementation of signal processing scheme in CDMA wireless location systems

    Science.gov (United States)

    Yang, Tiejun; Fu, Hongliang

    2012-01-01

    Subscriber radio location techniques for code division multiple access (CDMA) cellular networks has been studied extensively in recent years. The network-based angle of arrival (AOA), time difference of arrival (TDOA), and time of arrival (TOA) techniques offer solutions to the position estimation problem. In this paper, The signal processing scheme of wireless location system based IS-95 is presented, in which TOA of reverse access channel transmissions is measured using sub-correlation detection algorithms and TOA estimation accuracy is improved using second search. Furthermore, reverse access channel decoding is implemented used to identify access channel message type, mobile identification, and dialed number.

  16. An FGF3-BMP Signaling Axis Regulates Caudal Neural Tube Closure, Neural Crest Specification and Anterior-Posterior Axis Extension.

    Science.gov (United States)

    Anderson, Matthew J; Schimmang, Thomas; Lewandoski, Mark

    2016-05-01

    During vertebrate axis extension, adjacent tissue layers undergo profound morphological changes: within the neuroepithelium, neural tube closure and neural crest formation are occurring, while within the paraxial mesoderm somites are segmenting from the presomitic mesoderm (PSM). Little is known about the signals between these tissues that regulate their coordinated morphogenesis. Here, we analyze the posterior axis truncation of mouse Fgf3 null homozygotes and demonstrate that the earliest role of PSM-derived FGF3 is to regulate BMP signals in the adjacent neuroepithelium. FGF3 loss causes elevated BMP signals leading to increased neuroepithelium proliferation, delay in neural tube closure and premature neural crest specification. We demonstrate that elevated BMP4 depletes PSM progenitors in vitro, phenocopying the Fgf3 mutant, suggesting that excessive BMP signals cause the Fgf3 axis defect. To test this in vivo we increased BMP signaling in Fgf3 mutants by removing one copy of Noggin, which encodes a BMP antagonist. In such mutants, all parameters of the Fgf3 phenotype were exacerbated: neural tube closure delay, premature neural crest specification, and premature axis termination. Conversely, genetically decreasing BMP signaling in Fgf3 mutants, via loss of BMP receptor activity, alleviates morphological defects. Aberrant apoptosis is observed in the Fgf3 mutant tailbud. However, we demonstrate that cell death does not cause the Fgf3 phenotype: blocking apoptosis via deletion of pro-apoptotic genes surprisingly increases all Fgf3 defects including causing spina bifida. We demonstrate that this counterintuitive consequence of blocking apoptosis is caused by the increased survival of BMP-producing cells in the neuroepithelium. Thus, we show that FGF3 in the caudal vertebrate embryo regulates BMP signaling in the neuroepithelium, which in turn regulates neural tube closure, neural crest specification and axis termination. Uncovering this FGF3-BMP signaling axis is

  17. Wireless biomedical signal monitoring device on wheelchair using noncontact electro-mechanical film sensor.

    Science.gov (United States)

    Kim, Jong-Myoung; Hong, Joo-Hyun; Cho, Myeong-Chan; Cha, Eun-Jong; Lee, Tae-Soo

    2007-01-01

    The present study purposed to measure the BCG (Ballistocardiogram) of subjects on a wheelchair using a noncontact electro-mechanical film sensor (EMFi sensor) and detect the respiratory rate from BCG in real-time while the subjects are moving. In order to measure wirelessly the BCG of subjects moving on a wheelchair, we made a seat-type noncontact EMFi sensor and developed a transmitter and a receiver using Zigbee wireless RF communication technology. The sensor is embedded with a 3-axis accelerometer to remove the noise of wheelchair vibration from BCG signal. Signal obtained from each sensor goes through the A/D converter and is recorded in the SD (Secure Digital) card in PDA (Personal Digital Assistance) with a receiving part. We also developed a PC (Personal Computer) data analysis program, analyzed data recorded in the SD card using the program, and presented the results in graph. Lastly, this study demonstrated that a warning message can be sent from PDA to the remote server via a CDMA (Code Division Multiple Access) network in case the person on wheelchair falls in emergency. Our experiment was carried out with healthy male and female adults in their 20s who volunteered to help this research. The results of analyzing collected data will show that the respiratory rate can be measured in real-time on a moving wheelchair.

  18. Antenna and coil design for wireless signal detection and charging of embedded power active contact lens.

    Science.gov (United States)

    Ng, Benny; Heckler, Paul; Do, Alex; Azar, Phillip; Leon, Errol; Smilkstein, Tina

    2014-01-01

    This paper presents a screen printed 2.4 GHz antenna and induction charging coil for an active contact lens with a single large pixel user display and on-board 3.8 V 5 uAh rechargeable battery. The antenna traces are printed using silver conductive paste on a 25 um polyethylene terephthalate (PET) substrate. The incoming signal from the antenna feeds into an IC that amplifies and rectifies the signal. The coil provides wireless energy transfer to inductively charge a thin film battery [1] located on the contact lens. The printed antenna achieved a S11 of -4 dB at 2.4 GHz and a gain of -13 dB.

  19. Theta signal as the neural signature of social exclusion.

    Science.gov (United States)

    Cristofori, Irene; Moretti, Laura; Harquel, Sylvain; Posada, Andres; Deiana, Gianluca; Isnard, Jean; Mauguière, François; Sirigu, Angela

    2013-10-01

    The feeling of being excluded from a social interaction triggers social pain, a sensation as intense as actual physical pain. Little is known about the neurophysiological underpinnings of social pain. We addressed this issue using intracranial electroencephalography in 15 patients performing a ball game where inclusion and exclusion blocks were alternated. Time-frequency analyses showed an increase in power of theta-band oscillations during exclusion in the anterior insula (AI) and posterior insula, the subgenual anterior cingulate cortex (sACC), and the fusiform "face area" (FFA). Interestingly, the AI showed an initial fast response to exclusion but the signal rapidly faded out. Activity in the sACC gradually increased and remained significant thereafter. This suggests that the AI may signal social pain by detecting emotional distress caused by the exclusion, whereas the sACC may be linked to the learning aspects of social pain. Theta activity in the FFA was time-locked to the observation of a player poised to exclude the participant, suggesting that the FFA encodes the social value of faces. Taken together, our findings suggest that theta activity represents the neural signature of social pain. The time course of this signal varies across regions important for processing emotional features linked to social information.

  20. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

    Science.gov (United States)

    Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Fan, Joline M.; Kaufman, Matthew T.; Churchland, Mark M.; Rivera-Alvidrez, Zuley; Cunningham, John P.; Ryu, Stephen I.; Shenoy, Krishna V.

    2011-08-01

    Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

  1. Two multichannel integrated circuits for neural recording and signal processing.

    Science.gov (United States)

    Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

    2003-02-01

    We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

  2. Modulation of hippocampal neural plasticity by glucose-related signaling.

    Science.gov (United States)

    Mainardi, Marco; Fusco, Salvatore; Grassi, Claudio

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural plasticity (i.e., dynamics of dendritic spines), and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  3. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2017-08-01

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  4. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    Directory of Open Access Journals (Sweden)

    Marco Mainardi

    2015-01-01

    Full Text Available Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression, structural plasticity (i.e., dynamics of dendritic spines, and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  5. Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

    OpenAIRE

    Chen, Mingzhe; Challita, Ursula; Saad, Walid; Yin, Changchuan; Debbah, Mérouane

    2017-01-01

    Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user d...

  6. Acquiring neural signals for developing a perception and cognition model

    Science.gov (United States)

    Li, Wei; Li, Yunyi; Chen, Genshe; Shen, Dan; Blasch, Erik; Pham, Khanh; Lynch, Robert

    2012-06-01

    The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding contextbased modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of large amounts of information in ways directly useful for human operators. However, integration of human intelligence into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph (EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model.

  7. Neural activity based biofeedback therapy for Autism spectrum disorder through wearable wireless textile EEG monitoring system

    Science.gov (United States)

    Sahi, Ahna; Rai, Pratyush; Oh, Sechang; Ramasamy, Mouli; Harbaugh, Robert E.; Varadan, Vijay K.

    2014-04-01

    Mu waves, also known as mu rhythms, comb or wicket rhythms are synchronized patterns of electrical activity involving large numbers of neurons, in the part of the brain that controls voluntary functions. Controlling, manipulating, or gaining greater awareness of these functions can be done through the process of Biofeedback. Biofeedback is a process that enables an individual to learn how to change voluntary movements for purposes of improving health and performance through the means of instruments such as EEG which rapidly and accurately 'feedback' information to the user. Biofeedback is used for therapeutic purpose for Autism Spectrum Disorder (ASD) by focusing on Mu waves for detecting anomalies in brain wave patterns of mirror neurons. Conventional EEG measurement systems use gel based gold cup electrodes, attached to the scalp with adhesive. It is obtrusive and wires sticking out of the electrodes to signal acquisition system make them impractical for use in sensitive subjects like infants and children with ASD. To remedy this, sensors can be incorporated with skull cap and baseball cap that are commonly used for infants and children. Feasibility of Textile based Sensor system has been investigated here. Textile based multi-electrode EEG, EOG and EMG monitoring system with embedded electronics for data acquisition and wireless transmission has been seamlessly integrated into fabric of these items for continuous detection of Mu waves. Textile electrodes were placed on positions C3, CZ, C4 according to 10-20 international system and their capability to detect Mu waves was tested. The system is ergonomic and can potentially be used for early diagnosis in infants and planning therapy for ASD patients.

  8. Digital seismo-acoustic signal processing aboard a wireless sensor platform

    Science.gov (United States)

    Marcillo, O.; Johnson, J. B.; Lorincz, K.; Werner-Allen, G.; Welsh, M.

    2006-12-01

    We are developing a low power, low-cost wireless sensor array to conduct real-time signal processing of earthquakes at active volcanoes. The sensor array, which integrates data from both seismic and acoustic sensors, is based on Moteiv TMote Sky wireless sensor nodes (www.moteiv.com). The nodes feature a Texas Instruments MSP430 microcontroller, 48 Kbytes of program memory, 10 Kbytes of static RAM, 1 Mbyte of external flash memory, and a 2.4-GHz Chipcon CC2420 IEEE 802.15.4 radio. The TMote Sky is programmed in TinyOS. Basic signal processing occurs on an array of three peripheral sensor nodes. These nodes are tied into a dedicated GPS receiver node, which is focused on time synchronization, and a central communications node, which handles data integration and additional processing. The sensor nodes incorporate dual 12-bit digitizers sampling a seismic sensor and a pressure transducer at 100 samples per second. The wireless capabilities of the system allow flexible array geometry, with a maximum aperture of 200m. We have already developed the digital signal processing routines on board the Moteiv Tmote sensor nodes. The developed routines accomplish Real-time Seismic-Amplitude Measurement (RSAM), Seismic Spectral- Amplitude Measurement (SSAM), and a user-configured Short Term Averaging / Long Term Averaging (STA LTA ratio), which is used to calculate first arrivals. The processed data from individual nodes are transmitted back to a central node, where additional processing may be performed. Such processing will include back azimuth determination and other wave field analyses. Future on-board signal processing will focus on event characterization utilizing pattern recognition and spectral characterization. The processed data is intended as low bandwidth information which can be transmitted periodically and at low cost through satellite telemetry to a web server. The processing is limited by the computational capabilities (RAM, ROM) of the nodes. Nevertheless, we

  9. Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

    Directory of Open Access Journals (Sweden)

    Qi An

    2016-01-01

    Full Text Available Promptitude and accuracy of signals’ non-data-aided (NDA identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power of Q. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of 10-4 even when the SNR is as low as -15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

  10. The time light signals of New Zealand: yet another way of communicating time in the pre-wireless era

    Science.gov (United States)

    Kinns, Roger

    2017-08-01

    The signalling of exact time using an array of lights appears to have been unique to New Zealand. It was a simple and effective solution for calibration of marine chronometers when transmission of time signals by wireless was in its infancy. Three lights, coloured green, red and white, were arranged in a vertical array. They were switched on in a defined sequence during the evening and then extinguished together to signal exact time. Time lights were first operated at the Dominion Observatory in Wellington during February 1912 and on the Ferry Building in Auckland during October 1915. The Wellington lights were immediately adjacent to the observatory buildings, but those in Auckland were operated using telegraph signals from Wellington. The timings varied over the years, but the same physical arrangement was retained at each location. The time light service was withdrawn during 1937, when wireless signals had become almost universally available for civil and navigation purposes.

  11. Influence of the complex-shape light signal on the neural network

    Science.gov (United States)

    Melnikov, Leonid A.; Novosselova, Anna V.; Blinova, Nadejda V.

    1999-03-01

    The effect of external signals of different shapes (constant, serrated and others) on the ring neural network modeling the visual perception is investigated numerically. New specific features in the dynamics of the neural network, such as the excitation, the swapping and the depression, were observed. The cooperative amplication of the external signal and the memory effect have been observed.

  12. Integration of Resonant Coil for Wireless Power Transfer and Implantable Antenna for Signal Transfer

    Directory of Open Access Journals (Sweden)

    Dong-Wook Seo

    2016-01-01

    Full Text Available We propose the integration of the resonant coil for wireless power transfer (WPT and the implantable antenna for physiological signal transfer. The integration allows for a compact biomedical implantable system such as electrocardiogram (ECG recorder and pacemaker. While the resonant coils resonate at the frequency of 13.56 MHz for the WPT, the implantable antenna works in the medical implant communications service (MICS band of 402–405 MHz for wireless communications. They share the narrow substrate area of a bar-type shape; the coil has the current path on the outer part of the substrate and the meandered planar inverted-F antenna (PIFA occupies the inside of the coil. To verify the potentials of the proposed structure, a prototype is fabricated and tested in vitro. The power transfer efficiency (PTE of about 20% is obtained at a distance of 15 mm and the antenna gain of roughly −40 dBi is achieved.

  13. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    methodology and real-world application domains and is widely entering into everyday solutions adopted by research and industry, going far beyond “traditional” neural networks and academic examples. As reflected in this collection, contemporary neural networks for signal processing combine many ideas from......This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume...

  14. Disrupted dorsal neural tube BMP signaling in the cilia mutant Arl13bhnn stems from abnormal Shh signaling

    Science.gov (United States)

    Horner, Vanessa L.; Caspary, Tamara

    2011-01-01

    In the embryonic neural tube, multiple signaling pathways work in concert to create functional neuronal circuits in the adult spinal cord. In the ventral neural tube, Sonic hedgehog (Shh) acts as a graded morphogen to specify neurons necessary for movement. In the dorsal neural tube, bone morphogenetic protein (BMP) and Wnt signals cooperate to specify neurons involved in sensation. Several signaling pathways, including Shh, rely on primary cilia in vertebrates. In this study, we used a mouse mutant with abnormal cilia, Arl13bhnn, to study the relationship between cilia, cell signaling, and neural tube patterning. Alr13bhnn mutants have abnormal ventral neural tube patterning due to disrupted Shh signaling; in addition, dorsal patterning defects occur, but the cause of these is unknown. Here we show that the Arl13bhnn dorsal patterning defects result from abnormal BMP signaling. In addition, we find that Wnt ligands are abnormally expressed in Arl13bhnn mutants; surprisingly, however, downstream Wnt signaling is normal. We demonstrate that Arl13b is required non-autonomously for BMP signaling and Wnt ligand expression, indicating that the abnormal Shh signaling environment in Arl13bhnn embryos indirectly causes dorsal defects. PMID:21539826

  15. The Irregularity Propagation Characteristics of Radio Signals For Wireless Sensor Network In Farmland

    Directory of Open Access Journals (Sweden)

    Zhu Hua-Ji

    2016-01-01

    Full Text Available This work aims to investigate the irregular propagation characteristics of wireless sensor network (WSN at frequency of 433 MHz. Through the analysis of the received signal strength indicator (RSSI, it is found that the variance in received signal strength is largely random, along with a continuous change with incremental changes in direction. For the transceiver distance is 20 m, the packet loss rate (PLR in all directions is relatively small except for the east direction, indicating that the signal is still strong in all directions. While for the transceiver distance is 40 m, it can be found that there is about 90% packet loss in the east direction. That is, the received signal strength in the east direction is lower than that in the other directions. Moreover, the communication range varies with the degree of receiver direction ranging from 0 to 359. Through the regression analysis in Matlab, we find that the optimal fitting models in different directions are different. The optimal fitting model in east and west direction is the modified exponential decay, and in south and north direction is the linear logarithmic model. The values of R2 vary from 0.935 to 0.961, and the values of RMSE range from 1.75 to 2.31.

  16. Neural signalling of food healthiness associated with emotion processing

    Directory of Open Access Journals (Sweden)

    Uwe eHerwig

    2016-02-01

    Full Text Available The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regionsThirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analogue scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences.We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy.Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signalling associated with reward and self-relevance, which could promote salutary nutrition behaviour. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  17. Slit/Robo1 signaling regulates neural tube development by balancing neuroepithelial cell proliferation and differentiation

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Guang; Li, Yan; Wang, Xiao-yu [Key Laboratory for Regenerative Medicine of The Ministry of Education, Department of Histology and Embryology, School of Medicine, Jinan University, Guangzhou 510632 (China); Han, Zhe [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510224 (China); Chuai, Manli [College of Life Sciences Biocentre, University of Dundee, Dundee DD1 5EH (United Kingdom); Wang, Li-jing [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510224 (China); Ho Lee, Kenneth Ka [Stem Cell and Regeneration Thematic Research Programme, School of Biomedical Sciences, Chinese University of Hong Kong, Shatin (Hong Kong); Geng, Jian-guo, E-mail: jgeng@umich.edu [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510224 (China); Department of Biologic and Materials Sciences, University of Michigan School of Dentistry, Ann Arbor, MI 48109 (United States); Yang, Xuesong, E-mail: yang_xuesong@126.com [Key Laboratory for Regenerative Medicine of The Ministry of Education, Department of Histology and Embryology, School of Medicine, Jinan University, Guangzhou 510632 (China)

    2013-05-01

    Formation of the neural tube is the morphological hallmark for development of the embryonic central nervous system (CNS). Therefore, neural tube development is a crucial step in the neurulation process. Slit/Robo signaling was initially identified as a chemo-repellent that regulated axon growth cone elongation, but its role in controlling neural tube development is currently unknown. To address this issue, we investigated Slit/Robo1 signaling in the development of chick neCollege of Life Sciences Biocentre, University of Dundee, Dundee DD1 5EH, UKural tube and transgenic mice over-expressing Slit2. We disrupted Slit/Robo1 signaling by injecting R5 monoclonal antibodies into HH10 neural tubes to block the Robo1 receptor. This inhibited the normal development of the ventral body curvature and caused the spinal cord to curl up into a S-shape. Next, Slit/Robo1 signaling on one half-side of the chick embryo neural tube was disturbed by electroporation in ovo. We found that the morphology of the neural tube was dramatically abnormal after we interfered with Slit/Robo1 signaling. Furthermore, we established that silencing Robo1 inhibited cell proliferation while over-expressing Robo1 enhanced cell proliferation. We also investigated the effects of altering Slit/Robo1 expression on Sonic Hedgehog (Shh) and Pax7 expression in the developing neural tube. We demonstrated that over-expressing Robo1 down-regulated Shh expression in the ventral neural tube and resulted in the production of fewer HNK-1{sup +} migrating neural crest cells (NCCs). In addition, Robo1 over-expression enhanced Pax7 expression in the dorsal neural tube and increased the number of Slug{sup +} pre-migratory NCCs. Conversely, silencing Robo1 expression resulted in an enhanced Shh expression and more HNK-1{sup +} migrating NCCs but reduced Pax7 expression and fewer Slug{sup +} pre-migratory NCCs were observed. In conclusion, we propose that Slit/Robo1 signaling is involved in regulating neural tube

  18. The relationship between Sonic hedgehog signalling, cilia and neural tube defects

    Science.gov (United States)

    Murdoch, Jennifer N.; Copp, Andrew J.

    2013-01-01

    The Hedgehog signalling pathway is essential for many aspects of normal embryonic development, including formation and patterning of the neural tube. Absence of Shh ligand is associated with the midline defect holoprosencephaly, while increased Shh signalling is associated with exencephaly and spina bifida. To complicate this apparently simple relationship, mutation of proteins required for function of cilia often leads to impaired Shh signalling and to disruption of neural tube closure. In this manuscript, we review the literature on Shh pathway mutants and discuss the relationship between Shh signalling, cilia and neural tube defects. PMID:20544799

  19. EIRP Characterization of Electrically Large Wireless Equipment with Integrated Signal Generator in a Compact Environment

    Directory of Open Access Journals (Sweden)

    Soon-Soo Oh

    2015-01-01

    Full Text Available We describe a measurement technique to characterize the equivalent isotropically radiated power (EIRP of electrically large wireless equipment in a compact environment. A modified phase-measurement method was proposed and, thus, the separation of the signal generator and radiating element was not required during the measurement. A Fresnel-to-far-field transformation was used for the fast measurement time in a compact anechoic chamber. An experimental verification of the method was carried out in a compact anechoic chamber, where the source-detector separation was approximately 1/5 of the far-field distance. The measured magnitude and phase pattern exhibited only a small error. The EIRP obtained using a Fresnel-to-far-field transformation was compared with a reference value, and the error was within 0.5 dB.

  20. GPS low noise amplifier with high immunity to wireless jamming signals and power control option

    Directory of Open Access Journals (Sweden)

    H. Schulz

    2004-01-01

    Full Text Available A SiGe GPS low noise amplifier with power control option and high immunity to wireless jamming signals is presented. These novel features applied to Atmel’s ATR0610 GPS LNA allow significant power saving at the radio interface while meeting the out-of-band linearity requirements. The results show the noise figure less than 2.1 dB, including the embedded pre-select filter, and out-of-band IIP3 above +8 dBm in the frequency range between 1.8GHz and 2 GHz with 3mA current consumption. The GPS system performance shows GPS sensitivity below -141 dBm with 5 ms integration interval.

  1. Signaling and transcriptional regulation in neural crest specification and migration: lessons from xenopus embryos.

    Science.gov (United States)

    Pegoraro, Caterina; Monsoro-Burq, Anne H

    2013-01-01

    The neural crest is a population of highly migratory and multipotent cells, which arises from the border of the neural plate in vertebrate embryos. In the last few years, the molecular actors of neural crest early development have been intensively studied, notably by using the frog embryo, as a prime model for the analysis of the earliest embryonic inductions. In addition, tremendous progress has been made in understanding the molecular and cellular basis of Xenopus cranial neural crest migration, by combining in vitro and in vivo analysis. In this review, we examine how the action of previously known neural crest-inducing signals [bone morphogenetic protein (BMP), wingless-int (Wnt), fibroblast growth factor (FGF)] is controlled by newly discovered modulators during early neural plate border patterning and neural crest specification. This regulation controls the induction of key transcription factors that cooperate to pattern the premigratory neural crest progenitors. These data are discussed in the perspective of the gene regulatory network that controls neural and neural crest patterning. We then address recent findings on noncanonical Wnt signaling regulation, cell polarization, and collective cell migration which highlight how cranial neural crest cells populate their target tissue, the branchial arches, in vivo. More than ever, the neural crest stands as a powerful and attractive model to decipher complex vertebrate regulatory circuits in vivo. Copyright © 2012 Wiley Periodicals, Inc.

  2. An electromagnetic signals monitoring and analysis wireless platform employing personal digital assistants and pattern analysis techniques

    Science.gov (United States)

    Ninos, K.; Georgiadis, P.; Cavouras, D.; Nomicos, C.

    2010-05-01

    This study presents the design and development of a mobile wireless platform to be used for monitoring and analysis of seismic events and related electromagnetic (EM) signals, employing Personal Digital Assistants (PDAs). A prototype custom-developed application was deployed on a 3G enabled PDA that could connect to the FTP server of the Institute of Geodynamics of the National Observatory of Athens and receive and display EM signals at 4 receiver frequencies (3 KHz (E-W, N-S), 10 KHz (E-W, N-S), 41 MHz and 46 MHz). Signals may originate from any one of the 16 field-stations located around the Greek territory. Employing continuous recordings of EM signals gathered from January 2003 till December 2007, a Support Vector Machines (SVM)-based classification system was designed to distinguish EM precursor signals within noisy background. EM-signals corresponding to recordings preceding major seismic events (Ms≥5R) were segmented, by an experienced scientist, and five features (mean, variance, skewness, kurtosis, and a wavelet based feature), derived from the EM-signals were calculated. These features were used to train the SVM-based classification scheme. The performance of the system was evaluated by the exhaustive search and leave-one-out methods giving 87.2% overall classification accuracy, in correctly identifying EM precursor signals within noisy background employing all calculated features. Due to the insufficient processing power of the PDAs, this task was performed on a typical desktop computer. This optimal trained context of the SVM classifier was then integrated in the PDA based application rendering the platform capable to discriminate between EM precursor signals and noise. System's efficiency was evaluated by an expert who reviewed 1/ multiple EM-signals, up to 18 days prior to corresponding past seismic events, and 2/ the possible EM-activity of a specific region employing the trained SVM classifier. Additionally, the proposed architecture can form a

  3. Reconfigurable radio access unit to dynamically distribute W-band signals in 5G wireless access networks

    DEFF Research Database (Denmark)

    Rodríguez Páez, Juan Sebastián; Rommel, Simon; Vegas Olmos, Juan José

    2017-01-01

    In this paper a new type of radio access unit is proposed and demonstrated. This unit is composed only of the reduced amount of components (compared to conventional unit designs) to optically generate wireless signals on the W-band (75–110 GHz) in combination with a switching system. The proposed...... system not only achieves BER values below the FEC limit, but gives an extra level of flexibility to the network by easing the redirection of the signal to different antennas....

  4. Signal Processing for Wireless Communication MIMO System with Nano- Scaled CSDG MOSFET based DP4T RF Switch.

    Science.gov (United States)

    Srivastava, Viranjay M

    2015-01-01

    In the present technological expansion, the radio frequency integrated circuits in the wireless communication technologies became useful because of the replacement of increasing number of functions, traditional hardware components by modern digital signal processing. The carrier frequencies used for communication systems, now a day, shifted toward the microwave regime. The signal processing for the multiple inputs multiple output wireless communication system using the Metal- Oxide-Semiconductor Field-Effect-Transistor (MOSFET) has been done a lot. In this research the signal processing with help of nano-scaled Cylindrical Surrounding Double Gate (CSDG) MOSFET by means of Double- Pole Four-Throw Radio-Frequency (DP4T RF) switch, in terms of Insertion loss, Isolation, Reverse isolation and Inter modulation have been analyzed. In addition to this a channel model has been presented. Here, we also discussed some patents relevant to the topic.

  5. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2012-01-01

    Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

  6. A Routing Protocol Based on Received Signal Strength for Underwater Wireless Sensor Networks (UWSNs

    Directory of Open Access Journals (Sweden)

    Meiju Li

    2017-11-01

    Full Text Available Underwater wireless sensor networks (UWSNs are featured by long propagation delay, limited energy, narrow bandwidth, high BER (Bit Error Rate and variable topology structure. These features make it very difficult to design a short delay and high energy-efficiency routing protocol for UWSNs. In this paper, a routing protocol independent of location information is proposed based on received signal strength (RSS, which is called RRSS. In RRSS, a sensor node firstly establishes a vector from the node to a sink node; the length of the vector indicates the RSS of the beacon signal (RSSB from the sink node. A node selects the next-hop along the vector according to RSSB and the RSS of a hello packet (RSSH. The node nearer to the vector has higher priority to be a candidate next-hop. To avoid data packets being delivered to the neighbor nodes in a void area, a void-avoiding algorithm is introduced. In addition, residual energy is considered when selecting the next-hop. Meanwhile, we establish mathematic models to analyze the robustness and energy efficiency of RRSS. Lastly, we conduct extensive simulations, and the simulation results show RRSS can save energy consumption and decrease end-to-end delay.

  7. The neural subjective frame: from bodily signals to perceptual consciousness

    Science.gov (United States)

    Park, Hyeong-Dong; Tallon-Baudry, Catherine

    2014-01-01

    The report ‘I saw the stimulus’ operationally defines visual consciousness, but where does the ‘I’ come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness. PMID:24639580

  8. Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan

    1999-01-01

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...

  9. Wireless Biological Electronic Sensors.

    Science.gov (United States)

    Cui, Yue

    2017-10-09

    The development of wireless biological electronic sensors could open up significant advances for both fundamental studies and practical applications in a variety of areas, including medical diagnosis, environmental monitoring, and defense applications. One of the major challenges in the development of wireless bioelectronic sensors is the successful integration of biosensing units and wireless signal transducers. In recent years, there are a few types of wireless communication systems that have been integrated with biosensing systems to construct wireless bioelectronic sensors. To successfully construct wireless biological electronic sensors, there are several interesting questions: What types of biosensing transducers can be used in wireless bioelectronic sensors? What types of wireless systems can be integrated with biosensing transducers to construct wireless bioelectronic sensors? How are the electrical sensing signals generated and transmitted? This review will highlight the early attempts to address these questions in the development of wireless biological electronic sensors.

  10. Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis.

    Science.gov (United States)

    Ghamari, M; Soltanpur, C; Cabrera, S; Romero, R; Martinek, R; Nazeran, H

    2016-08-01

    Heart Rate Variability (HRV) signal analysis provides a quantitative marker of the Autonomic Nervous System (ANS) function. A wristband-type wireless photoplethysmographic (PPG) device was custom-designed to collect and analyze the arterial pulse in the wrist. The proposed device is comprised of an optical sensor to monitor arterial pulse, a signal conditioning unit to filter and amplify the analog PPG signal, a microcontroller to digitize the analog PPG signal, and a Bluetooth module to transfer the data to a smart device. This paper proposes a novel model to represent the PPG signal as the summation of two Gaussian functions. The paper concludes with a verification procedure for HRV signal analysis during sedentary activities.

  11. Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

    OpenAIRE

    Kale, S. N.; Dudul, S. V.

    2009-01-01

    Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors d...

  12. Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

    Directory of Open Access Journals (Sweden)

    Vahid Emamian

    2003-03-01

    Full Text Available Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA, and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

  13. Neural retina identity is specified by lens-derived BMP signals.

    Science.gov (United States)

    Pandit, Tanushree; Jidigam, Vijay K; Patthey, Cedric; Gunhaga, Lena

    2015-05-15

    The eye has served as a classical model to study cell specification and tissue induction for over a century. Nevertheless, the molecular mechanisms that regulate the induction and maintenance of eye-field cells, and the specification of neural retina cells are poorly understood. Moreover, within the developing anterior forebrain, how prospective eye and telencephalic cells are differentially specified is not well defined. In the present study, we have analyzed these issues by manipulating signaling pathways in intact chick embryo and explant assays. Our results provide evidence that at blastula stages, BMP signals inhibit the acquisition of eye-field character, but from neural tube/optic vesicle stages, BMP signals from the lens are crucial for the maintenance of eye-field character, inhibition of dorsal telencephalic cell identity and specification of neural retina cells. Subsequently, our results provide evidence that a Rax2-positive eye-field state is not sufficient for the progress to a neural retina identity, but requires BMP signals. In addition, our results argue against any essential role of Wnt or FGF signals during the specification of neural retina cells, but provide evidence that Wnt signals together with BMP activity are sufficient to induce cells of retinal pigment epithelial character. We conclude that BMP activity emanating from the lens ectoderm maintains eye-field identity, inhibits telencephalic character and induces neural retina cells. Our findings link the requirement of the lens ectoderm for neural retina specification with the molecular mechanism by which cells in the forebrain become specified as neural retina by BMP activity. © 2015. Published by The Company of Biologists Ltd.

  14. Effects of social sustainability signals on neural valuation signals and taste-experience of food products

    Directory of Open Access Journals (Sweden)

    Laura eEnax

    2015-09-01

    Full Text Available Value-based decision making occurs when individuals choose between different alternatives and place a value on each alternative and its attributes. Marketing actions frequently manipulate product attributes, by adding e.g., health claims on the packaging. A previous imaging study found that an emblem for organic products increased willingness to pay (WTP and activity in the ventral striatum (VS. The current study investigated neural and behavioral processes underlying the influence of Fair Trade (FT labeling on food valuation and choice. Sustainability is an important product attribute for many consumers, with FT signals being one way to highlight ethically sustainable production. Forty participants valuated products in combination with an FT emblem or no emblem and stated their WTP in a bidding task while in an MRI scanner. After that, participants tasted – objectively identical – chocolates, presented either as FT or as conventionally produced. In the fMRI task, WTP was significantly higher for FT products. FT labeling increased activity in regions important for reward-processing and salience, that is, in the VS, anterior and posterior cingulate, as well as superior frontal gyrus. Subjective value, that is, WTP was correlated with activity in the ventromedial prefrontal cortex (vmPFC. We find that the anterior cingulate, VS and superior frontal gyrus exhibit task-related increases in functional connectivity to the vmPFC when an FT product was evaluated, suggesting a network which alters valuation processes. We also found a significant taste-placebo effect, with higher experienced taste pleasantness and intensity for FT labeled chocolates. Our results reveal a possible neural mechanism underlying valuation processes of certified food products. The results are important in light of understanding current marketing trends as well as designing future interventions that aim at positively influencing food choice.

  15. Effects of social sustainability signaling on neural valuation signals and taste-experience of food products.

    Science.gov (United States)

    Enax, Laura; Krapp, Vanessa; Piehl, Alexandra; Weber, Bernd

    2015-01-01

    Value-based decision making occurs when individuals choose between different alternatives and place a value on each alternative and its attributes. Marketing actions frequently manipulate product attributes, by adding, e.g., health claims on the packaging. A previous imaging study found that an emblem for organic products increased willingness to pay (WTP) and activity in the ventral striatum (VS). The current study investigated neural and behavioral processes underlying the influence of Fair Trade (FT) labeling on food valuation and choice. Sustainability is an important product attribute for many consumers, with FT signals being one way to highlight ethically sustainable production. Forty participants valuated products in combination with an FT emblem or no emblem and stated their WTP in a bidding task while in an MRI scanner. After that, participants tasted-objectively identical-chocolates, presented either as "FT" or as "conventionally produced". In the fMRI task, WTP was significantly higher for FT products. FT labeling increased activity in regions important for reward-processing and salience, that is, in the VS, anterior and posterior cingulate, as well as superior frontal gyrus. Subjective value, that is, WTP was correlated with activity in the ventromedial prefrontal cortex (vmPFC). We find that the anterior cingulate, VS and superior frontal gyrus exhibit task-related increases in functional connectivity to the vmPFC when an FT product was evaluated. Effective connectivity analyses revealed a highly probable directed modulation of the vmPFC by those three regions, suggesting a network which alters valuation processes. We also found a significant taste-placebo effect, with higher experienced taste pleasantness and intensity for FT labeled chocolates. Our results reveal a possible neural mechanism underlying valuation processes of certified food products. The results are important in light of understanding current marketing trends as well as designing

  16. A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

    Science.gov (United States)

    von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

    2016-10-26

    The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

  17. G-protein-coupled receptor signaling and neural tube closure defects.

    Science.gov (United States)

    Shimada, Issei S; Mukhopadhyay, Saikat

    2017-01-30

    Disruption of the normal mechanisms that mediate neural tube closure can result in neural tube defects (NTDs) with devastating consequences in affected patients. With the advent of next-generation sequencing, we are increasingly detecting mutations in multiple genes in NTD cases. However, our ability to determine which of these genes contribute to the malformation is limited by our understanding of the pathways controlling neural tube closure. G-protein-coupled receptors (GPCRs) comprise the largest family of transmembrane receptors in humans and have been historically favored as drug targets. Recent studies implicate several GPCRs and downstream signaling pathways in neural tube development and closure. In this review, we will discuss our current understanding of GPCR signaling pathways in pathogenesis of NTDs. Notable examples include the orphan primary cilia-localized GPCR, Gpr161 that regulates the basal suppression machinery of sonic hedgehog pathway by means of activation of cAMP-protein kinase A signaling in the neural tube, and protease-activated receptors that are activated by a local network of membrane-tethered proteases during neural tube closure involving the surface ectoderm. Understanding the role of these GPCR-regulated pathways in neural tube development and closure is essential toward identification of underlying genetic causes to prevent NTDs. Birth Defects Research 109:129-139, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  18. A Novel Technique for Transmission of M-Ary Signal through Wireless Fading Channel Using Wavelet Denoising

    Directory of Open Access Journals (Sweden)

    Md. Zahangir Alam

    2011-01-01

    Full Text Available The paper proposes a novel technique for reducing noise in M-ary signal transmission through wireless fading channel using wavelet denoising that play the key role. The paper also explains that the conventional threshold-based technique is not capable of denoising M-ary quadrature amplitude modulated (M-QAM signals having multilevel wavelet coefficients through wireless fading channels. A detailed step by step wavelet decomposition and reconstruction processes are discussed here to transform a signal function into wavelet coefficients using simulation software like MATLAB. A 16-QAM modulated symbol through a Rician fading channel is weighted by a control variable of complex form to force the mean of each detail coefficient except low frequency component to zero to enhance noiseless property. The bit error rate (BER performance of the simulation results are furnished to show the effectiveness of the proposed technique. The root mean square of the deviation of the reconstruct signal from the original signal is used to express the effectiveness of the proposed technique. The traditional denoising provides very high value (above 90% of the percentage root mean square difference (PDR and the proposed technique provides only 10% PDR value for the symbol through a noisy channel. The result of the simulation study reveals that the BER performance can be increased using an appropriate control variable to force the mean of each detail coefficient to zero.

  19. Effects of the Wireless Channel, Signal Compression and Network Architecture on Speech Quality in Voip Networks

    National Research Council Canada - National Science Library

    Nikolaos, Tiantioukas

    2007-01-01

    .... The purpose of this thesis is to measure the quality of voice in VoIP communications. More specifically it investigates the effects of wireless channel conditions as well as channel coding and compression on the received speech quality...

  20. Comparative evaluation of different wavelet thresholding methods for neural signal processing.

    Science.gov (United States)

    Barabino, Gianluca; Baldazzi, Giulia; Sulas, Eleonora; Carboni, Caterina; Raffo, Luigi; Pani, Danilo

    2017-07-01

    Neural signal decoding is the basis for the development of neuroprosthetic devices and systems. Depending on the part of the nervous system these signals are picked up from, different signal-to-noise ratios (SNR) can be experienced. Wavelet denoising is often adopted due to its capability of reducing, to some extent, the noise falling within the signal spectrum. Several variables influence the denoising quality, but usually the focus in on the selection of the best performing mother wavelet. However, the threshold definition and the way it is applied to the signal have a significant impact on the denoising quality, determining the amount of noise removed and the distortion introduced on the signal. This work presents a comparative analysis of different threshold definition and thresholding mechanisms on neural signals, either largely adopted for neural signal processing or not. In order to evaluate the quality of the denoising in terms of the introduced distortion, which is important when decoding is implemented through spike-sorting algorithms, a synthetic dataset built on real action potentials was used, creating signals with different SNR and characterized by an additive white Gaussian noise (AWGN). The obtained results reveal the superiority of an approach, originally conceived for noisy non-linear time series, over the more typical ones. When compared to the original signal, a correlation above 0.9 was obtained, while in terms of root mean square error (RMSE) an improvement of 13% and 33% was reported with respect to the Minimax and Universal thresholds respectively.

  1. Neural responses to multimodal ostensive signals in 5-month-old infants.

    Directory of Open Access Journals (Sweden)

    Eugenio Parise

    Full Text Available Infants' sensitivity to ostensive signals, such as direct eye contact and infant-directed speech, is well documented in the literature. We investigated how infants interpret such signals by assessing common processing mechanisms devoted to them and by measuring neural responses to their compounds. In Experiment 1, we found that ostensive signals from different modalities display overlapping electrophysiological activity in 5-month-old infants, suggesting that these signals share neural processing mechanisms independently of their modality. In Experiment 2, we found that the activation to ostensive signals from different modalities is not additive to each other, but rather reflects the presence of ostension in either stimulus stream. These data support the thesis that ostensive signals obligatorily indicate to young infants that communication is directed to them.

  2. Within a Stone's Throw: Proximal Geolocation of Internet Users via Covert Wireless Signaling

    Energy Technology Data Exchange (ETDEWEB)

    Paul, Nathanael R [ORNL; Shue, Craig [Worcester Polytechnic Institute, Worcester; Taylor, Curtis [Worcester Polytechnic Institute, Worcester

    2013-01-01

    While Internet users may often believe they have anonymity online, a culmination of technologies and recent research may allow an adversary to precisely locate an online user s geophysical location. In many cases, such as peer-to-peer applications, an adversary can easily use a target s IP address to quickly obtain the general geographical location of the target. Recent research has scoped this general area to a 690m (0.43 mile) radius circle. In this work, we show how an adversary can exploit Internet communication for geophysical location by embedding covert signals in communication with a target on a remote wireless local area network. We evaluated the approach in two common real-world settings: a residential neighborhood and an apartment building. In the neighborhood case, we used a single-blind trial in which an observer located a target network to within three houses in less than 40 minutes. Directional antennas may have allowed even more precise geolocation. This approach had only a 0.38% false positive rate, despite 24,000 observed unrelated packets and many unrelated networks. This low rate allowed the observer to exclude false locations and continue searching for the target. Our results enable law enforcement or copyright holders to quickly locate online Internet users without requiring time-consuming subpoenas to Internet Service Providers. Other privacy use cases include rapidly locating individuals based on their online speech or interests. We hope to raise awareness of these issues and to spur discussion on privacy and geolocating techniques.

  3. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  4. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    OpenAIRE

    Marco Mainardi; Salvatore Fusco; Claudio Grassi

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural p...

  5. Using adaline neural network for performance improvement of smart antennas in TDD wireless communications.

    Science.gov (United States)

    Kavak, Adnan; Yigit, Halil; Ertunc, H Metin

    2005-11-01

    In time-division-duplex (TDD) mode wireless communications, downlink beamforming performance of a smart antenna system at the base station can be degraded due to variation of spatial signature vectors corresponding to mobile users especially in fast fading scenarios. To mitigate this, downlink beams must be controlled by properly adjusting their weight vectors in response to changing propagation dynamics. This can be achieved by modeling the spatial signature vectors in the uplink period and then predicting them to be used as beamforming weight vectors for the new mobile position in the downlink transmission period. We show that ADAptive LInear NEuron (ADALINE) network modeling based prediction of spatial signatures provides certain level of performance improvement compared to conventional beamforming method that employs spatial signature obtained in previous uplink interval. We compare the performance of ADALINE with autoregressive (AR) modeling based predictions under varying channel propagation (mobile speed, multipath angle spread, and number of multipaths), and filter order/delay conditions. ADALINE modeling outperforms AR modeling in terms of downlink SNR improvement and relative error improvement especially under high mobile speeds, i.e., V = 100 km/h.

  6. Towards closed-loop neuromodulation: a wireless miniaturized neural implant SoC

    Science.gov (United States)

    Liu, Wentai; Wang, Po-Min; Lo, Yi-Kai

    2017-05-01

    This work reports a platform technology toward the development of closed-loop neuromodulation. A neural implant based on the SoC developed in our laboratory is used as an example to illustrate the necessary functionalities for the efficacious implantable system. We also present an example of using the system to investigate the epidural stimulation for partial motor function recovery after spinal cord injury in a rat model. This hardware-software co-design tool demonstrate its promising potential towards an effective closed-loop neuromodulation for various biomedical applications.

  7. An Artificial Neural Network Based Robot Controller that Uses Rat’s Brain Signals

    Directory of Open Access Journals (Sweden)

    Marsel Mano

    2013-04-01

    Full Text Available Brain machine interface (BMI has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.

  8. A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

    Science.gov (United States)

    Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo

    2012-07-01

    A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

  9. 40 Gb/s W-band (75-110 GHz) 16-QAM radio-over-fiber signal generation and its wireless transmission.

    Science.gov (United States)

    Kanno, Atsushi; Inagaki, Keizo; Morohashi, Isao; Sakamoto, Takahide; Kuri, Toshiaki; Hosako, Iwao; Kawanishi, Tetsuya; Yoshida, Yuki; Kitayama, Ken-ichi

    2011-12-12

    The generation of a 40-Gb/s 16-QAM radio-over-fiber (RoF) signal and its demodulation of the wireless signal transmitted over free space of 30 mm in W-band (75-110 GHz) is demonstrated. The 16-QAM signal is generated by a coherent polarization synthesis method using a dual-polarization QPSK modulator. A combination of the simple RoF generation and the versatile digital receiver technique is suitable for the proposed coherent optical/wireless seamless network. © 2011 Optical Society of America

  10. Neural basis of impaired safety signaling in Obsessive Compulsive Disorder.

    Science.gov (United States)

    Apergis-Schoute, Annemieke M; Gillan, Claire M; Fineberg, Naomi A; Fernandez-Egea, Emilio; Sahakian, Barbara J; Robbins, Trevor W

    2017-03-21

    The ability to assign safety to stimuli in the environment is integral to everyday functioning. A key brain region for this evaluation is the ventromedial prefrontal cortex (vmPFC). To investigate the importance of vmPFC safety signaling, we used neuroimaging of Pavlovian fear reversal, a paradigm that involves flexible updating when the contingencies for a threatening (CS+) and safe (CS-) stimulus reverse, in a prototypical disorder of inflexible behavior influenced by anxiety, Obsessive Compulsive Disorder (OCD). Skin conductance responses in OCD patients (n = 43) failed to differentiate during reversal compared with healthy controls (n = 35), although significant differentiation did occur during early conditioning and amygdala BOLD signaling was unaffected in these patients. Increased vmPFC activation (for CS+ > CS-) during early conditioning predicted the degree of generalization in OCD patients during reversal, whereas vmPFC safety signals were absent throughout learning in these patients. Regions of the salience network (dorsal anterior cingulate, insula, and thalamus) showed early learning task-related hyperconnectivity with the vmPFC in OCD, consistent with biased processing of the CS+. Our findings reveal an absence of vmPFC safety signaling in OCD, undermining flexible threat updating and explicit contingency knowledge. Although differential threat learning can occur to some extent in the absence of vmPFC safety signals, effective CS- signaling becomes crucial during conflicting threat and safety cues. These results promote further investigation of vmPFC safety signaling in other anxiety disorders, with potential implications for the development of exposure-based therapies, in which safety signaling is likely to play a key role.

  11. Development of wireless, chipless neural stimulator by using one-port surface acoustic wave delay line and diode-capacitor interface

    Science.gov (United States)

    Kim, Jisung; Kim, Saehan; Lee, Keekeun

    2017-06-01

    For the first time, a wireless and chipless neuron stimulator was developed by utilizing a surface acoustic wave (SAW) delay line, a diode-capacitor interface, a sharp metal tip, and antennas for the stimulation of neurons in the brain. The SAW delay line supersedes presently existing complex wireless transmission systems composed of a few thousands of transistors, enabling the fabrication of wireless and chipless transceiver systems. The diode-capacitor interface was used to convert AC signals to DC signals and induce stimulus pulses at a sharp metal probe. A 400 MHz RF energy was wirelessly radiated from antennas and then stimulation pulses were observed at a sharp gold probe. A ˜5 m reading distance was obtained using a 1 mW power from a network analyzer. The cycles of electromagnetic (EM) radiation from an antenna were controlled by shielding the antenna with an EM absorber. Stimulation pulses with different amplitudes and durations were successfully observed at the probe. The obtained pulses were ˜0.08 mV in amplitude and 3-10 Hz in frequency. Coupling-of-mode (COM) and SPICE modeling simulations were also used to determine the optimal structural parameters for SAW delay line and the values of passive elements. On the basis of the extracted parameters, the entire system was experimentally implemented and characterized.

  12. Perlecan is required for FGF-2 signaling in the neural stem cell niche

    Directory of Open Access Journals (Sweden)

    Aurelien Kerever

    2014-03-01

    Full Text Available In the adult subventricular zone (neurogenic niche, neural stem cells double-positive for two markers of subsets of neural stem cells in the adult central nervous system, glial fibrillary acidic protein and CD133, lie in proximity to fractones and to blood vessel basement membranes, which contain the heparan sulfate proteoglycan perlecan. Here, we demonstrate that perlecan deficiency reduces the number of both GFAP/CD133-positive neural stem cells in the subventricular zone and new neurons integrating into the olfactory bulb. We also show that FGF-2 treatment induces the expression of cyclin D2 through the activation of the Akt and Erk1/2 pathways and promotes neurosphere formation in vitro. However, in the absence of perlecan, FGF-2 fails to promote neurosphere formation. These results suggest that perlecan is a component of the neurogenic niche that regulates FGF-2 signaling and acts by promoting neural stem cell self-renewal and neurogenesis.

  13. ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal.

    Science.gov (United States)

    Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul

    2014-03-01

    Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.

  14. Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections

    Directory of Open Access Journals (Sweden)

    Leyre Azpilicueta

    2016-07-01

    Full Text Available With the growing demand of Intelligent Transportation Systems (ITS for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network.

  15. Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections.

    Science.gov (United States)

    Azpilicueta, Leyre; López-Iturri, Peio; Aguirre, Erik; Martínez, Carlos; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

    2016-07-22

    With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs) deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network.

  16. Signalling and detection of parallel triple layer wireless sensor networks with M-ary orthogonal modulation

    NARCIS (Netherlands)

    Mahboob, M.

    2012-01-01

    Recent advances in the underlying technologies of WSNs (Wireless Sensor Networks) have led to its use in different applications, in fields as diverse as battlefield applications, temperature control and healthcare. Research in the different aspects of WSNs is therefore in full swing, in both

  17. Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections

    Science.gov (United States)

    Azpilicueta, Leyre; López-Iturri, Peio; Aguirre, Erik; Martínez, Carlos; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

    2016-01-01

    With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs) deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network. PMID:27455270

  18. Subjective assessment of cochlear implant users' signal-to-noise ratio requirements for different levels of wireless device usability.

    Science.gov (United States)

    Julstrom, Stephen; Kozma-Spytek, Linda

    2014-01-01

    In order to better inform the development and revision of the American National Standards Institute C63.19 and American National Standards Institute/Telecommunications Industry Association-1083 hearing aid compatibility standards, a previous study examined the signal strength and signal (speech)-to-noise (interference) ratio needs of hearing aid users when using wireless and cordless phones in the telecoil coupling mode. This study expands that examination to cochlear implant (CI) users, in both telecoil and microphone modes of use. The purpose of this study was to evaluate the magnetic and acoustic signal levels needed by CI users for comfortable telephone communication and the users' tolerance relative to the speech levels of various interfering wireless communication-related noise types. Design was a descriptive and correlational study. Simulated telephone speech and eight interfering noise types presented as continuous signals were linearly combined and were presented together either acoustically or magnetically to the participants' CIs. The participants could adjust the loudness of the telephone speech and the interfering noises based on several assigned criteria. The 21 test participants ranged in age from 23-81 yr. All used wireless phones with their CIs, and 15 also used cordless phones at home. There were 12 participants who normally used the telecoil mode for telephone communication, whereas 9 used the implant's microphone; all were tested accordingly. A guided-intake questionnaire yielded general background information for each participant. A custom-built test control box fed by prepared speech-and-noise files enabled the tester or test participant, as appropriate, to switch between the various test signals and to precisely control the speech-and-noise levels independently. The tester, but not the test participant, could read and record the selected levels. Subsequent analysis revealed the preferred speech levels, speech (signal)-to-noise ratios, and the

  19. Processing of signals from an ion-elective electrode array by a neural network

    NARCIS (Netherlands)

    Bos, M.; Bos, A.; van der Linden, W.E.

    1990-01-01

    Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous

  20. Reward Motivation Accelerates the Onset of Neural Novelty Signals in Humans to 85 Milliseconds

    National Research Council Canada - National Science Library

    Bunzeck, Nico; Doeller, Christian F; Fuentemilla, Lluis; Dolan, Raymond J; Duzel, Emrah

    2009-01-01

    ... are rewarded [8] . In human recognition memory studies, on the other hand, reward is not used to motivate the detection of novel or familiar items. Remarkably, the possibility that the timing of neural novelty signals might be affected if the discrimination of novel and familiar items is rewarded has not yet been tested. Indeed, novelty proces...

  1. Neural cell adhesion molecule induces intracellular signaling via multiple mechanisms of Ca2+ homeostasis

    DEFF Research Database (Denmark)

    Kiryushko, Darya; Korshunova, Irina; Berezin, Vladimir

    2006-01-01

    The neural cell adhesion molecule (NCAM) plays a pivotal role in the development of the nervous system, promoting neuronal differentiation via homophilic (NCAM-NCAM) as well as heterophilic (NCAM-fibroblast growth factor receptor [FGFR]) interactions. NCAM-induced intracellular signaling has been...

  2. Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding.

    Science.gov (United States)

    Packard, Pau A; Rodríguez-Fornells, Antoni; Bunzeck, Nico; Nicolás, Berta; de Diego-Balaguer, Ruth; Fuentemilla, Lluís

    2017-01-11

    As the stream of experience unfolds, our memory system rapidly transforms current inputs into long-lasting meaningful memories. A putative neural mechanism that strongly influences how input elements are transformed into meaningful memory codes relies on the ability to integrate them with existing structures of knowledge or schemas. However, it is not yet clear whether schema-related integration neural mechanisms occur during online encoding. In the current investigation, we examined the encoding-dependent nature of this phenomenon in humans. We showed that actively integrating words with congruent semantic information provided by a category cue enhances memory for words and increases false recall. The memory effect of such active integration with congruent information was robust, even with an interference task occurring right after each encoding word list. In addition, via electroencephalography, we show in 2 separate studies that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. That the neural signals of successful encoding of congruent and incongruent information followed similarly ∼200 ms later suggests that this earlier neural response contributed to memory formation. We propose that the encoding of events that are congruent with readily available contextual semantics can trigger an accelerated onset of the neural mechanisms, supporting the integration of semantic information with the event input. This faster onset would result in a long-lasting and meaningful memory trace for the event but, at the same time, make it difficult to distinguish it from plausible but never encoded events (i.e., related false memories). Conceptual or schema congruence has a strong influence on long-term memory. However, the question of whether schema-related integration neural mechanisms occur during online encoding has yet to be clarified. We investigated the neural mechanisms reflecting how the active

  3. A four-channel microelectronic system for neural signal regeneration

    Energy Technology Data Exchange (ETDEWEB)

    Xie Shushan; Wang Zhigong; Li Wenyuan [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying; Pan Haixian, E-mail: zgwang@seu.edu.c [State Key Laboratory of Bio-Electronics, Southeast University, Nanjing 210096 (China)

    2009-12-15

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-g{sub m} RTR input and output OPA. The system has been realized in CSMC 0.5-{mu}m CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration. (semiconductor integrated circuits)

  4. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  5. FGF signalling regulates chromatin organisation during neural differentiation via mechanisms that can be uncoupled from transcription.

    Directory of Open Access Journals (Sweden)

    Nishal S Patel

    Full Text Available Changes in higher order chromatin organisation have been linked to transcriptional regulation; however, little is known about how such organisation alters during embryonic development or how it is regulated by extrinsic signals. Here we analyse changes in chromatin organisation as neural differentiation progresses, exploiting the clear spatial separation of the temporal events of differentiation along the elongating body axis of the mouse embryo. Combining fluorescence in situ hybridisation with super-resolution structured illumination microscopy, we show that chromatin around key differentiation gene loci Pax6 and Irx3 undergoes both decompaction and displacement towards the nuclear centre coincident with transcriptional onset. Conversely, down-regulation of Fgf8 as neural differentiation commences correlates with a more peripheral nuclear position of this locus. During normal neural differentiation, fibroblast growth factor (FGF signalling is repressed by retinoic acid, and this vitamin A derivative is further required for transcription of neural genes. We show here that exposure to retinoic acid or inhibition of FGF signalling promotes precocious decompaction and central nuclear positioning of differentiation gene loci. Using the Raldh2 mutant as a model for retinoid deficiency, we further find that such changes in higher order chromatin organisation are dependent on retinoid signalling. In this retinoid deficient condition, FGF signalling persists ectopically in the elongating body, and importantly, we find that inhibiting FGF receptor (FGFR signalling in Raldh2-/- embryos does not rescue differentiation gene transcription, but does elicit both chromatin decompaction and nuclear position change. These findings demonstrate that regulation of higher order chromatin organisation during differentiation in the embryo can be uncoupled from the machinery that promotes transcription and, for the first time, identify FGF as an extrinsic signal that

  6. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Astrocytic Calcium Waves Signal Brain Injury to Neural Stem and Progenitor Cells

    Directory of Open Access Journals (Sweden)

    Anna Kraft

    2017-03-01

    Full Text Available Brain injuries, such as stroke or trauma, induce neural stem cells in the subventricular zone (SVZ to a neurogenic response. Very little is known about the molecular cues that signal tissue damage, even over large distances, to the SVZ. Based on our analysis of gene expression patterns in the SVZ, 48 hr after an ischemic lesion caused by middle cerebral artery occlusion, we hypothesized that the presence of an injury might be transmitted by an astrocytic traveling calcium wave rather than by diffusible factors or hypoxia. Using a newly established in vitro system we show that calcium waves induced in an astrocytic monolayer spread to neural stem and progenitor cells and increase their self-renewal as well as migratory behavior. These changes are due to an upregulation of the Notch signaling pathway. This introduces the concept of propagating astrocytic calcium waves transmitting brain injury signals over long distances.

  8. Wireless Technician

    Science.gov (United States)

    Tech Directions, 2011

    2011-01-01

    One of the hottest areas in technology is invisible. Wireless communications allow people to transmit voice messages, data, and other signals through the air without physically connecting senders to receivers with cables or wires. And the technology is spreading at lightning speed. Cellular phones, personal digital assistants, and wireless…

  9. Effect of signal noise on the learning capability of an artificial neural network

    Science.gov (United States)

    Vega, J. J.; Reynoso, R.; Calvet, H. Carrillo

    2009-07-01

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  10. Effect of signal noise on the learning capability of an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Vega, J.J. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico)], E-mail: jjvc@nuclear.inin.mx; Reynoso, R. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico); Calvet, H. Carrillo [Laboratorio de Dinamica no Lineal, Facultad de Ciencias, Universidad Nacional Autonoma de Mexico, Mexico D.F. 04510 (Mexico)

    2009-07-21

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  11. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  12. Wireless Telemetry of In-Flight Collision Avoidance Neural Signals in Insects

    Science.gov (United States)

    2010-09-01

    EQUIPMENT USED TO  FILM  THE ANIMALS.  11  FIGURE 14.    STIMULI GENERATED ON THE REAR PROJECTION SCREEN.  12  FIGURE 15.    ANIMAL’S TRAJECTORY DURING A...the animals (Fig. 13). Figure 13. Side views of the wind tunnel and of the equipment used to film the animals. A. Left view (relative to the...Graziano, M., Andersen, R. and Snowden , R. (1994). Tuning of mst neurons to spiral motions. J. Neurosci. 14, 54– 67. Gu, Y., Angelaki, D.E. and Deangelis

  13. Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

    Directory of Open Access Journals (Sweden)

    Yang Xu

    2017-01-01

    Full Text Available Integrating wireless sensor network (WSN into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS, has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC-based extreme learning machine (ELM algorithm (ELM-RCC. Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square

  14. Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme.

    Science.gov (United States)

    Xu, Yang; Luo, Xiong; Wang, Weiping; Zhao, Wenbing

    2017-01-12

    Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE

  15. Radial glial neural progenitors regulate nascent brain vascular network stabilization via inhibition of Wnt signaling.

    Directory of Open Access Journals (Sweden)

    Shang Ma

    Full Text Available The cerebral cortex performs complex cognitive functions at the expense of tremendous energy consumption. Blood vessels in the brain are known to form stereotypic patterns that facilitate efficient oxygen and nutrient delivery. Yet little is known about how vessel development in the brain is normally regulated. Radial glial neural progenitors are well known for their central role in orchestrating brain neurogenesis. Here we show that, in the late embryonic cortex, radial glial neural progenitors also play a key role in brain angiogenesis, by interacting with nascent blood vessels and regulating vessel stabilization via modulation of canonical Wnt signaling. We find that ablation of radial glia results in vessel regression, concomitant with ectopic activation of Wnt signaling in endothelial cells. Direct activation of Wnt signaling also results in similar vessel regression, while attenuation of Wnt signaling substantially suppresses regression. Radial glial ablation and ectopic Wnt pathway activation leads to elevated endothelial expression of matrix metalloproteinases, while inhibition of metalloproteinase activity significantly suppresses vessel regression. These results thus reveal a previously unrecognized role of radial glial progenitors in stabilizing nascent brain vascular network and provide novel insights into the molecular cascades through which target neural tissues regulate vessel stabilization and patterning during development and throughout life.

  16. A low power multichannel analog front end for portable neural signal recordings.

    Science.gov (United States)

    Obeid, Iyad; Nicolelis, Miguel A L; Wolf, Patrick D

    2004-02-15

    We present the design and testing of a 16-channel analog amplifier for processing neural signals. Each channel has the following features: (1) variable gain (70-94 dB), (2) four high pass Bessel filter poles (f(-3 dB)=445 Hz), (3) five low pass Bessel filter poles (f(-3 dB)=6.6 kHz), and (4) differential amplification with a user selectable reference channel to reject common mode background biological noise. Processed signals are time division multiplexed and sampled by an on-board 12-bit analog to digital converter at up to 62.5k samples/s per channel. The board is powered by two low dropout voltage regulators which may be supplied by a single battery. The board measures 8.1 cm x 9.9 cm, weighs 50 g, and consumes up to 130 mW. Its low input-referred noise (1.0 microV(RMS)) makes it possible to process low amplitude neural signals; the board was successfully tested in vivo to process cortically derived extracellular action potentials in primates. Signals processed by this board were compared to those generated by a commercially available system and were found to be nearly identical. Background noise generated by mastication was substantially attenuated by the selectable reference circuit. The described circuit is light weight and low power and is used as a component of a wearable multichannel neural telemetry system.

  17. Global and local missions of cAMP signaling in neural plasticity, learning, and memory.

    Science.gov (United States)

    Lee, Daewoo

    2015-01-01

    The fruit fly Drosophila melanogaster has been a popular model to study cAMP signaling and resultant behaviors due to its powerful genetic approaches. All molecular components (AC, PDE, PKA, CREB, etc) essential for cAMP signaling have been identified in the fly. Among them, adenylyl cyclase (AC) gene rutabaga and phosphodiesterase (PDE) gene dunce have been intensively studied to understand the role of cAMP signaling. Interestingly, these two mutant genes were originally identified on the basis of associative learning deficits. This commentary summarizes findings on the role of cAMP in Drosophila neuronal excitability, synaptic plasticity and memory. It mainly focuses on two distinct mechanisms (global versus local) regulating excitatory and inhibitory synaptic plasticity related to cAMP homeostasis. This dual regulatory role of cAMP is to increase the strength of excitatory neural circuits on one hand, but to act locally on postsynaptic GABA receptors to decrease inhibitory synaptic plasticity on the other. Thus the action of cAMP could result in a global increase in the neural circuit excitability and memory. Implications of this cAMP signaling related to drug discovery for neural diseases are also described.

  18. Global Synchronization Measurement of Multivariate Neural Signals with Massively Parallel Nonlinear Interdependence Analysis.

    Science.gov (United States)

    Chen, Dan; Li, Xiaoli; Cui, Dong; Wang, Lizhe; Lu, Dongchuan

    2014-01-01

    The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of 1) measuring the direction and strength of synchronization of activities of multiple brain regions, and 2) adapting to the quickly increasing sizes and scales of neural signals. Nonlinear Interdependence (NLI) analysis is an effective method for measuring synchronization direction and strength of bivariate neural signal. However, the method currently does not directly apply in handling multivariate signal. Its application in practice has also long been largely hampered by the ultra-high complexity of NLI algorithms. Aiming at these problems, this study 1) extends the conventional NLI to quantify the global synchronization of multivariate neural signals, and 2) develops a parallelized NLI method with general-purpose computing on the graphics processing unit (GPGPU), namely, G-NLI. The approach performs synchronization measurement in a massively parallel manner. The G-NLI has improved the runtime performance by more than 1000 times comparing to the original sequential NLI. Meanwhile, the G-NLI was employed to analyze 10-channel local field potential (LFP) recordings from a patient suffering from temporal lobe epilepsy. The results demonstrate that the proposed G-NLI method can support real-time global synchronization measurement and it could be successful in localization of epileptic focus.

  19. Intrinsic lens potential of neural retina inhibited by Notch signaling as the cause of lens transdifferentiation.

    Science.gov (United States)

    Iida, Hideaki; Ishii, Yasuo; Kondoh, Hisato

    2017-01-15

    Embryonic neural retinas of avians produce lenses under spreading culture conditions. This phenomenon has been regarded as a paradigm of transdifferentiation due to the overt change in cell type. Here we elucidated the underlying mechanisms. Retina-to-lens transdifferentiation occurs in spreading cultures, suggesting that it is triggered by altered cell-cell interactions. Thus, we tested the involvement of Notch signaling based on its role in retinal neurogenesis. Starting from E8 retina, a small number of crystallin-expressing lens cells began to develop after 20 days in control spreading cultures. By contrast, addition of Notch signal inhibitors to cultures after day 2 strongly promoted lens development beginning at day 11, and a 10-fold increase in δ-crystallin expression level. After Notch signal inhibition, transcription factor genes that regulate the early stage of eye development, Prox1 and Pitx3, were sequentially activated. These observations indicate that the lens differentiation potential is intrinsic to the neural retina, and this potential is repressed by Notch signaling during normal embryogenesis. Therefore, Notch suppression leads to lens transdifferentiation by disinhibiting the neural retina-intrinsic program of lens development. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

    Science.gov (United States)

    Dasgupta, Hirak

    2016-12-01

    The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

  1. Ultra-low-power and robust digital-signal-processing hardware for implantable neural interface microsystems.

    Science.gov (United States)

    Narasimhan, S; Chiel, H J; Bhunia, S

    2011-04-01

    Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

  2. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  3. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

    Science.gov (United States)

    Sriraam, N

    2011-01-01

    A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  4. Wireless communication with chaos.

    Science.gov (United States)

    Ren, Hai-Peng; Baptista, Murilo S; Grebogi, Celso

    2013-05-03

    The modern world fully relies on wireless communication. Because of intrinsic physical constraints of the wireless physical media (multipath, damping, and filtering), signals carrying information are strongly modified, preventing information from being transmitted with a high bit rate. We show that, though a chaotic signal is strongly modified by the wireless physical media, its Lyapunov exponents remain unaltered, suggesting that the information transmitted is not modified by the channel. For some particular chaotic signals, we have indeed proved that the dynamic description of both the transmitted and the received signals is identical and shown that the capacity of the chaos-based wireless channel is unaffected by the multipath propagation of the physical media. These physical properties of chaotic signals warrant an effective chaos-based wireless communication system.

  5. A 128-ch Δ-Σ ADC based mixed signal IC for full digital beamforming Wireless handheld Ultrasound imaging system.

    Science.gov (United States)

    Chirala, Mohan K; Phuong Huynh; Jaeyoung Ryu; Young-Hwan Kim

    2015-08-01

    This paper reports a massively integrated Δ-Σ ADC based mixed signal chipset for a handheld Wireless Ultrasound imaging system. The IC has been fabricated in a standard 0.13 μm 1.5V 7M2F CMOS process with 128 parallel channels containing Delta-Sigma (Δ-Σ) ADCs, Anti-aliasing filter, Decimation filters, Serializers and LVDS drivers. The entire chip is SPI controlled and allows group-level power control through an FPGA. The IC measures 15 × 15 mm and dissipates around ~ 4.6 W of power, with 12-bit resolution at 20 Msps sample rate. The chip was packaged in a thermally stable BGA package and demonstrated in a handheld ultrasound battery operated system with complete digital beamforming.

  6. Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels.

    Science.gov (United States)

    Hardalaç, Firat

    2008-04-01

    Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.

  7. Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals.

    Science.gov (United States)

    Manivannan, R; Samidurai, R; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-03-01

    This paper investigates the problems of exponential stability and dissipativity of generalized neural networks (GNNs) with time-varying delay signals. By constructing a novel Lyapunov-Krasovskii functionals (LKFs) with triple integral terms that contain more advantages of the state vectors of the neural networks, and the upper bound on the time-varying delay signals are formulated. We employ a new integral inequality technique (IIT), free-matrix-based (FMB) integral inequality approach, and Wirtinger double integral inequality (WDII) technique together with the reciprocally convex combination (RCC) approach to bound the time derivative of the LKFs. An improved exponential stability and strictly (Q,S,R)-γ-dissipative conditions of the addressed systems are represented by the linear matrix inequalities (LMIs). Finally, four interesting numerical examples are developed to verify the usefulness of the proposed method with a practical application to a biological network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Defective ALK5 signaling in the neural crest leads to increased postmigratory neural crest cell apoptosis and severe outflow tract defects

    Directory of Open Access Journals (Sweden)

    Sucov Henry M

    2006-11-01

    Full Text Available Abstract Background Congenital cardiovascular diseases are the most common form of birth defects in humans. A substantial portion of these defects has been associated with inappropriate induction, migration, differentiation and patterning of pluripotent cardiac neural crest stem cells. While TGF-β-superfamily signaling has been strongly implicated in neural crest cell development, the detailed molecular signaling mechanisms in vivo are still poorly understood. Results We deleted the TGF-β type I receptor Alk5 specifically in the mouse neural crest cell lineage. Failure in signaling via ALK5 leads to severe cardiovascular and pharyngeal defects, including inappropriate remodeling of pharyngeal arch arteries, abnormal aortic sac development, failure in pharyngeal organ migration and persistent truncus arteriosus. While ALK5 is not required for neural crest cell migration, our results demonstrate that it plays an important role in the survival of post-migratory cardiac neural crest cells. Conclusion Our results demonstrate that ALK5-mediated signaling in neural crest cells plays an essential cell-autonomous role in the pharyngeal and cardiac outflow tract development.

  9. Signalling through the Type 1 Insulin-Like Growth Factor Receptor (IGF1R Interacts with Canonical Wnt Signalling to Promote Neural Proliferation in Developing Brain

    Directory of Open Access Journals (Sweden)

    Qichen Hu

    2012-05-01

    Full Text Available Signalling through the IGF1R [type 1 IGF (insulin-like growth factor receptor] and canonical Wnt signalling are two signalling pathways that play critical roles in regulating neural cell generation and growth. To determine whether the signalling through the IGF1R can interact with the canonical Wnt signalling pathway in neural cells in vivo, we studied mutant mice with altered IGF signalling. We found that in mice with blunted IGF1R expression specifically in nestin-expressing neural cells (IGF1RNestin–KO mice the abundance of neural β-catenin was significantly reduced. Blunting IGF1R expression also markedly decreased: (i the activity of a LacZ (β-galactosidase reporter transgene that responds to Wnt nuclear signalling (LacZTCF reporter transgene and (ii the number of proliferating neural precursors. In contrast, overexpressing IGF-I (insulin-like growth factor I in brain markedly increased the activity of the LacZTCF reporter transgene. Consistently, IGF-I treatment also markedly increased the activity of the LacZTCF reporter transgene in embryonic neuron cultures that are derived from LacZTCF Tg (transgenic mice. Importantly, increasing the abundance of β-catenin in IGF1RNestin–KO embryonic brains by suppressing the activity of GSK3β (glycogen synthase kinase-3β significantly alleviated the phenotypic changes induced by IGF1R deficiency. These phenotypic changes includes: (i retarded brain growth, (ii reduced precursor proliferation and (iii decreased neuronal number. Our current data, consistent with our previous study of cultured oligodendrocytes, strongly support the concept that IGF signalling interacts with canonical Wnt signalling in the developing brain to promote neural proliferation. The interaction of IGF and canonical Wnt signalling plays an important role in normal brain development by promoting neural precursor proliferation.

  10. Reconfigurable embedded system architecture for next-generation Neural Signal Processing.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2010-01-01

    This work presents a new architectural framework for next generation Neural Signal Processing (NSP). The essential features of the NSP hardware platform include scalability, reconfigurability, real-time processing ability and data storage. This proposed framework has been implemented in a proof-of-concept NSP prototype using an embedded system architecture synthesized in a Xilinx(®)Virtex(®)5 development board. The prototype includes a threshold-based spike detector and a fuzzy logic-based spike sorter.

  11. Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Elham Ghoochani

    2011-03-01

    Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

  12. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  13. Genetic algorithm for the optimization of features and neural networks in ECG signals classification.

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-31

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  14. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  15. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  16. RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

    Science.gov (United States)

    Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

    2008-10-01

    A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

  17. Cancellation of artifacts in ECG signals using a normalized adaptive neural filter.

    Science.gov (United States)

    Wu, Yunfeng; Rangayyan, Rangaraj M; Ng, Sin-Chun

    2007-01-01

    Denoising electrocardiographic (ECG) signals is an essential procedure prior to their analysis. In this paper, we present a normalized adaptive neural filter (NANF) for cancellation of artifacts in ECG signals. The normalized filter coefficients are updated by the steepest-descent algorithm; the adaptation process is designed to minimize the difference between second-order estimated output values and the desired artifact-free ECG signals. Empirical results with benchmark data show that the adaptive artifact canceller that includes the NANF can effectively remove muscle-contraction artifacts and high-frequency noise in ambulatory ECG recordings, leading to a high signal-to-noise ratio. Moreover, the performance of the NANF in terms of the root-mean-squared error, normalized correlation coefficient, and filtered artifact entropy is significantly better than that of the popular least-mean-square (LMS) filter.

  18. Embryonic requirements for ErbB signaling in neural crest development and adult pigment pattern formation

    Science.gov (United States)

    Budi, Erine H.; Patterson, Larissa B.; Parichy, David M.

    2009-01-01

    SUMMARY Vertebrate pigment cells are derived from neural crest cells and are a useful system for studying neural crest-derived traits during post-embryonic development. In zebrafish, neural crest-derived melanophores differentiate during embryogenesis to produce stripes in the early larva. Dramatic changes to the pigment pattern occur subsequently during the larva-to-adult transformation, or metamorphosis. At this time, embryonic melanophores are replaced by newly differentiating metamorphic melanophores that form the adult stripes. Mutants with normal embryonic/early larval pigment patterns but defective adult patterns identify factors required uniquely to establish, maintain, or recruit the latent precursors to metamorphic melanophores. We show that one such mutant, picasso, lacks most metamorphic melanophores and results from mutations in the ErbB gene erbb3b, encoding an EGFR-like receptor tyrosine kinase. To identify critical periods for ErbB activities, we treated fish with pharmacological ErbB inhibitors and also knocked-down erbb3b by morpholino injection. These analyses reveal an embryonic critical period for ErbB signaling in promoting later pigment pattern metamorphosis, despite the normal patterning of embryonic/early larval melanophores. We further demonstrate a peak requirement during neural crest migration that correlates with early defects in neural crest pathfinding and peripheral ganglion formation. Finally, we show that erbb3b activities are both autonomous and non-autonomous to the metamorphic melanophore lineage. These data identify a very early, embryonic, requirement for erbb3b in the development of much later metamorphic melanophores, and suggest complex modes by which ErbB signals promote adult pigment pattern development. PMID:18508863

  19. Securing OFDM over Wireless Time-Varying Channels Using Subcarrier Overloading with Joint Signal Constellations

    Directory of Open Access Journals (Sweden)

    Gill R. Tsouri

    2009-01-01

    Full Text Available A method of overloading subcarriers by multiple transmitters to secure OFDM in wireless time-varying channels is proposed and analyzed. The method is based on reverse piloting, superposition modulation, and joint decoding. It makes use of channel randomness, reciprocity, and fast decorrelation in space to secure OFDM with low overheads on encryption, decryption, and key distribution. These properties make it a good alternative to traditional software-based information security algorithms in systems where the costs associated with such algorithms are an implementation obstacle. A necessary and sufficient condition for achieving information theoretic security in accordance with channel and system parameters is derived. Security by complexity is assessed for cases where the condition for information theoretic security is not satisfied. In addition, practical means for implementing the method are derived including generating robust joint constellations, decoding data with low complexity, and mitigating the effects of imperfections due to mobility, power control errors, and synchronization errors.

  20. Optical Switching for Dynamic Distribution of Wireless-Over-Fiber Signals in Active Optical Networks

    DEFF Research Database (Denmark)

    Vegas Olmos, Juan José; Rodes, Guillermo; Tafur Monroy, Idelfonso

    2012-01-01

    In this paper, we report on an experimental validation of dynamic distribution of wireless-over-fiber by employing optical switching using semiconductor optical amplifiers; we also provide a channel distribution scheme and a generic topology for such an optical switch. The experiment consists...... of a four wavelength-division-multiplexed channel system operating on a WiMax frequency band and employing an orthogonal-frequency-division-multiplexing modulation at 625 Mbits/s per channel, transmission of the data over 20 km of optical fiber, and active switching in a 1 × 16 active optical switch....... The results show a negligible power penalty on each channel for both the best and the worst case in terms of inter-channel crosstalk. The presented system is highly scalable both in terms of port count and throughput, a desirable feature in highly branched access networks, and is modulation- and frequency...

  1. Neural plasticity in the gastrointestinal tract: chronic inflammation, neurotrophic signals, and hypersensitivity.

    Science.gov (United States)

    Demir, Ihsan Ekin; Schäfer, Karl-Herbert; Tieftrunk, Elke; Friess, Helmut; Ceyhan, Güralp O

    2013-04-01

    Neural plasticity is not only the adaptive response of the central nervous system to learning, structural damage or sensory deprivation, but also an increasingly recognized common feature of the gastrointestinal (GI) nervous system during pathological states. Indeed, nearly all chronic GI disorders exhibit a disease-stage-dependent, structural and functional neuroplasticity. At structural level, GI neuroplasticity usually comprises local tissue hyperinnervation (neural sprouting, neural, and ganglionic hypertrophy) next to hypoinnervated areas, a switch in the neurochemical (neurotransmitter/neuropeptide) code toward preferential expression of neuropeptides which are frequently present in nociceptive neurons (e.g., substance P/SP, calcitonin-gene-related-peptide/CGRP) and of ion channels (TRPV1, TRPA1, PAR2), and concomitant activation of peripheral neural glia. The functional counterpart of these structural alterations is altered neuronal electric activity, leading to organ dysfunction (e.g., impaired motility and secretion), together with reduced sensory thresholds, resulting in hypersensitivity and pain. The present review underlines that neural plasticity in all GI organs, starting from esophagus, stomach, small and large intestine to liver, gallbladder, and pancreas, actually exhibits common phenotypes and mechanisms. Careful appraisal of these GI neuroplastic alterations reveals that--no matter which etiology, i.e., inflammatory, infectious, neoplastic/malignant, or degenerative--neural plasticity in the GI tract primarily occurs in the presence of chronic tissue- and neuro-inflammation. It seems that studying the abundant trophic and activating signals which are generated during this neuro-immune-crosstalk represents the key to understand the remarkable neuroplasticity of the GI tract.

  2. Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

    Science.gov (United States)

    Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

    2013-06-01

    Objective. Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results. 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional ‘bias’ in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions

  3. Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network

    Science.gov (United States)

    Sun, W. Z.; Jiang, M. Y.; Ren, L.; Dang, J.; You, T.; Yin, F.-F.

    2017-09-01

    To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.

  4. Social discounting involves modulation of neural value signals by temporoparietal junction

    Science.gov (United States)

    Strombach, Tina; Weber, Bernd; Hangebrauk, Zsofia; Kenning, Peter; Karipidis, Iliana I.; Tobler, Philippe N.; Kalenscher, Tobias

    2015-01-01

    Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation. PMID:25605887

  5. Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Li Xiumin; Small, Michael, E-mail: ensmall@polyu.edu.h, E-mail: 07901216r@eie.polyu.edu.h [Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)

    2010-08-15

    Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

  6. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  7. Altered neural reward and loss processing and prediction error signalling in depression

    Science.gov (United States)

    Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela

    2015-01-01

    Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763

  8. Integration of Signals along Orthogonal Axes of the Vertebrate Neural Tube Controls Progenitor Competence and Increases Cell Diversity

    Science.gov (United States)

    Sasai, Noriaki; Kutejova, Eva; Briscoe, James

    2014-01-01

    A relatively small number of signals are responsible for the variety and pattern of cell types generated in developing embryos. In part this is achieved by exploiting differences in the concentration or duration of signaling to increase cellular diversity. In addition, however, changes in cellular competence—temporal shifts in the response of cells to a signal—contribute to the array of cell types generated. Here we investigate how these two mechanisms are combined in the vertebrate neural tube to increase the range of cell types and deliver spatial control over their location. We provide evidence that FGF signaling emanating from the posterior of the embryo controls a change in competence of neural progenitors to Shh and BMP, the two morphogens that are responsible for patterning the ventral and dorsal regions of the neural tube, respectively. Newly generated neural progenitors are exposed to FGF signaling, and this maintains the expression of the Nk1-class transcription factor Nkx1.2. Ventrally, this acts in combination with the Shh-induced transcription factor FoxA2 to specify floor plate cells and dorsally in combination with BMP signaling to induce neural crest cells. As development progresses, the intersection of FGF with BMP and Shh signals is interrupted by axis elongation, resulting in the loss of Nkx1.2 expression and allowing the induction of ventral and dorsal interneuron progenitors by Shh and BMP signaling to supervene. Hence a similar mechanism increases cell type diversity at both dorsal and ventral poles of the neural tube. Together these data reveal that tissue morphogenesis produces changes in the coincidence of signals acting along orthogonal axes of the neural tube and this is used to define spatial and temporal transitions in the competence of cells to interpret morphogen signaling. PMID:25026549

  9. Simple Adaptive Single Differential Coherence Detection of BPSK Signals in IEEE 802.15.4 Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Gaoyuan; Wen, Hong; Wang, Longye; Xie, Ping; Song, Liang; Tang, Jie; Liao, Runfa

    2017-12-26

    In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation sin - 1 ( x ) ≈ x and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector.

  10. Feature reconstruction of LFP signals based on PLSR in the neural information decoding study.

    Science.gov (United States)

    Yonghui Dong; Zhigang Shang; Mengmeng Li; Xinyu Liu; Hong Wan

    2017-07-01

    To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

  11. Employment and comparison of different Artificial Neural Networks for epilepsy diagnosis from EEG signals.

    Science.gov (United States)

    Sezer, Esma; Işik, Hakan; Saracoğlu, Esra

    2012-02-01

    In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals' automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.

  12. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  13. Neural crest-derived mesenchymal cells require Wnt signaling for their development and drive invagination of the telencephalic midline.

    Directory of Open Access Journals (Sweden)

    Youngshik Choe

    Full Text Available Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis.

  14. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  15. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  16. Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.

    Science.gov (United States)

    Das, A; Reddy, N P; Narayanan, J

    2001-02-01

    Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.

  17. Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

    Science.gov (United States)

    Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

    2015-01-01

    Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. PMID:25972586

  18. Larger Neural Responses Produce BOLD Signals That Begin Earlier in Time

    Directory of Open Access Journals (Sweden)

    Serena eThompson

    2014-06-01

    Full Text Available Functional MRI analyses commonly rely on the assumption that the temporal dynamics of hemodynamic response functions (HRFs are independent of the amplitude of the neural signals that give rise to them. The validity of this assumption is particularly important for techniques that use fMRI to resolve sub-second timing distinctions between responses, in order to make inferences about the ordering of neural processes. Whether or not the detailed shape of the HRF is independent of neural response amplitude remains an open question, however. We performed experiments in which we measured responses in primary visual cortex (V1 to large, contrast-reversing checkerboards at a range of contrast levels, which should produce varying amounts of neural activity. Ten subjects (ages 22-52 were studied in each of two experiments using 3 Tesla scanners. We used rapid, 250 msec, temporal sampling (repetition time, or TR and both short and long inter-stimulus interval (ISI stimulus presentations. We tested for a systematic relationship between the onset of the HRF and its amplitude across conditions, and found a strong negative correlation between the two measures when stimuli were separated in time (long- and medium-ISI experiments, but not the short-ISI experiment. Thus, stimuli that produce larger neural responses, as indexed by HRF amplitude, also produced HRFs with shorter onsets. The relationship between amplitude and latency was strongest in voxels with lowest mean-normalized variance (i.e., parenchymal voxels. The onset differences observed in the longer-ISI experiments are likely attributable to mechanisms of neurovascular coupling, since they are substantially larger than reported differences in the onset of action potentials in V1 as a function of response amplitude.

  19. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    OpenAIRE

    Sadik Kamel Gharghan; Rosdiadee Nordin; Mahamod Ismail

    2016-01-01

    In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the...

  20. Eliminating sun glare disturbance at signalized intersections by a vehicle to infrastructure wireless communication : final report.

    Science.gov (United States)

    2016-05-01

    Due to sun glare disturbances, drivers may encounter fatal threats on roadways, particularly at signalized intersections. Many studies have attempted to develop applicable solutions, such as avoiding sun positions, road geometric redirections, and we...

  1. Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect.

    Science.gov (United States)

    Kostarakos, Konstantinos; Römer, Heiner

    2015-07-22

    produces an extremely noisy sound, yet the second species still detects its own song. Using intracellular recording techniques we identified two neural mechanisms underlying the surprising behavioral signal detection at the level of single identified interneurons. These neural mechanisms for signal detection are likely to be important for other sensory modalities as well, where noise in the communication channel creates similar problems. Also, they may be used for the development of algorithms for the filtering of specific signals in technical microphones or hearing aids. Copyright © 2015 Kostarakos and Römer.

  2. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  3. Astrocytic Calcium Waves Signal Brain Injury to Neural Stem and Progenitor Cells.

    Science.gov (United States)

    Kraft, Anna; Jubal, Eduardo Rosales; von Laer, Ruth; Döring, Claudia; Rocha, Adriana; Grebbin, Moyo; Zenke, Martin; Kettenmann, Helmut; Stroh, Albrecht; Momma, Stefan

    2017-03-14

    Brain injuries, such as stroke or trauma, induce neural stem cells in the subventricular zone (SVZ) to a neurogenic response. Very little is known about the molecular cues that signal tissue damage, even over large distances, to the SVZ. Based on our analysis of gene expression patterns in the SVZ, 48 hr after an ischemic lesion caused by middle cerebral artery occlusion, we hypothesized that the presence of an injury might be transmitted by an astrocytic traveling calcium wave rather than by diffusible factors or hypoxia. Using a newly established in vitro system we show that calcium waves induced in an astrocytic monolayer spread to neural stem and progenitor cells and increase their self-renewal as well as migratory behavior. These changes are due to an upregulation of the Notch signaling pathway. This introduces the concept of propagating astrocytic calcium waves transmitting brain injury signals over long distances. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  4. Investigations of Escherichia coli promoter sequences with artificial neural networks: new signals discovered upstream of the transcriptional startpoint

    DEFF Research Database (Denmark)

    Pedersen, Anders Gorm; Engelbrecht, Jacob

    1995-01-01

    We present a novel method for using the learning ability of a neural network as a measure of information in local regions of input data. Using the method to analyze Escherichia coli promoters, we discover all previously described signals, and furthermore find new signals that are regularly spaced...

  5. Real-time neural signals of perceptual priming with unfamiliar geometric shapes.

    Science.gov (United States)

    Voss, Joel L; Paller, Ken A

    2010-07-07

    Perceptual priming is a type of item-specific implicit memory that is distinct from explicit memory. Neural signals of the processing responsible for perceptual priming can be difficult to isolate due to concurrent conceptual processing and explicit recognition. We successfully identified neural correlates of perceptual priming by using minimally meaningful, difficult-to-recognize, kaleidoscope images. Human participants were required to quickly indicate the number of colors present in each stimulus, and priming was shown by faster and more accurate visual discriminations for repeated compared with initial presentations. Electroencephalographic responses linked with this differential perceptual fluency were identified as negative potentials 100-300 ms poststimulus onset. Furthermore, different potentials recorded during initial presentations were indicative of perceptual learning, in that their amplitude predicted the magnitude of later priming. These electrophysiological findings show that the degree of perceptual learning engaged upon first encountering a novel visual stimulus predicts the degree of perceptual fluency experienced when the stimulus is processed a second time. It is thus possible to isolate multiple neural processing stages relevant to perceptual priming by using real-time measures of relevant neurophysiological activity in conjunction with experimental circumstances that limit the contaminating influences of other neurocognitive events.

  6. High-speed ultra-wideband wireless signals over fiber systems: photonic generation and DSP detection

    DEFF Research Database (Denmark)

    Yu, Xianbin; Gibbon, Timothy Braidwood; Tafur Monroy, Idelfonso

    2009-01-01

    We firstly review the efforts in the literature on ultra-wideband (UWB)-over-fiber systems. Secondly, we present experimental results on photonic generation of high-speed UWB signals by both direct modulation and external optical injecting an uncooled semiconductor laser. Furthermore, we introduce...

  7. Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

    Science.gov (United States)

    Smith, Aaron; Evans, Michael; Downey, Joseph

    2017-01-01

    National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

  8. Encoding physiological signals as images for affective state recognition using convolutional neural networks.

    Science.gov (United States)

    Guangliang Yu; Xiang Li; Dawei Song; Xiaozhao Zhao; Peng Zhang; Yuexian Hou; Bin Hu

    2016-08-01

    Affective state recognition based on multiple modalities of physiological signals has been a hot research topic. Traditional methods require designing hand-crafted features based on domain knowledge, which is time-consuming and has not achieved a satisfactory performance. On the other hand, conducting classification on raw signals directly can also cause some problems, such as the interference of noise and the curse of dimensionality. To address these problems, we propose a novel approach that encodes different modalities of data as images and use convolutional neural networks (CNN) to perform the affective state recognition task. We validate our aproach on the DECAF dataset in comparison with two state-of-the-art methods, i.e., the Support Vector Machines (SVM) and Random Forest (RF). Experimental results show that our aproach outperforms the baselines by 5% to 9%.

  9. Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

    Science.gov (United States)

    Curilem, Gloria; Vergara, Jorge; Fuentealba, Gustavo; Acuña, Gonzalo; Chacón, Max

    2009-02-01

    Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The

  10. Rabconnectin-3a regulates vesicle endocytosis and canonical Wnt signaling in zebrafish neural crest migration.

    Directory of Open Access Journals (Sweden)

    Adam M Tuttle

    2014-05-01

    Full Text Available Cell migration requires dynamic regulation of cell-cell signaling and cell adhesion. Both of these processes involve endocytosis, lysosomal degradation, and recycling of ligand-receptor complexes and cell adhesion molecules from the plasma membrane. Neural crest (NC cells in vertebrates are highly migratory cells, which undergo an epithelial-mesenchymal transition (EMT to leave the neural epithelium and migrate throughout the body to give rise to many different derivatives. Here we show that the v-ATPase interacting protein, Rabconnectin-3a (Rbc3a, controls intracellular trafficking events and Wnt signaling during NC migration. In zebrafish embryos deficient in Rbc3a, or its associated v-ATPase subunit Atp6v0a1, many NC cells fail to migrate and misregulate expression of cadherins. Surprisingly, endosomes in Rbc3a- and Atp6v0a1-deficient NC cells remain immature but still acidify. Rbc3a loss-of-function initially downregulates several canonical Wnt targets involved in EMT, but later Frizzled-7 accumulates at NC cell membranes, and nuclear B-catenin levels increase. Presumably due to this later Wnt signaling increase, Rbc3a-deficient NC cells that fail to migrate become pigment progenitors. We propose that Rbc3a and Atp6v0a1 promote endosomal maturation to coordinate Wnt signaling and intracellular trafficking of Wnt receptors and cadherins required for NC migration and cell fate determination. Our results suggest that different v-ATPases and associated proteins may play cell-type-specific functions in intracellular trafficking in many contexts.

  11. The PennBMBI: Design of a General Purpose Wireless Brain-Machine-Brain Interface System.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Subei, Basheer; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2015-04-01

    In this paper, a general purpose wireless Brain-Machine-Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz. Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage up to ± 12 V is included. The stimulator is capable of delivering unipolar or bipolar, charge-balanced current pulses with programmable pulse shape, amplitude, width, pulse train frequency and latency. A multi-functional sensor node, including an accelerometer, a temperature sensor, a flexiforce sensor and a general sensor extension port has been designed. A computer interface is designed to monitor, control and configure all aforementioned devices via a wireless link, according to a custom designed communication protocol. Wireless closed-loop operation between the sensory devices, neural stimulator, and neural signal analyzer can be configured. The proposed system was designed to link two sites in the brain, bridging the brain and external hardware, as well as creating new sensory and motor pathways for clinical practice. Bench test and in vivo experiments are performed to verify the functions and performances of the system.

  12. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  13. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  14. Underwater wireless optical communication using an arrayed transmitter/receiver and optical superimposition-based PAM-4 signal.

    Science.gov (United States)

    Kong, Meiwei; Chen, Yifei; Sarwar, Rohail; Sun, Bin; Xu, Zhiwei; Han, Jun; Chen, Jiawang; Qin, Huawei; Xu, Jing

    2018-02-05

    In this work, we propose an underwater wireless optical communication (UWOC) system using an arrayed transmitter/receiver and optical superimposition-based pulse amplitude modulation with 4 levels (PAM-4). At the transmitter side, we design a spatial summing scheme using a light emitting diode (LED) array, which is divided into two groups in a uniformly interleaved manner. With on-off keying (OOK) modulation for each group, optical superimposition-based PAM-4 can be realized. It has enhanced tolerance to the modulation nonlinearities of LEDs. We numerically investigate the feasibility of the proposed spatial summing scheme in various underwater channels via Monte Carlo simulation. With the increase of divergence angle of LEDs and link distance, the optical power distribution tends to be more uniform at the reception plane. It can significantly relax the requirement on the link alignment. Furthermore, we conduct a proof-of-concept experiment employing two blue LEDs. A multi-pixel photon counter (MPPC), containing an array of single-photon avalanche diodes (SPADs), is used as the detector. It has a much higher sensitivity and can further relax the requirement for pointing. Over a 2-m tap water channel, data rates of 6.144 Mb/s, 8.192 Mb/s, and 12.288 Mb/s were achieved by using the PAM-4 signal generated by optical superimposition, within a 2.5-MHz system bandwidth. With 0.570-mg/L Mg(OH) 2 , the measured optical power is just 12.890 µW after a 2-m underwater channel. The corresponding bit error rate (BER) of the 12.288-Mbs PAM-4 signal is 2.9 × 10 -3 , which is still below the forward error correction (FEC) limit of 3.8 × 10 -3 . It implies that the UWOC system based on the high-sensitivity MPPC with array structure has superior power efficiency and robustness.

  15. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of

  16. Compound developmental eye disorders following inactivation of TGFβ signaling in neural-crest stem cells

    Directory of Open Access Journals (Sweden)

    Suter Ueli

    2005-12-01

    Full Text Available Abstract Background Development of the eye depends partly on the periocular mesenchyme derived from the neural crest (NC, but the fate of NC cells in mammalian eye development and the signals coordinating the formation of ocular structures are poorly understood. Results Here we reveal distinct NC contributions to both anterior and posterior mesenchymal eye structures and show that TGFβ signaling in these cells is crucial for normal eye development. In the anterior eye, TGFβ2 released from the lens is required for the expression of transcription factors Pitx2 and Foxc1 in the NC-derived cornea and in the chamber-angle structures of the eye that control intraocular pressure. TGFβ enhances Foxc1 and induces Pitx2 expression in cell cultures. As in patients carrying mutations in PITX2 and FOXC1, TGFβ signal inactivation in NC cells leads to ocular defects characteristic of the human disorder Axenfeld-Rieger's anomaly. In the posterior eye, NC cell-specific inactivation of TGFβ signaling results in a condition reminiscent of the human disorder persistent hyperplastic primary vitreous. As a secondary effect, retinal patterning is also disturbed in mutant mice. Conclusion In the developing eye the lens acts as a TGFβ signaling center that controls the development of eye structures derived from the NC. Defective TGFβ signal transduction interferes with NC-cell differentiation and survival anterior to the lens and with normal tissue morphogenesis and patterning posterior to the lens. The similarity to developmental eye disorders in humans suggests that defective TGFβ signal modulation in ocular NC derivatives contributes to the pathophysiology of these diseases.

  17. A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Engelbrecht, Jacob; Brunak, Søren

    1997-01-01

    We have developed a new method for the identication of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs signicantly better than previous prediction schemes, and can easily be applied to genome......-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision....

  18. USP9X deubiquitylating enzyme maintains RAPTOR protein levels, mTORC1 signalling and proliferation in neural progenitors.

    Science.gov (United States)

    Bridges, Caitlin R; Tan, Men-Chee; Premarathne, Susitha; Nanayakkara, Devathri; Bellette, Bernadette; Zencak, Dusan; Domingo, Deepti; Gecz, Jozef; Murtaza, Mariyam; Jolly, Lachlan A; Wood, Stephen A

    2017-03-24

    USP9X, is highly expressed in neural progenitors and, essential for neural development in mice. In humans, mutations in USP9X are associated with neurodevelopmental disorders. To understand USP9X's role in neural progenitors, we studied the effects of altering its expression in both the human neural progenitor cell line, ReNcell VM, as well as neural stem and progenitor cells derived from Nestin-cre conditionally deleted Usp9x mice. Decreasing USP9X resulted in ReNcell VM cells arresting in G0 cell cycle phase, with a concomitant decrease in mTORC1 signalling, a major regulator of G0/G1 cell cycle progression. Decreased mTORC1 signalling was also observed in Usp9x-null neurospheres and embryonic mouse brains. Further analyses revealed, (i) the canonical mTORC1 protein, RAPTOR, physically associates with Usp9x in embryonic brains, (ii) RAPTOR protein level is directly proportional to USP9X, in both loss- and gain-of-function experiments in cultured cells and, (iii) USP9X deubiquitlyating activity opposes the proteasomal degradation of RAPTOR. EdU incorporation assays confirmed Usp9x maintains the proliferation of neural progenitors similar to Raptor-null and rapamycin-treated neurospheres. Interestingly, loss of Usp9x increased the number of sphere-forming cells consistent with enhanced neural stem cell self-renewal. To our knowledge, USP9X is the first deubiquitylating enzyme shown to stabilize RAPTOR.

  19. Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals.

    Science.gov (United States)

    Ubeyli, Elif Derya

    2008-11-01

    A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and the PNN. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the EEG signals and the PNN trained on these features achieved high classification accuracies.

  20. SMAD4-mediated WNT signaling controls the fate of cranial neural crest cells during tooth morphogenesis

    Science.gov (United States)

    Li, Jingyuan; Huang, Xiaofeng; Xu, Xun; Mayo, Julie; Bringas, Pablo; Jiang, Rulang; Wang, Songling; Chai, Yang

    2011-01-01

    TGFβ/BMP signaling regulates the fate of multipotential cranial neural crest (CNC) cells during tooth and jawbone formation as these cells differentiate into odontoblasts and osteoblasts, respectively. The functional significance of SMAD4, the common mediator of TGFβ/BMP signaling, in regulating the fate of CNC cells remains unclear. In this study, we investigated the mechanism of SMAD4 in regulating the fate of CNC-derived dental mesenchymal cells through tissue-specific inactivation of Smad4. Ablation of Smad4 results in defects in odontoblast differentiation and dentin formation. Moreover, ectopic bone-like structures replaced normal dentin in the teeth of Osr2-IresCre;Smad4fl/fl mice. Despite the lack of dentin, enamel formation appeared unaffected in Osr2-IresCre;Smad4fl/fl mice, challenging the paradigm that the initiation of enamel development depends on normal dentin formation. At the molecular level, loss of Smad4 results in downregulation of the WNT pathway inhibitors Dkk1 and Sfrp1 and in the upregulation of canonical WNT signaling, including increased β-catenin activity. More importantly, inhibition of the upregulated canonical WNT pathway in Osr2-IresCre;Smad4fl/fl dental mesenchyme in vitro partially rescued the CNC cell fate change. Taken together, our study demonstrates that SMAD4 plays a crucial role in regulating the interplay between TGFβ/BMP and WNT signaling to ensure the proper CNC cell fate decision during organogenesis. PMID:21490069

  1. Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

    Science.gov (United States)

    Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

    2016-09-01

    The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

  2. Exploiting Wireless Received Signal Strength Indicators to Detect Evil-Twin Attacks in Smart Homes

    Directory of Open Access Journals (Sweden)

    Zhanyong Tang

    2017-01-01

    Full Text Available Evil-Twin is becoming a common attack in smart home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-Twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP, or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel Evil-Twin attack detection method based on the received signal strength indicator (RSSI. Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the Wi-Fi signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of a one-off, modest connection delay.

  3. Novel Mutation of LRP6 Identified in Chinese Han Population Links Canonical WNT Signaling to Neural Tube Defects.

    Science.gov (United States)

    Shi, Zhiwen; Yang, Xueyan; Li, Bin-Bin; Chen, Shuxia; Yang, Luming; Cheng, Liangping; Zhang, Ting; Wang, Hongyan; Zheng, Yufang

    2017-09-29

    Neural tube defects (NTDs), the second most frequent cause of human congenital abnormalities, are debilitating birth defects due to failure of neural tube closure. It has been shown that noncanonical WNT/planar cell polarity (PCP) signaling is required for convergent extension (CE), the initiation step of neural tube closure (NTC). But the effect of canonical WNT//β-catenin signaling during NTC is still elusive. LRP6 (low density lipoprotein receptor related proteins 6) was identified as a co-receptor for WNT/β-catenin signaling, but recent studies showed that it also can mediate WNT/PCP signaling. In this study, we screened mutations in the LRP6 gene in 343 NTDs and 215 ethnically matched normal controls of Chinese Han population. Three rare missense mutations (c.1514A>G, p.Y505C); c.2984A>G, p.D995G; and c.4280C>A, p.P1427Q) of the LRP6 gene were identified in Chinese NTD patients. The Y505C mutation is a loss-of-function mutation on both WNT/β-catenin and PCP signaling. The D995G mutation only partially lost inhibition on PCP signaling without affecting WNT/β-catenin signaling. The P1427Q mutation dramatically increased WNT/β-catenin signaling but only mildly loss of inhibition on PCP signaling. All three mutations failed to rescue CE defects caused by lrp6 morpholino oligos knockdown in zebrafish. Of interest, when overexpressed, D995G did not induce any defects, but Y505C and P1427Q caused more severe CE defects in zebrafish. Our results suggested that over-active canonical WNT signaling induced by gain-of-function mutation in LRP6 could also contribute to human NTDs, and a balanced WNT/β-catenin and PCP signaling is probably required for proper neural tube development. Birth Defects Research, 2017.© 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  4. Phosphorylation of Sox9 is required for neural crest delamination and is regulated downstream of BMP and canonical Wnt signaling.

    Science.gov (United States)

    Liu, Jessica A J; Wu, Ming-Hoi; Yan, Carol H; Chau, Bolton K H; So, Henry; Ng, Alvis; Chan, Alan; Cheah, Kathryn S E; Briscoe, James; Cheung, Martin

    2013-02-19

    Coordination of neural crest cell (NCC) induction and delamination is orchestrated by several transcription factors. Among these, Sry-related HMG box-9 (Sox9) and Snail2 have been implicated in both the induction of NCC identity and, together with phoshorylation, NCC delamination. How phosphorylation effects this function has not been clear. Here we show, in the developing chick neural tube, that phosphorylation of Sox9 on S64 and S181 facilitates its SUMOylation, and the phosphorylated forms of Sox9 are essential for trunk neural crest delamination. Both phosphorylation and to a lesser extent SUMOylation, of Sox9 are required to cooperate with Snail2 to promote delamination. Moreover, bone morphogenetic protein and canonical Wnt signaling induce phosphorylation of Sox9, thereby connecting extracellular signals with the delamination of NCCs. Together the data suggest a model in which extracellular signals initiate phosphorylation of Sox9 and its cooperation with Snail2 to induce NCC delamination.

  5. On-chip temperature-based digital signal processing for customized wireless microcontroller

    Science.gov (United States)

    Farhah Razanah Faezal, Siti; Isa, Mohd Nazrin Md; Harun, Azizi; Nizam Mohyar, Shaiful; Bahari Jambek, Asral

    2017-11-01

    Increases in die size and power density inside system-on-chip (SoC) design have brought thermal issue inside the system. Uneven heat-up and increasing in temperature offset on-chip has become a major factor that can limits the system performance. This paper presents the design and simulation of a temperature-based digital signal processing for modern system-on-chip design using the Verilog HDL. This design yields continuous monitoring of temperature and reacts to specified conditions. The simulation of the system has been done on Altera Quartus Software v. 14. With system above, microcontroller can achieve nominal power dissipation and operation is within the temperature range due to the incorporate of an interrupt-based system.

  6. A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.

    Science.gov (United States)

    Kim, Yoon Jae; Park, Sung Woo; Yeom, Hong Gi; Bang, Moon Suk; Kim, June Sic; Chung, Chun Kee; Kim, Sungwan

    2015-08-20

    A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified. The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories. The average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively. A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because

  7. Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

    DEFF Research Database (Denmark)

    S. Nadimi, Esmaeil; Jørgensen, Rasmus Nyholm; Blanes-Vidal, Victoria

    2012-01-01

    Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them...... into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high...... communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer...

  8. A quantum theory for the irreplaceable role of docosahexaenoic acid in neural cell signalling throughout evolution.

    Science.gov (United States)

    Crawford, Michael A; Broadhurst, C Leigh; Guest, Martin; Nagar, Atulya; Wang, Yiqun; Ghebremeskel, Kebreab; Schmidt, Walter F

    2013-01-01

    Six hundred million years ago, the fossil record displays the sudden appearance of intracellular detail and the 32 phyla. The "Cambrian Explosion" marks the onset of dominant aerobic life. Fossil intracellular structures are so similar to extant organisms that they were likely made with similar membrane lipids and proteins, which together provided for organisation and specialisation. While amino acids could be synthesised over 4 billion years ago, only oxidative metabolism allows for the synthesis of highly unsaturated fatty acids, thus producing novel lipid molecular species for specialised cell membranes. Docosahexaenoic acid (DHA) provided the core for the development of the photoreceptor, and conversion of photons into electricity stimulated the evolution of the nervous system and brain. Since then, DHA has been conserved as the principle acyl component of photoreceptor synaptic and neuronal signalling membranes in the cephalopods, fish, amphibian, reptiles, birds, mammals and humans. This extreme conservation in electrical signalling membranes despite great genomic change suggests it was DHA dictating to DNA rather than the generally accepted other way around. We offer a theoretical explanation based on the quantum mechanical properties of DHA for such extreme conservation. The unique molecular structure of DHA allows for quantum transfer and communication of π-electrons, which explains the precise depolarisation of retinal membranes and the cohesive, organised neural signalling which characterises higher intelligence. Copyright © 2012 Elsevier Ltd. All rights reserved.

  9. EEG signal classification method based on fractal features and neural network.

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2008-01-01

    In this paper, we propose a method to classify electroencephalogram (EEG) signal recorded from left- and right-hand movement imaginations. Three subjects (two males and one female) are volunteered to participate in the experiment. We use a technique of complexity measure based on fractal analysis to reveal feature patterns in the EEG signal. Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded fractal dimension (FD) values between relaxing and imaging states of the recorded EEG signal. To show the waveform of FDs, we use a windowing-based method or called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. Two feature parameters; K-L divergence and different expected values are proposed to be input variables of the classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results can be considerably applied in a brain-computer interface (BCI) application and show that the proposed method is more effective than the conventional method by improving average classification rates of 87.5% and 88.3% for left- and right-hand movement imagery tasks, respectively.

  10. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  11. Spectral properties of multiple myoelectric signals: New insights into the neural origin of muscle synergies.

    Science.gov (United States)

    Frère, Julien

    2017-07-04

    It is still unclear if muscle synergies reflect neural strategies or mirror the underlying mechanical constraints. Therefore, this study aimed to verify the consistency of muscle groupings between the synergies based on the linear envelope (LE) of muscle activities and those incorporating the time-frequency (TF) features of the electromyographic (EMG) signals. Twelve healthy participants performed six 20-m walking trials at a comfort and fast self-selected speed, while the activity of eleven lower limb muscles was recorded by means of surface EMG. Wavelet-transformed EMG was used to obtain the TF pattern and muscle synergies were extracted by non-negative matrix factorization. When five muscle synergies were extracted, both methods defined similar muscle groupings whatever the walking speed. When accounting the reconstruction level of the initial dataset, a new TF synergy emerged. This new synergy dissociated the activity of the rectus femoris from those of the vastii muscles (synergy #1) and from the one of the tensor fascia latae (synergy #5). Overall, extracting TF muscle synergies supports the neural origin of muscle synergies and provides an opportunity to distinguish between prescriptive and descriptive muscle synergies. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  12. AUTOMATIC SEGMENTATION OF BROADCAST AUDIO SIGNALS USING AUTO ASSOCIATIVE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2010-12-01

    Full Text Available In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC, Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.

  13. A low-noise low-power amplifier for implantable device for neural signal acquisition.

    Science.gov (United States)

    Li, Ming-Ze; Tang, Kea-Tiong

    2009-01-01

    This paper presents a low-noise low-power amplifier for implantable device for neural signal acquisition. By operating MOS transistors in the subthreshold region, smaller low-frequency noise and lower power consumption can be achieved. A low power, low-noise common-drain buffer and a low-noise, high-linearity, low pass filter are used for high frequency noise filtering. Post-layout simulation shows the input referred noise of the system is 2.19microVrms from 10Hz to 10 KHz, power consumption is 55.8microW, and the NEF is 2.53. The amplifier was fabricated using a TSMC 0.18microm 1P6M CMOS process. Simulation results show that this low-noise, low-power amplifier is suitable for implantable device applications.

  14. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Xiaofei Yan

    2016-08-01

    Full Text Available Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN-based multi-sensor system and artificial neural network (ANN. Sensors (CO, CO2, smoke, air temperature and relative humidity were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5% than single-sensor input (50.9%–92.5%. Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

  15. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

    Science.gov (United States)

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-08-04

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

  16. G-protein-coupled receptors and localized signaling in the primary cilium during ventral neural tube patterning.

    Science.gov (United States)

    Hwang, Sun-Hee; Mukhopadhyay, Saikat

    2015-01-01

    The primary cilium is critical in sonic hedgehog (Shh)-dependent ventral patterning of the vertebrate neural tube. Most mutants that cause disruption of the cilium result in decreased Shh signaling in the neural tube. In contrast, mutations in the intraflagellar complex A (IFT-A) and the tubby family protein, Tulp3, result in increased Shh signaling in the neural tube. Proteomic analysis of Tulp3-binding proteins first pointed to the role of the IFT-A complex in trafficking Tulp3 into the cilia. Tulp3 directs trafficking of rhodopsin family G-protein-coupled receptors (GPCRs) to the cilia, suggesting the role of a GPCR in mediating the paradoxical effects of the Tulp3/IFT-A complex in causing increased Shh signaling. Gpr161 has recently been identified as a Tulp3/IFT-A-regulated GPCR that localizes to the primary cilium. A null knock-out mouse model of Gpr161 phenocopies Tulp3 and IFT-A mutants, and causes increased Shh signaling throughout the neural tube. In the absence of Shh, the bifunctional Gli transcription factors are proteolytically processed into repressor forms in a protein kinase A (PKA) -dependent and cilium-dependent manner. Gpr161 activity results in increased cAMP levels in a Gαs -coupled manner, and determines processing of Gli3. Shh signaling also results in removal of Gpr161 from the cilia, suggesting that Gpr161 functions in a positive feedback loop in the Shh pathway. As PKA-null and Gαs mutant embryos also exhibit increased Shh signaling in the neural tube, Gpr161 is a strong candidate for a GPCR that regulates ciliary cAMP levels, and activates PKA in close proximity to the cilia. © 2014 Wiley Periodicals, Inc.

  17. Decoding neural events from fMRI BOLD signal: A comparison of existing approaches and development of a new algorithm

    Science.gov (United States)

    Bush, Keith; Cisler, Josh

    2013-01-01

    Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variance in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system’s state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification, and observation sampling rate (i.e., TR). Further, we compare the algorithms’ performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms’ performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed. PMID:23602664

  18. Optimization of neural network architecture for classification of radar jamming FM signals

    Science.gov (United States)

    Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

    2017-05-01

    The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

  19. Fatty acid–induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans

    OpenAIRE

    Van Oudenhove, Lukas; Mckie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

    2011-01-01

    Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an in...

  20. Mixer circuit, receiver comprising a mixer circuit, wireless communication comprising a receiver, method for generating an output signal by mixing an input signal with an oscillator signal

    NARCIS (Netherlands)

    Klumperink, Eric A.M.; Louwsma, S.M.; Stikvoort, Eduard F.

    2006-01-01

    The invention relates to a mixer circuit comprising an input node for receiving an input signal, a first output node 202, and a second output node 203, voltage to current conversion means and switching means operatively coupled to each other and to the input node, the first output node and the

  1. Mixer circuit, receiver comprising a mixer circuit, wireless communication comprising a receiver, method for generating an output signal by mixing an input signal with an oscillator signal

    NARCIS (Netherlands)

    Klumperink, Eric A.M.; Louwsma, S.M.; Stikvoort, E.

    2004-01-01

    The invention relates to a mixer circuit comprising an input node for receiving an input signal, a first output node 202, and a second output node 203, voltage to current conversion means and switching means operatively coupled to each other and to the input node, the first output node and the

  2. Lingo-1 shRNA and Notch signaling inhibitor DAPT promote differentiation of neural stem/progenitor cells into neurons.

    Science.gov (United States)

    Wang, Jue; Ye, Zhizhong; Zheng, Shuhui; Chen, Luming; Wan, Yong; Deng, Yubin; Yang, Ruirui

    2016-03-01

    Determination of the exogenous factors that regulate differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes is an important step in the clinical therapy of spinal cord injury (SCI). The Notch pathway inhibits the differentiation of neural stem/progenitor cells and Lingo-1 is a strong negative regulator for myelination and axon growth. While Lingo-1 shRNA and N-[N-(3, 5-difluorophenacetyl)-1-alanyl]-S-Phenylglycinet-butylester (DAPT), a Notch pathway inhibitor, have been used separately to help repair SCI, the results have been unsatisfactory. Here we investigated and elucidated the preliminary mechanism for the effect of Lingo-1 shRNA and DAPT on neural stem/progenitor cells differentiation. We found that neural stem/progenitor cells from E14 rat embryos expressed Nestin, Sox-2 and Lingo-1, and we optimized the transduction of neural stem/progenitor cells using lentiviral vectors encoding Lingo-1 shRNA. The addition of DAPT decreased the expression of Notch intracellular domain (NICD) as well as the downstream genes Hes1 and Hes5. Expression of NeuN, CNPase and GFAP in DAPT treated cells and expression of NeuN in Lingo-1 shRNA treated cells confirmed differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes. These results revealed that while Lingo-1 shRNA and Notch signaling inhibitor DAPT both promoted differentiation of neural stem cells into neurons, only DAPT was capable of driving neural stem/progenitor cells differentiation into oligodendrocytes and astrocytes. Since we were able to show that both Lingo-1 shRNA and DAPT could drive neural stem/progenitor cells differentiation, our data might aid the development of more effective SCI therapies using Lingo-1 shRNA and DAPT. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Analysis of acoustic emission signals at austempering of steels using neural networks

    Science.gov (United States)

    Łazarska, Malgorzata; Wozniak, Tadeusz Z.; Ranachowski, Zbigniew; Trafarski, Andrzej; Domek, Grzegorz

    2017-05-01

    Bearing steel 100CrMnSi6-4 and tool steel C105U were used to carry out this research with the steels being austempered to obtain a martensitic-bainitic structure. During the process quite a large number of acoustic emissions (AE) were observed. These signals were then analysed using neural networks resulting in the identification of three groups of events of: high, medium and low energy and in addition their spectral characteristics were plotted. The results were presented in the form of diagrams of AE incidence as a function of time. It was demonstrated that complex transformations of austenite into martensite and bainite occurred when austempering bearing steel at 160 °C and tool steel at 130 °C respectively. The selected temperatures of isothermal quenching of the tested steels were within the area near to MS temperature, which affected the complex course of phase transition. The high activity of AE is a typical occurrence for martensitic transformation and this is the transformation mechanism that induces the generation of AE signals of higher energy in the first stage of transition. In the second stage of transformation, the initially nucleated martensite accelerates the occurrence of the next bainitic transformation.

  4. Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.

    Science.gov (United States)

    Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon

    2017-03-01

    An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Nogo Receptor Signaling Restricts Adult Neural Plasticity by Limiting Synaptic AMPA Receptor Delivery.

    Science.gov (United States)

    Jitsuki, Susumu; Nakajima, Waki; Takemoto, Kiwamu; Sano, Akane; Tada, Hirobumi; Takahashi-Jitsuki, Aoi; Takahashi, Takuya

    2016-01-01

    Experience-dependent plasticity is limited in the adult brain, and its molecular and cellular mechanisms are poorly understood. Removal of the myelin-inhibiting signaling protein, Nogo receptor (NgR1), restores adult neural plasticity. Here we found that, in NgR1-deficient mice, whisker experience-driven synaptic α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) insertion in the barrel cortex, which is normally complete by 2 weeks after birth, lasts into adulthood. In vivo live imaging by two-photon microscopy revealed more AMPAR on the surface of spines in the adult barrel cortex of NgR1-deficient than on those of wild-type (WT) mice. Furthermore, we observed that whisker stimulation produced new spines in the adult barrel cortex of mutant but not WT mice, and that the newly synthesized spines contained surface AMPAR. These results suggest that Nogo signaling limits plasticity by restricting synaptic AMPAR delivery in coordination with anatomical plasticity. © The Author 2015. Published by Oxford University Press.

  6. Depression and treatment response: dynamic interplay of signaling pathways and altered neural processes.

    Science.gov (United States)

    Duric, Vanja; Duman, Ronald S

    2013-01-01

    Since the 1960s, when the first tricyclic and monoamine oxidase inhibitor antidepressant drugs were introduced, most of the ensuing agents were designed to target similar brain pathways that elevate serotonin and/or norepinephrine signaling. Fifty years later, the main goal of the current depression research is to develop faster-acting, more effective therapeutic agents with fewer side effects, as currently available antidepressants are plagued by delayed therapeutic onset and low response rates. Clinical and basic science research studies have made significant progress towards deciphering the pathophysiological events within the brain involved in development, maintenance, and treatment of major depressive disorder. Imaging and postmortem brain studies in depressed human subjects, in combination with animal behavioral models of depression, have identified a number of different cellular events, intracellular signaling pathways, proteins, and target genes that are modulated by stress and are potentially vital mediators of antidepressant action. In this review, we focus on several neural mechanisms, primarily within the hippocampus and prefrontal cortex, which have recently been implicated in depression and treatment response.

  7. Feasibility of retroreflective transdermal optical wireless communication.

    Science.gov (United States)

    Gil, Yotam; Rotter, Nadav; Arnon, Shlomi

    2012-06-20

    There is an increasing demand for transdermal high-data-rate communication for use with in-body devices, such as pacemakers, smart prostheses, neural signals processors at the brain interface, and cameras acting as artificial eyes as well as for collecting signals generated within the human body. Prominent requirements of these communication systems include (1) wireless modality, (2) noise immunity and (3) ultra-low-power consumption for the in-body device. Today, the common wireless methods for transdermal communication are based on communication at radio frequencies, electrical induction, or acoustic waves. In this paper, we will explore another alternative to these methods--optical wireless communication (OWC)--for which modulated light carries the information. The main advantages of OWC in transdermal communication, by comparison to the other methods, are the high data rates and immunity to external interference availed, which combine to make it a promising technology for next-generation systems. In this paper, we present a mathematical model and experimental results of measurements from direct link and retroreflection link configurations with Gallus gallus domesticus derma as the transdermal channel. The main conclusion from this work is that an OWC link is an attractive communication solution in medical applications. For a modulating retroreflective link to become a competitive solution in comparison with a direct link, low-energy-consumption modulating retroreflectors should be developed.

  8. Estrogen Stimulates Proliferation and Differentiation of Neural Stem/Progenitor Cells through Different Signal Transduction Pathways

    Directory of Open Access Journals (Sweden)

    Makiko Okada

    2010-10-01

    Full Text Available Our previous study indicated that both 17β-estradiol (E2, known to be an endogenous estrogen, and bisphenol A (BPA, known to be a xenoestrogen, could positively influence the proliferation or differentiation of neural stem/progenitor cells (NS/PCs. The aim of the present study was to identify the signal transduction pathways for estrogenic activities promoting proliferation and differentiation of NS/PCs via well known nuclear estrogen receptors (ERs or putative membrane-associated ERs. NS/PCs were cultured from the telencephalon of 15-day-old rat embryos. In order to confirm the involvement of nuclear ERs for estrogenic activities, their specific antagonist, ICI-182,780, was used. The presence of putative membrane-associated ER was functionally examined as to whether E2 can activate rapid intracellular signaling mechanism. In order to confirm the involvement of membrane-associated ERs for estrogenic activities, a cell-impermeable E2, bovine serum albumin-conjugated E2 (E2-BSA was used. We showed that E2 could rapidly activate extracellular signal-regulated kinases 1/2 (ERK 1/2, which was not inhibited by ICI-182,780. ICI-182,780 abrogated the stimulatory effect of these estrogens (E2 and BPA on the proliferation of NS/PCs, but not their effect on the differentiation of the NS/PCs into oligodendroglia. Furthermore, E2-BSA mimicked the activity of differentiation from NS/PCs into oligodendroglia, but not the activity of proliferation. Our study suggests that (1 the estrogen induced proliferation of NS/PCs is mediated via nuclear ERs; (2 the oligodendroglial generation from NS/PCs is likely to be stimulated via putative membrane‑associated ERs.

  9. Inhibitory control and trait aggression: neural and behavioral insights using the emotional stop signal task.

    Science.gov (United States)

    Pawliczek, Christina M; Derntl, Birgit; Kellermann, Thilo; Kohn, Nils; Gur, Ruben C; Habel, Ute

    2013-10-01

    Deficits in response inhibition and heightened impulsivity have been linked to psychiatric disorders and aggression. They have been investigated in clinical groups as well as individuals with trait characteristics, yielding insights into the underlying neural and behavioral mechanisms of response inhibition and impulsivity. The motor inhibition tasks employed in most studies, however, have lacked an emotional component, which is crucial given that both response inhibition and impulsivity attain salience within a socio-emotional context. For this fMRI study, we selected a group with high trait aggression (HA, n=17) and one with low trait aggression (LA, n=16) from 550 males who had completed an Aggression Questionnaire. Neural activation was compared to an emotional version (including angry and neutral faces) of the stop signal task. Behavioral results revealed impaired response inhibition in HA, associated with higher motor impulsivity. This was accompanied by attenuated activation in brain regions involved in response inhibition, including the pre-supplementary motor area (SMA) and motor cortex. Together, these findings offer evidence that a reduced inhibition capacity is present in HA. Notably, response inhibition improved during anger trials in both groups, suggesting a facilitation effect through heightened activation in the related brain regions. In both groups, inclusion of the anger stimuli enhanced the activation of the motor and somatosensory areas, which modulate executive control, and of limbic regions including the amygdala. In summary, the investigation of response inhibition in individuals with high and low trait characteristics affords useful insights into the underlying distinct processing mechanisms. It can contribute to the investigation of trait markers in a clinical context without having to deal with the complex mechanisms of a clinical disorder itself. In contrast, the mechanisms of emotional response inhibition did not differ between groups

  10. Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

    Science.gov (United States)

    Marathe, Amar R; Lawhern, Vernon J; Wu, Dongrui; Slayback, David; Lance, Brent J

    2016-03-01

    The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibrated to account for changes in the neural signals due to a variety of factors including changes in human state, the surrounding environment, and task conditions. Novel approaches to reduce calibration time or effort will dramatically improve the usability of BCI systems. Active Learning (AL) is an iterative semi-supervised learning technique for learning in situations in which data may be abundant, but labels for the data are difficult or expensive to obtain. In this paper, we apply AL to a simulated BCI system for target identification using data from a rapid serial visual presentation (RSVP) paradigm to minimize the amount of training samples needed to initially calibrate a neural classifier. Our results show AL can produce similar overall classification accuracy with significantly less labeled data (in some cases less than 20%) when compared to alternative calibration approaches. In fact, AL classification performance matches performance of 10-fold cross-validation (CV) in over 70% of subjects when training with less than 50% of the data. To our knowledge, this is the first work to demonstrate the use of AL for offline electroencephalography (EEG) calibration in a simulated BCI paradigm. While AL itself is not often amenable for use in real-time systems, this work opens the door to alternative AL-like systems that are more amenable for BCI applications and thus enables future efforts for developing highly adaptive BCI systems.

  11. Weak signal detection and propagation in diluted feed-forward neural network with recurrent excitation and inhibition

    Science.gov (United States)

    Wang, Jiang; Han, Ruixue; Wei, Xilei; Qin, Yingmei; Yu, Haitao; Deng, Bin

    2016-12-01

    Reliable signal propagation across distributed brain areas provides the basis for neural circuit function. Modeling studies on cortical circuits have shown that multilayered feed-forward networks (FFNs), if strongly and/or densely connected, can enable robust signal propagation. However, cortical networks are typically neither densely connected nor have strong synapses. This paper investigates under which conditions spiking activity can be propagated reliably across diluted FFNs. Extending previous works, we model each layer as a recurrent sub-network constituting both excitatory (E) and inhibitory (I) neurons and consider the effect of interactions between local excitation and inhibition on signal propagation. It is shown that elevation of cellular excitation-inhibition (EI) balance in the local sub-networks (layers) softens the requirement for dense/strong anatomical connections and thereby promotes weak signal propagation in weakly connected networks. By means of iterated maps, we show how elevated local excitability state compensates for the decreased gain of synchrony transfer function that is due to sparse long-range connectivity. Finally, we report that modulations of EI balance and background activity provide a mechanism for selectively gating and routing neural signal. Our results highlight the essential role of intrinsic network states in neural computation.

  12. Two major gate-keepers in the self-renewal of neural stem cells: Erk1/2 and PLCγ1 in FGFR signaling

    Directory of Open Access Journals (Sweden)

    Lee Jin-A

    2009-06-01

    Full Text Available Abstract Neural stem cells are undifferentiated precursor cells that proliferate, self-renew, and give rise to neuronal and glial lineages. Understanding the molecular mechanisms underlying their self-renewal is an important aspect in neural stem cell biology. The regulation mechanisms governing self-renewal of neural stem cells and the signaling pathways responsible for the proliferation and maintenance of adult stem cells remain largely unknown. In this issue of Molecular Brain [Ma DK et al. Molecular genetic analysis of FGFR1 signaling reveals distinct roles of MAPK and PLCγ1 activation for self-renewal of adult neural stem cells. Molecular Brain 2009, 2:16], characterized the different roles of MAPK and PLCγ1 in FGFR1 signaling in the self-renewal of neural stem cells. These novel findings provide insights into basic neural stem cell biology and clinical applications of potential stem-cell-based therapy.

  13. Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform.

    Science.gov (United States)

    Işik, Hakan; Sezer, Esma

    2012-02-01

    In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.

  14. STAT3 signal that mediates the neural plasticity is involved in willed-movement training in focal ischemic rats.

    Science.gov (United States)

    Tang, Qing-Ping; Shen, Qin; Wu, Li-Xiang; Feng, Xiang-Ling; Liu, Hui; Wu, Bei; Huang, Xiao-Song; Wang, Gai-Qing; Li, Zhong-Hao; Liu, Zun-Jing

    2016-07-01

    Willed-movement training has been demonstrated to be a promising approach to increase motor performance and neural plasticity in ischemic rats. However, little is known regarding the molecular signals that are involved in neural plasticity following willed-movement training. To investigate the potential signals related to neural plasticity following willed-movement training, littermate rats were randomly assigned into three groups: middle cerebral artery occlusion, environmental modification, and willed-movement training. The infarct volume was measured 18 d after occlusion of the right middle cerebral artery. Reverse transcription-polymerase chain reaction (PCR) and immunofluorescence staining were used to detect the changes in the signal transducer and activator of transcription 3 (STAT3) mRNA and protein, respectively. A chromatin immunoprecipitation was used to investigate whether STAT3 bound to plasticity-related genes, such as brain-derived neurotrophic factor (BDNF), synaptophysin, and protein interacting with C kinase 1 (PICK1). In this study, we demonstrated that STAT3 mRNA and protein were markedly increased following 15-d willed-movement training in the ischemic hemispheres of the treated rats. STAT3 bound to BDNF, PICK1, and synaptophysin promoters in the neocortical cells of rats. These data suggest that the increased STAT3 levels after willed-movement training might play critical roles in the neural plasticity by directly regulating plasticity-related genes.

  15. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  16. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  17. Dynamics of BMP and Hes1/Hairy1 signaling in the dorsal neural tube underlies the transition from neural crest to definitive roof plate.

    Science.gov (United States)

    Nitzan, Erez; Avraham, Oshri; Kahane, Nitza; Ofek, Shai; Kumar, Deepak; Kalcheim, Chaya

    2016-03-24

    The dorsal midline region of the neural tube that results from closure of the neural folds is generally termed the roof plate (RP). However, this domain is highly dynamic and complex, and is first transiently inhabited by prospective neural crest (NC) cells that sequentially emigrate from the neuroepithelium. It only later becomes the definitive RP, the dorsal midline cells of the spinal cord. We previously showed that at the trunk level of the axis, prospective RP progenitors originate ventral to the premigratory NC and progressively reach the dorsal midline following NC emigration. However, the molecular mechanisms underlying the end of NC production and formation of the definitive RP remain virtually unknown. Based on distinctive cellular and molecular traits, we have defined an initial NC and a subsequent RP stage, allowing us to investigate the mechanisms responsible for the transition between the two phases. We demonstrate that in spite of the constant production of BMP4 in the dorsal tube at both stages, RP progenitors only transiently respond to the ligand and lose competence shortly before they arrive at their final location. In addition, exposure of dorsal tube cells at the NC stage to high levels of BMP signaling induces premature RP traits, such as Hes1/Hairy1, while concomitantly inhibiting NC production. Reciprocally, early inhibition of BMP signaling prevents Hairy1 mRNA expression at the RP stage altogether, suggesting that BMP is both necessary and sufficient for the development of this RP-specific trait. Furthermore, when Hes1/Hairy1 is misexpressed at the NC stage, it inhibits BMP signaling and downregulates BMPR1A/Alk3 mRNA expression, transcription of BMP targets such as Foxd3, cell-cycle progression, and NC emigration. Reciprocally, Foxd3 inhibits Hairy1, suggesting that repressive cross-interactions at the level of, and downstream from, BMP ensure the temporal separation between both lineages. Together, our data suggest that BMP signaling is

  18. Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

    Science.gov (United States)

    Shimabukuro, Hayato; Semelin, Benoit

    2017-07-01

    The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

  19. With a little help from my friends: androgens tap BDNF signaling pathways to alter neural circuits.

    Science.gov (United States)

    Ottem, E N; Bailey, D J; Jordan, C L; Breedlove, S M

    2013-06-03

    Gonadal androgens are critical for the development and maintenance of sexually dimorphic regions of the male nervous system, which is critical for male-specific behavior and physiological functioning. In rodents, the motoneurons of the spinal nucleus of the bulbocavernosus (SNB) provide a useful example of a neural system dependent on androgen. Unless rescued by perinatal androgens, the SNB motoneurons will undergo apoptotic cell death. In adulthood, SNB motoneurons remain dependent on androgen, as castration leads to somal atrophy and dendritic retraction. In a second vertebrate model, the zebra finch, androgens are critical for the development of several brain nuclei involved in song production in males. Androgen deprivation during a critical period during postnatal development disrupts song acquisition and dimorphic size-associated nuclei. Mechanisms by which androgens exert masculinizing effects in each model system remain elusive. Recent studies suggest that brain-derived neurotrophic factor (BDNF) may play a role in androgen-dependent masculinization and maintenance of both SNB motoneurons and song nuclei of birds. This review aims to summarize studies demonstrating that BDNF signaling via its tyrosine receptor kinase (TrkB) receptor may work cooperatively with androgens to maintain somal and dendritic morphology of SNB motoneurons. We further describe studies that suggest the cellular origin of BDNF is of particular importance in androgen-dependent regulation of SNB motoneurons. We review evidence that androgens and BDNF may synergistically influence song development and plasticity in bird species. Finally, we provide hypothetical models of mechanisms that may underlie androgen- and BDNF-dependent signaling pathways. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  20. Serotonin 2A Receptor Signaling Underlies LSD-induced Alteration of the Neural Response to Dynamic Changes in Music.

    Science.gov (United States)

    Barrett, Frederick S; Preller, Katrin H; Herdener, Marcus; Janata, Petr; Vollenweider, Franz X

    2017-09-28

    Classic psychedelic drugs (serotonin 2A, or 5HT2A, receptor agonists) have notable effects on music listening. In the current report, blood oxygen level-dependent (BOLD) signal was collected during music listening in 25 healthy adults after administration of placebo, lysergic acid diethylamide (LSD), and LSD pretreated with the 5HT2A antagonist ketanserin, to investigate the role of 5HT2A receptor signaling in the neural response to the time-varying tonal structure of music. Tonality-tracking analysis of BOLD data revealed that 5HT2A receptor signaling alters the neural response to music in brain regions supporting basic and higher-level musical and auditory processing, and areas involved in memory, emotion, and self-referential processing. This suggests a critical role of 5HT2A receptor signaling in supporting the neural tracking of dynamic tonal structure in music, as well as in supporting the associated increases in emotionality, connectedness, and meaningfulness in response to music that are commonly observed after the administration of LSD and other psychedelics. Together, these findings inform the neuropsychopharmacology of music perception and cognition, meaningful music listening experiences, and altered perception of music during psychedelic experiences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  1. The effects of life stress and neural learning signals on fluid intelligence.

    Science.gov (United States)

    Friedel, Eva; Schlagenhauf, Florian; Beck, Anne; Dolan, Raymond J; Huys, Quentin J M; Rapp, Michael A; Heinz, Andreas

    2015-02-01

    Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account.

  2. Fast contactless vibrating structure characterization using real time field programmable gate array-based digital signal processing: demonstrations with a passive wireless acoustic delay line probe and vision.

    Science.gov (United States)

    Goavec-Mérou, G; Chrétien, N; Friedt, J-M; Sandoz, P; Martin, G; Lenczner, M; Ballandras, S

    2014-01-01

    Vibrating mechanical structure characterization is demonstrated using contactless techniques best suited for mobile and rotating equipments. Fast measurement rates are achieved using Field Programmable Gate Array (FPGA) devices as real-time digital signal processors. Two kinds of algorithms are implemented on FPGA and experimentally validated in the case of the vibrating tuning fork. A first application concerns in-plane displacement detection by vision with sampling rates above 10 kHz, thus reaching frequency ranges above the audio range. A second demonstration concerns pulsed-RADAR cooperative target phase detection and is applied to radiofrequency acoustic transducers used as passive wireless strain gauges. In this case, the 250 ksamples/s refresh rate achieved is only limited by the acoustic sensor design but not by the detection bandwidth. These realizations illustrate the efficiency, interest, and potentialities of FPGA-based real-time digital signal processing for the contactless interrogation of passive embedded probes with high refresh rates.

  3. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

    Directory of Open Access Journals (Sweden)

    Zhang Wei

    2017-01-01

    Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

  4. Lymphotropic Virions Affect Chemokine Receptor-Mediated Neural Signaling and Apoptosis: Implications for Human Immunodeficiency Virus Type 1-Associated Dementia

    Science.gov (United States)

    Zheng, Jialin; Ghorpade, Anuja; Niemann, Douglas; Cotter, Robin L.; Thylin, Michael R.; Epstein, Leon; Swartz, Jennifer M.; Shepard, Robin B.; Liu, Xiaojuan; Nukuna, Adeline; Gendelman, Howard E.

    1999-01-01

    Chemokine receptors pivotal for human immunodeficiency virus type 1 (HIV-1) infection in lymphocytes and macrophages (CCR3, CCR5, and CXCR4) are expressed on neural cells (microglia, astrocytes, and/or neurons). It is these cells which are damaged during progressive HIV-1 infection of the central nervous system. We theorize that viral coreceptors could effect neural cell damage during HIV-1-associated dementia (HAD) without simultaneously affecting viral replication. To these ends, we studied the ability of diverse viral strains to affect intracellular signaling and apoptosis of neurons, astrocytes, and monocyte-derived macrophages. Inhibition of cyclic AMP, activation of inositol 1,4,5-trisphosphate, and apoptosis were induced by diverse HIV-1 strains, principally in neurons. Virions from T-cell-tropic (T-tropic) strains (MN, IIIB, and Lai) produced the most significant alterations in signaling of neurons and astrocytes. The HIV-1 envelope glycoprotein, gp120, induced markedly less neural damage than purified virions. Macrophage-tropic (M-tropic) strains (ADA, JR-FL, Bal, MS-CSF, and DJV) produced the least neural damage, while 89.6, a dual-tropic HIV-1 strain, elicited intermediate neural cell damage. All T-tropic strain-mediated neuronal impairments were blocked by the CXCR4 antibody, 12G5. In contrast, the M-tropic strains were only partially blocked by 12G5. CXCR4-mediated neuronal apoptosis was confirmed in pure populations of rat cerebellar granule neurons and was blocked by HA1004, an inhibitor of calcium/calmodulin-dependent protein kinase II, protein kinase A, and protein kinase C. Taken together, these results suggest that progeny HIV-1 virions can influence neuronal signal transduction and apoptosis. This process occurs, in part, through CXCR4 and is independent of CD4 binding. T-tropic viruses that traffic in and out of the brain during progressive HIV-1 disease may play an important role in HAD neuropathogenesis. PMID:10482576

  5. Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks.

    Science.gov (United States)

    Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh

    2017-05-01

    In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music. Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively. The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

  6. Forcast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Vannucci, A.; Oliveira, K.A.; Tajima, T.

    1998-03-03

    A feed-forward neural network with two hidden layers is used in this work to forecast major and minor disruptive instabilities in TEXT discharges. Using soft X-ray signals as input data, the neural net is trained with one disruptive plasma pulse, and a different disruptive discharge is used for validation. After being properly trained the networks, with the same set of weights. is then used to forecast disruptions in two others different plasma pulses. It is observed that the neural net is able to predict the incoming of a disruption more than 3 ms in advance. This time interval is almost three times longer than the one already obtained previously when magnetic signal from a Mirnov coil was used to feed the neural networks with. To our own eye we fail to see any indication of an upcoming disruption from the experimental data this far back from the time of disruption. Finally, from what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism would be associated to an increase of the m = 2 magnetic island, that disturbs the central part of the plasma column afterwards or, in face of the results from this work, the initial perturbation would have occurred first in the central part of the plasma column, within the q = 1 magnetic surface, and then the m = 2 MHD mode would be destabilized afterwards.

  7. Wireless Access

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Wireless Access. Wireless connect to the Base station. Easy and Convenient access. Costlier as compared to the wired technology. Reliability challenges. We see it as a complementary technology to the DSL.

  8. Multiphoton minimal inertia scanning for fast acquisition of neural activity signals.

    Science.gov (United States)

    Schuck, Renaud; Go, Mary Ann; Garasto, Stefania; Reynolds, Stephanie; Dragotti, Pier Luigi; Schultz, Simon

    2017-11-13

    Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms. Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on evoked calcium traces recorded

  9. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

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    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  10. Radiation-induced glioblastoma signaling cascade regulates viability, apoptosis and differentiation of neural stem cells (NSC).

    Science.gov (United States)

    Ivanov, Vladimir N; Hei, Tom K

    2014-12-01

    Ionizing radiation alone or in combination with chemotherapy is the main treatment modality for brain tumors including glioblastoma. Adult neurons and astrocytes demonstrate substantial radioresistance; in contrast, human neural stem cells (NSC) are highly sensitive to radiation via induction of apoptosis. Irradiation of tumor cells has the potential risk of affecting the viability and function of NSC. In this study, we have evaluated the effects of irradiated glioblastoma cells on viability, proliferation and differentiation potential of non-irradiated (bystander) NSC through radiation-induced signaling cascades. Using media transfer experiments, we demonstrated significant effects of the U87MG glioblastoma secretome after gamma-irradiation on apoptosis in non-irradiated NSC. Addition of anti-TRAIL antibody to the transferred media partially suppressed apoptosis in NSC. Furthermore, we observed a dramatic increase in the production and secretion of IL8, TGFβ1 and IL6 by irradiated glioblastoma cells, which could promote glioblastoma cell survival and modify the effects of death factors in bystander NSC. While differentiation of NSC into neurons and astrocytes occurred efficiently with the corresponding differentiation media, pretreatment of NSC for 8 h with medium from irradiated glioblastoma cells selectively suppressed the differentiation of NSC into neurons, but not into astrocytes. Exogenous IL8 and TGFβ1 increased NSC/NPC survival, but also suppressed neuronal differentiation. On the other hand, IL6 was known to positively affect survival and differentiation of astrocyte progenitors. We established a U87MG neurosphere culture that was substantially enriched by SOX2(+) and CD133(+) glioma stem-like cells (GSC). Gamma-irradiation up-regulated apoptotic death in GSC via the FasL/Fas pathway. Media transfer experiments from irradiated GSC to non-targeted NSC again demonstrated induction of apoptosis and suppression of neuronal differentiation of NSC. In

  11. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

    Science.gov (United States)

    Samarasinghe, S; Ling, H

    In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

  12. The Neural Feedback Response to Error As a Teaching Signal for the Motor Learning System

    Science.gov (United States)

    Shadmehr, Reza

    2016-01-01

    When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system. SIGNIFICANCE STATEMENT Our sensory organs transduce errors in behavior. To improve performance, we must generate better motor commands. How does the nervous system transform an error in sensory coordinates into better motor commands in muscle coordinates? Here we show that when an error occurs during a movement, the reflexes transform the sensory representation of error into motor

  13. OPTICAL WIRELESS COMMUNICATION SYSTEM

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    JOSHUA L.Y. CHIENG

    2016-02-01

    Full Text Available The growing demand of bandwidth in this modern internet age has been testing the existing telecommunication infrastructures around the world. With broadband speeds moving towards the region of Gbps and Tbps, many researches have begun on the development of using optical wireless technology as feasible and future methods to the current wireless technology. Unlike the existing radio frequency wireless applications, optical wireless uses electromagnetic spectrums that are unlicensed and free. With that, this project aim to understand and gain better understanding of optical wireless communication system by building an experimental and simulated model. The quality of service and system performance will be investigated and reviewed. This project employs laser diode as the propagation medium and successfully transferred audio signals as far as 15 meters. On its quality of service, results of the project model reveal that the bit error rate increases, signal-to-noise ratio and quality factor decreases as the link distance between the transmitter and receiver increases. OptiSystem was used to build the simulated model and MATLAB was used to assist signal-to-noise ratio calculations. By comparing the simulated and experimental receiver’s power output, the experimental model’s efficiency is at 66.3%. Other than the system’s performance, challenges and factors affecting the system have been investigated and discussed. Such challenges include beam divergence, misalignment and particle absorption.

  14. Agonist-Evoked Ca2+ Signaling in Enteric Glia Drives Neural Programs That Regulate Intestinal Motility in MiceSummary

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    Jonathon L. McClain

    2015-11-01

    Full Text Available Background & Aims: Gastrointestinal motility is regulated by enteric neural circuitry that includes enteric neurons and glia. Enteric glia monitor synaptic activity and exhibit responses to neurotransmitters that are encoded by intracellular calcium (Ca2+ signaling. What role evoked glial responses play in the neural regulation of gut motility is unknown. We tested how evoking Ca2+ signaling in enteric glia affects the neural control of intestinal motility. Methods: We used a novel chemogenetic mouse model that expresses the designer receptor hM3Dq under the transcriptional control of the glial fibrillary acidic protein (GFAP promoter (GFAP::hM3Dq mice to selectively trigger glial Ca2+ signaling. We used in situ Ca2+ imaging and immunohistochemistry to validate this model, and we assessed gut motility by measuring pellet output and composition, colonic bead expulsion time, small intestinal transit time, total gut transit time, colonic migrating motor complex (CMMC recordings, and muscle tension recordings. Results: Expression of the hM3Dq receptor is confined to GFAP-positive enteric glia in the intestines of GFAP::hM3Dq mice. In these mice, application of the hM3Dq agonist clozapine-N-oxide (CNO selectively triggers intracellular Ca2+ responses in enteric glia. Glial activation drove neurogenic contractions in the ileum and colon but had no effect on neurogenic relaxations. CNO enhanced the amplitude and frequency of CMMCs in ex vivo preparations of the colon, and CNO increased colonic motility in vivo. CNO had no effect on the composition of fecal matter, small intestinal transit, or whole gut transit. Conclusions: Glial excitability encoded by intracellular Ca2+ signaling functions to modulate excitatory enteric circuits. Selectively triggering glial Ca2+ signaling might be a novel strategy to improve gut function in motility disorders. Keywords: Autonomic, Chemogenetic, Enteric Nervous System, Intestine, Gut

  15. Neural mechanisms underlying the effects of face-based affective signals on memory for faces: a tentative model.

    Science.gov (United States)

    Tsukiura, Takashi

    2012-01-01

    In our daily lives, we form some impressions of other people. Although those impressions are affected by many factors, face-based affective signals such as facial expression, facial attractiveness, or trustworthiness are important. Previous psychological studies have demonstrated the impact of facial impressions on remembering other people, but little is known about the neural mechanisms underlying this psychological process. The purpose of this article is to review recent functional MRI (fMRI) studies to investigate the effects of face-based affective signals including facial expression, facial attractiveness, and trustworthiness on memory for faces, and to propose a tentative concept for understanding this affective-cognitive interaction. On the basis of the aforementioned research, three brain regions are potentially involved in the processing of face-based affective signals. The first candidate is the amygdala, where activity is generally modulated by both affectively positive and negative signals from faces. Activity in the orbitofrontal cortex (OFC), as the second candidate, increases as a function of perceived positive signals from faces; whereas activity in the insular cortex, as the third candidate, reflects a function of face-based negative signals. In addition, neuroscientific studies have reported that the three regions are functionally connected to the memory-related hippocampal regions. These findings suggest that the effects of face-based affective signals on memory for faces could be modulated by interactions between the regions associated with the processing of face-based affective signals and the hippocampus as a memory-related region.

  16. A negative modulatory role for rho and rho-associated kinase signaling in delamination of neural crest cells

    Science.gov (United States)

    Groysman, Maya; Shoval, Irit; Kalcheim, Chaya

    2008-01-01

    Background Neural crest progenitors arise as epithelial cells and then undergo a process of epithelial to mesenchymal transition that precedes the generation of cellular motility and subsequent migration. We aim at understanding the underlying molecular network. Along this line, possible roles of Rho GTPases that act as molecular switches to control a variety of signal transduction pathways remain virtually unexplored, as are putative interactions between Rho proteins and additional known components of this cascade. Results We investigated the role of Rho/Rock signaling in neural crest delamination. Active RhoA and RhoB are expressed in the membrane of epithelial progenitors and are downregulated upon delamination. In vivo loss-of-function of RhoA or RhoB or of overall Rho signaling by C3 transferase enhanced and/or triggered premature crest delamination yet had no effect on cell specification. Consistently, treatment of explanted neural primordia with membrane-permeable C3 or with the Rock inhibitor Y27632 both accelerated and enhanced crest emigration without affecting cell proliferation. These treatments altered neural crest morphology by reducing stress fibers, focal adhesions and downregulating membrane-bound N-cadherin. Reciprocally, activation of endogenous Rho by lysophosphatidic acid inhibited emigration while enhancing the above. Since delamination is triggered by BMP and requires G1/S transition, we examined their relationship with Rho. Blocking Rho/Rock function rescued crest emigration upon treatment with noggin or with the G1/S inhibitor mimosine. In the latter condition, cells emigrated while arrested at G1. Conversely, BMP4 was unable to rescue cell emigration when endogenous Rho activity was enhanced by lysophosphatidic acid. Conclusion Rho-GTPases, through Rock, act downstream of BMP and of G1/S transition to negatively regulate crest delamination by modifying cytoskeleton assembly and intercellular adhesion. PMID:18945340

  17. Planar polarization of Vangl2 in the vertebrate neural plate is controlled by Wnt and Myosin II signaling

    Directory of Open Access Journals (Sweden)

    Olga Ossipova

    2015-07-01

    Full Text Available The vertebrate neural tube forms as a result of complex morphogenetic movements, which require the functions of several core planar cell polarity (PCP proteins, including Vangl2 and Prickle. Despite the importance of these proteins for neurulation, their subcellular localization and the mode of action have remained largely unknown. Here we describe the anteroposterior planar cell polarity (AP-PCP of the cells in the Xenopus neural plate. At the neural midline, the Vangl2 protein is enriched at anterior cell edges and that this localization is directed by Prickle, a Vangl2-interacting protein. Our further analysis is consistent with the model, in which Vangl2 AP-PCP is established in the neural plate as a consequence of Wnt-dependent phosphorylation. Additionally, we uncover feedback regulation of Vangl2 polarity by Myosin II, reiterating a role for mechanical forces in PCP. These observations indicate that both Wnt signaling and Myosin II activity regulate cell polarity and cell behaviors during vertebrate neurulation.

  18. Wireless Communication over Dispersive Channels

    NARCIS (Netherlands)

    Fang, K.

    2010-01-01

    Broadband wireless communication systems require high transmission rates, where the bandwidth of the transmitted signal is larger than the channel coherence bandwidth. This gives rise to time dispersion of the transmitted symbols or frequency-selectivity with different frequency components

  19. Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals.

    Science.gov (United States)

    Meza-Kubo, Victoria; Morán, Alberto L; Carrillo, Ivan; Galindo, Gilberto; García-Canseco, Eloisa

    2016-08-01

    The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task, by identifying the experience that a user has while interacting with an AAL technology under particular conditions. This may help designing better products and improve user engagement and adoption of AAL solutions. However, evaluating the UX of AAL technologies is a difficult task, due to the inherent limitations of their subjects and of the evaluation methods. In this study, we validated the feasibility of assessing the UX of older adults while they use a cognitive stimulation application using a neural network trained to recognize pleasant and unpleasant emotions from electroencephalography (EEG) signals by contrasting our results with those of additional self-report and qualitative analysis UX evaluations. Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%. As future work we will conduct additional UX evaluation studies using the three different methods, in order to appropriately validate these results. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Fatty acid-induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans.

    Science.gov (United States)

    Van Oudenhove, Lukas; McKie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

    2011-08-01

    Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an intragastric infusion of fatty acid solution or saline during neutral or sad emotion induction and rated sensations of hunger, fullness, and mood. We found an interaction between fatty acid infusion and emotion induction both in the behavioral readouts (hunger, mood) and at the level of neural activity in multiple pre-hypothesized regions of interest. Specifically, the behavioral and neural responses to sad emotion induction were attenuated by fatty acid infusion. These findings increase our understanding of the interplay among emotions, hunger, food intake, and meal-induced sensations in health, which may have important implications for a wide range of disorders, including obesity, eating disorders, and depression.

  1. Dual small-molecule targeting of SMAD signaling stimulates human induced pluripotent stem cells toward neural lineages.

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    Methichit Wattanapanitch

    Full Text Available Incurable neurological disorders such as Parkinson's disease (PD, Huntington's disease (HD, and Alzheimer's disease (AD are very common and can be life-threatening because of their progressive disease symptoms with limited treatment options. To provide an alternative renewable cell source for cell-based transplantation and as study models for neurological diseases, we generated induced pluripotent stem cells (iPSCs from human dermal fibroblasts (HDFs and then differentiated them into neural progenitor cells (NPCs and mature neurons by dual SMAD signaling inhibitors. Reprogramming efficiency was improved by supplementing the histone deacethylase inhibitor, valproic acid (VPA, and inhibitor of p160-Rho associated coiled-coil kinase (ROCK, Y-27632, after retroviral transduction. We obtained a number of iPS colonies that shared similar characteristics with human embryonic stem cells in terms of their morphology, cell surface antigens, pluripotency-associated gene and protein expressions as well as their in vitro and in vivo differentiation potentials. After treatment with Noggin and SB431542, inhibitors of the SMAD signaling pathway, HDF-iPSCs demonstrated rapid and efficient differentiation into neural lineages. Six days after neural induction, neuroepithelial cells (NEPCs were observed in the adherent monolayer culture, which had the ability to differentiate further into NPCs and neurons, as characterized by their morphology and the expression of neuron-specific transcripts and proteins. We propose that our study may be applied to generate neurological disease patient-specific iPSCs allowing better understanding of disease pathogenesis and drug sensitivity assays.

  2. Electroencephalogram Signals Processing for the Diagnosis of Petit mal and Grand mal Epilepsies Using an Artificial Neural Network

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    M. R. Arab

    2010-04-01

    Full Text Available In this study, a novel wavelet transform‐neural network method is presented. The presented method is used for theclassification of grand mal (clonic stage and petit mal (absence epilepsies into healthy, ictal and interictal (EEGs. Preprocessingis included to remove an artifact occurred by blinking and a wandering baseline (electrodes movement as well as an eyeballmovement artifact using the Discrete Wavelet Transformation (DWT. Denoising EEG signals from the AC power supplyfrequency with a suitable notch filter is another job of preprocessing. The preprocessing enhanced speed and accuracy of theprocessing stage (wavelet transform and neural network. The EEGs signals are categorized into normal and petit mal and clonicepilepsy by an expert neurologist. The categorization is confirmed by the Fast Fourier Transform (FFT analysis. The datasetincludes waves such as sharp, spike and spike‐slow wave. Through the Countinous Wavelet Transform (CWT of EEG records,transient features are accurately captured and separated and used as classifier input. We introduce a two‐stage classifier basedon the Learning Vector Quantization (LVQ neural network localized in both time and frequency contexts. The particularcoefficients of the Continuous Wavelet Transform (CWT are networks. The simulation results are very promising and theaccuracy of the proposed method obtained is of about 80%.

  3. Quantitative and kinetic profile of Wnt/β-catenin signaling components during human neural progenitor cell differentiation.

    Science.gov (United States)

    Mazemondet, Orianne; Hubner, Rayk; Frahm, Jana; Koczan, Dirk; Bader, Benjamin M; Weiss, Dieter G; Uhrmacher, Adelinde M; Frech, Moritz J; Rolfs, Arndt; Luo, Jiankai

    2011-12-01

    ReNcell VM is an immortalized human neural progenitor cell line with the ability to differentiate in vitro into astrocytes and neurons, in which the Wnt/β-catenin pathway is known to be involved. However, little is known about kinetic changes of this pathway in human neural progenitor cell differentiation. In the present study, we provide a quantitative profile of Wnt/β-catenin pathway dynamics showing its spatio-temporal regulation during ReNcell VM cell differentiation. We show first that T-cell factor dependent transcription can be activated by stabilized β-catenin. Furthermore, endogenous Wnt ligands, pathway receptors and signaling molecules are temporally controlled, demonstrating changes related to differentiation stages. During the first three hours of differentiation the signaling molecules LRP6, Dvl2 and β-catenin are spatio-temporally regulated between distinct cellular compartments. From 24 h onward, components of the Wnt/β-catenin pathway are strongly activated and regulated as shown by mRNA up-regulation of Wnt ligands (Wnt5a and Wnt7a), receptors including Frizzled-2, -3, -6, -7, and -9, and co-receptors, and target genes including Axin2. This detailed temporal profile of the Wnt/β-catenin pathway is a first step to understand, control and to orientate, in vitro, human neural progenitor cell differentiation.

  4. Wireless microsensor network solutions for neurological implantable devices

    Science.gov (United States)

    Abraham, Jose K.; Whitchurch, Ashwin; Varadan, Vijay K.

    2005-05-01

    The design and development of wireless mocrosensor network systems for the treatment of many degenerative as well as traumatic neurological disorders is presented in this paper. Due to the advances in micro and nano sensors and wireless systems, the biomedical sensors have the potential to revolutionize many areas in healthcare systems. The integration of nanodevices with neurons that are in communication with smart microsensor systems has great potential in the treatment of many neurodegenerative brain disorders. It is well established that patients suffering from either Parkinson"s disease (PD) or Epilepsy have benefited from the advantages of implantable devices in the neural pathways of the brain to alter the undesired signals thus restoring proper function. In addition, implantable devices have successfully blocked pain signals and controlled various pelvic muscles in patients with urinary and fecal incontinence. Even though the existing technology has made a tremendous impact on controlling the deleterious effects of disease, it is still in its infancy. This paper presents solutions of many problems of today's implantable and neural-electronic interface devices by combining nanowires and microelectronics with BioMEMS and applying them at cellular level for the development of a total wireless feedback control system. The only device that will actually be implanted in this research is the electrodes. All necessary controllers will be housed in accessories that are outside the body that communicate with the implanted electrodes through tiny inductively-coupled antennas. A Parkinson disease patient can just wear a hat-system close to the implantable neural probe so that the patient is free to move around, while the sensors continually monitor, record, transmit all vital information to health care specialist. In the event of a problem, the system provides an early warning to the patient while they are still mobile thus providing them the opportunity to react and

  5. CONCEPTION OF USE VIBROACOUSTIC SIGNALS AND NEURAL NETWORKS FOR DIAGNOSING OF CHOSEN ELEMENTS OF INTERNAL COMBUSTION ENGINES IN CAR VEHICLES

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    Piotr CZECH

    2014-03-01

    Full Text Available Currently used diagnostics systems are not always efficient and do not give straightforward results which allow for the assessment of the technological condition of the engine or for the identification of the possible damages in their early stages of development. Growing requirements concerning durability, reliability, reduction of costs to minimum and decrease of negative influence on the natural environment are the reasons why there is a need to acquire information about the technological condition of each of the elements of a vehicle during its exploitation. One of the possibilities to achieve information about technological condition of a vehicle are vibroacoustic phenomena. Symptoms of defects, achieved as a result of advanced methods of vibroacoustic signals processing can serve as models which can be used during construction of intelligent diagnostic system based on artificial neural networks. The work presents conception of use artificial neural networks in the task of combustion engines diagnosis.

  6. [Retinoic acid signal pathway regulation of zebra fish tooth development through manipulation of the differentiation of neural crest].

    Science.gov (United States)

    Liu, Xin; Huang, Xing; Xu, Zhiyun; Yang, Deqin

    2016-04-01

    To investigate the mechanism of retinoic acid (RA) signal in dental evolution, RA is used to explore the influence of the mechanism on neural crest's migration during the early stage of zebra fish embryos. We divided embryos of wild type and transgenic line zebra fish into three groups. 1 x 10(-7) to 6 x 10(-7) mol x L(-1) RA and 1 x 10(-7) mo x L(-1) 4-diethylaminobenzaldehyde (DEAB) were added into egg water at 24 hpf for 9 h. Dimethyl sulfoxid (DMSO) with the concentration was used as control group. Then, antisense probes of dlx2a, dlx2b, and barxl were formulated to perform whole-mount in situ hybridization to check the expressions of the genes in 48 hpf to 72 hpf embryos. We observed fluorescence of transgenic line in 4 dpf embryos. We obtained three mRNA probes successfully. Compared with DMSO control group, a low concentration (1 x 10(-7) mol x L(-1)) of RA could up-regulate the expression of mRNA (barx1, dlx2a) in neural crest. Obvious migration trend was observed toward the pharyngeal arch in which teeth adhered. Transgenic fish had spreading fluorescence tendency in pharyngeal arch. However, a high concentration (4 x 10(-7) mol x L(-1)) of RA malformed the embryos and killed them after treatment. One third of the embryos of middle concentration (3 x 10(-7) mo x L(-1)) exhibited delayed development. DEAB resulted in neural crest dysplasia. The expression of barxl and dlx2a were suppressed, and the appearance of dlx2b in tooth was delayed. RA signal pathway can regulate the progenitors of tooth by controlling the growth of the neural crest and manipulating tooth development

  7. Wireless communication links for brain-machine interface applications

    Science.gov (United States)

    Larson, L.

    2016-05-01

    Recent technological developments have given neuroscientists direct access to neural signals in real time, with the accompanying ability to decode the resulting information and control various prosthetic devices and gain insight into deeper aspects of cognition. These developments - along with deep brain stimulation for Parkinson's disease and the possible use of electro-stimulation for other maladies - leads to the conclusion that the widespread use electronic brain interface technology is a long term possibility. This talk will summarize the various technical challenges and approaches that have been developed to wirelessly communicate with the brain, including technology constraints, dc power limits, compression and data rate issues.

  8. Wireless Networks

    OpenAIRE

    Samaka, Mohammed; Khan, Khaled M.D.

    2007-01-01

    Wireless communication is the fastest-growing field in the telecommunication industry. Wireless networks have grown significantly as an important segment of the communications industry. They have become popular networks with the potential to provide high-speed, high-quality information exchange between two or more portable devices without any wire or conductors. Wireless networks can simply be characterized as the technology that provides seamless access to information, anywhere, anyplace, an...

  9. Pax3 and Hippo Signaling Coordinate Melanocyte Gene Expression in Neural Crest

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    Lauren J. Manderfield

    2014-12-01

    Full Text Available Loss of Pax3, a developmentally regulated transcription factor expressed in premigratory neural crest, results in severe developmental defects and embryonic lethality. Although Pax3 mutations produce profound phenotypes, the intrinsic transcriptional activation exhibited by Pax3 is surprisingly modest. We postulated the existence of transcriptional coactivators that function with Pax3 to mediate developmental functions. A high-throughput screen identified the Hippo effector proteins Taz and Yap65 as Pax3 coactivators. Synergistic coactivation of target genes by Pax3-Taz/Yap65 requires DNA binding by Pax3, is Tead independent, and is regulated by Hippo kinases Mst1 and Lats2. In vivo, Pax3 and Yap65 colocalize in the nucleus of neural crest progenitors in the dorsal neural tube. Neural crest deletion of Taz and Yap65 results in embryo-lethal neural crest defects and decreased expression of the Pax3 target gene, Mitf. These results suggest that Pax3 activity is regulated by the Hippo pathway and that Pax factors are Hippo effectors.

  10. Adenosine signaling promotes neuronal, catecholaminergic differentiation of primary neural crest cells and CNS-derived CAD cells.

    Science.gov (United States)

    Bilodeau, Matthew L; Ji, Ming; Paris, Maryline; Andrisani, Ourania M

    2005-07-01

    In neural crest (NC) cultures cAMP signaling is an instructive signal in catecholaminergic, sympathoadrenal cell development. However, the extracellular signals activating the cAMP pathway during NC cell development have not been identified. We demonstrate that in avian NC cultures, evidenced by tyrosine hydroxylase expression and catecholamine biosynthesis, adenosine and not adrenergic signaling, together with BMP2, promotes sympathoadrenal cell development. In NC cultures, addition of the adenosine receptor agonist NECA in the presence of BMP2 promotes sympathoadrenal cell development, whereas the antagonist CGS 15943 or the adenosine degrading enzyme adenosine deaminase (ADA) suppresses TH expression. Importantly, NC cells express A2A and A2B receptors which couple with Gsalpha increasing intracellular cAMP. Employing the CNS-derived catecholaminergic CAD cell line, we also demonstrate that neuronal differentiation mediated by serum withdrawal is further enhanced by treatment with IBMX, a cAMP-elevating agent, or the adenosine receptor agonist NECA, acting via cAMP. By contrast, the adenosine receptor antagonist CGS 15943 or the adenosine degrading enzyme ADA inhibits CAD cell neuronal differentiation mediated by serum withdrawal. These results support that adenosine is a physiological signal in neuronal differentiation of the CNS-derived catecholaminergic CAD cell line and suggest that adenosine signaling is involved in NC cell development in vivo.

  11. A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2012-01-01

    Full Text Available Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67% is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

  12. Wireless virtualization

    CERN Document Server

    Wen, Heming; Le-Ngoc, Tho

    2013-01-01

    This SpringerBriefs is an overview of the emerging field of wireless access and mobile network virtualization. It provides a clear and relevant picture of the current virtualization trends in wireless technologies by summarizing and comparing different architectures, techniques and technologies applicable to a future virtualized wireless network infrastructure. The readers are exposed to a short walkthrough of the future Internet initiative and network virtualization technologies in order to understand the potential role of wireless virtualization in the broader context of next-generation ubiq

  13. A signal pre-processing algorithm designed for the needs of hardware implementation of neural classifiers used in condition monitoring

    DEFF Research Database (Denmark)

    Dabrowski, Dariusz; Hashemiyan, Zahra; Adamczyk, Jan

    2015-01-01

    Gearboxes have a significant influence on the durability and reliability of a power transmission system. Currently, extensive research studies are being carried out to increase the reliability of gearboxes working in the energy industry, especially with a focus on planetary gears in wind turbines...... is to estimate the features of a vibration signal that are related to failures, e.g. misalignment and unbalance. These features can serve as the components of an input vector for a neural classifier. The approach proposed here has several important benefits: it is resistant to small speed fluctuations up to 7...

  14. A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals.

    Science.gov (United States)

    Cheng, Jun; Park, Ju H; Karimi, Hamid Reza; Shen, Hao

    2017-08-02

    This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure.

  15. Characterization of Apoptosis Signaling Cascades During the Differentiation Process of Human Neural ReNcell VM Progenitor Cells In Vitro.

    Science.gov (United States)

    Jaeger, Alexandra; Fröhlich, Michael; Klum, Susanne; Lantow, Margareta; Viergutz, Torsten; Weiss, Dieter G; Kriehuber, Ralf

    2015-11-01

    Apoptosis is an essential physiological process accompanying the development of the central nervous system and human neurogenesis. However, the time scale and the underlying molecular mechanisms are yet poorly understood. Due to this fact, we investigated the functionality and general inducibility of apoptosis in the human neural ReNcell VM progenitor cell line during differentiation and also after exposure to staurosporine (STS) and ultraviolet B (UVB) irradiation. Transmission light microscopy, flow cytometry, and Western-/Immunoblot analysis were performed to compare proliferating and differentiating, in addition to STS- and UVB-treated cells. In particular, from 24 to 72 h post-initiation of differentiation, G0/G1 cell cycle arrest, increased loss of apoptotic cells, activation of pro-apoptotic BAX, Caspase-3, and cleavage of its substrate PARP were observed during cell differentiation and, to a higher extent, after treatment with STS and UVB. We conclude that redundant or defective cells are eliminated by apoptosis, while otherwise fully differentiated cells were less responsive to apoptosis induction by STS than proliferating cells, likely as a result of reduced APAF-1 expression, and increased levels of BCL-2. These data provide the evidence that apoptotic mechanisms in the neural ReNcell VM progenitor cell line are not only functional, but also inducible by external stimuli like growth factor withdrawal or treatment with STS and UVB, which marks this cell line as a suitable model to investigate apoptosis signaling pathways in respect to the differentiation processes of human neural progenitor cells in vitro.

  16. The Sustained Effect of Emotional Signals on Neural Processing in 12-Month-Olds

    Science.gov (United States)

    Leventon, Jacqueline S.; Bauer, Patricia J.

    2013-01-01

    Around the end of the first year of life, infants develop a social referencing ability -- using emotional information from others to guide their own behavior. Much research on social referencing has focused on changes in behavior in response to emotional information. The present study was an investigation of the changes in neural responses that…

  17. MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes

    Directory of Open Access Journals (Sweden)

    Sergey M Plis

    2010-11-01

    Full Text Available The combined analysis of MEG/EEG and functional MRI measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the BOLD response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater SNR, that confirms the expectation arising from the nature of the experiment. The highly nonlinear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

  18. Linking Users' Subjective QoE Evaluation to Signal Strength in an IEEE 802.11b/g Wireless LAN Environment

    Directory of Open Access Journals (Sweden)

    Lieven De Marez

    2010-01-01

    Full Text Available Although the literature on Quality of Experience (QoE has boomed over the last few years, only a limited number of studies have focused on the relation between objective technical parameters and subjective user-centric indicators of QoE. Building on an overview of the related literature, this paper introduces the use of a software monitoring tool as part of an interdisciplinary approach to QoE measurement. In the presented study, a panel of test users evaluated a mobile web-browsing application (i.e., Wapedia on a PDA in an IEEE 802.11b/g Wireless LAN environment by rating a number of key QoE dimensions on the device immediately after usage. This subjective evaluation was linked to the signal strength, monitored during PDA usage at four different locations in the test environment. The aim of this study is to assess and model the relation between the subjective evaluation of QoE and the (objective signal strength in order to achieve future QoE optimization.

  19. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

    Directory of Open Access Journals (Sweden)

    George J. A. Jiang

    2015-01-01

    Full Text Available Electroencephalogram (EEG signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  20. Augmented BMPRIA-mediated BMP signaling in cranial neural crest lineage leads to cleft palate formation and delayed tooth differentiation.

    Directory of Open Access Journals (Sweden)

    Lu Li

    Full Text Available The importance of BMP receptor Ia (BMPRIa mediated signaling in the development of craniofacial organs, including the tooth and palate, has been well illuminated in several mouse models of loss of function, and by its mutations associated with juvenile polyposis syndrome and facial defects in humans. In this study, we took a gain-of-function approach to further address the role of BMPR-IA-mediated signaling in the mesenchymal compartment during tooth and palate development. We generated transgenic mice expressing a constitutively active form of BmprIa (caBmprIa in cranial neural crest (CNC cells that contributes to the dental and palatal mesenchyme. Mice bearing enhanced BMPRIa-mediated signaling in CNC cells exhibit complete cleft palate and delayed odontogenic differentiation. We showed that the cleft palate defect in the transgenic animals is attributed to an altered cell proliferation rate in the anterior palatal mesenchyme and to the delayed palatal elevation in the posterior portion associated with ectopic cartilage formation. Despite enhanced activity of BMP signaling in the dental mesenchyme, tooth development and patterning in transgenic mice appeared normal except delayed odontogenic differentiation. These data support the hypothesis that a finely tuned level of BMPRIa-mediated signaling is essential for normal palate and tooth development.

  1. SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

    Energy Technology Data Exchange (ETDEWEB)

    Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results

  2. Wireless Internet

    NARCIS (Netherlands)

    el Zarki, M.; Heijenk, Geert; Lee, Kenneth S.; Bidgoli, H.

    This chapter addresses the topic of wireless Internet, the extension of the wireline Internet architecture to the wireless domain. As such the chapter introduces the reader to the dominant characteristics of the Internet, from its structure to the protocols that control the forwarding of data and

  3. Neural recording front-end IC using action potential detection and analog buffer with digital delay for data compression.

    Science.gov (United States)

    Liu, Lei; Yao, Lei; Zou, Xiaodan; Goh, Wang Ling; Je, Minkyu

    2013-01-01

    This paper presents a neural recording analog front-end IC intended for simultaneous neural recording with action potential (AP) detection for data compression in wireless multichannel neural implants. The proposed neural recording front-end IC detects the neural spikes and sends only the preserved AP information for wireless transmission in order to reduce the overall power consumption of the neural implant. The IC consists of a low-noise neural amplifier, an AP detection circuit and an analog buffer with digital delay. The neural amplifier makes use of a current-reuse technique to maximize the transconductance efficiency for attaining a good noise efficiency factor. The AP detection circuit uses an adaptive threshold voltage to generate an enable signal for the subsequent functional blocks. The analog buffer with digital delay is employed using a finite impulse response (FIR) filter which preserves the AP waveform before the enable signal as well as provides low-pass filtering. The neural recording front-end IC has been designed using standard CMOS 0.18-µm technology occupying a core area of 220 µm by 820 µm.

  4. I can't wait! Neural reward signals in impulsive individuals exaggerate the difference between immediate and future rewards.

    Science.gov (United States)

    Schmidt, Barbara; Holroyd, Clay B; Debener, Stefan; Hewig, Johannes

    2017-03-01

    Waiting for rewards is difficult, and highly impulsive individuals with low self-control have an especially hard time with it. Here, we investigated whether neural responses to rewards in a delayed gratification task predict impulsivity and self-control. The EEG was recorded from participants engaged in a guessing game in which on each trial they could win either a large or small reward, paid either now or after 6 months. Ratings confirmed that participants preferred immediate, large rewards over small, delayed rewards. Electrophysiological reward signals reflecting the difference between immediate and future rewards predicted self-report measures of impulsivity and self-control. Further, these signals were highly reliable across two sessions over a 1-week interval, showing high temporal stability like stable personality traits. These results suggest that greater valuation of immediate rewards causes impulsive individuals to redirect control away from delayed rewards, indicating why it is so hard for them to wait. © 2016 Society for Psychophysiological Research.

  5. Optimizing usability and signal capture: a proactive risk assessment for the implementation of a wireless vital sign monitoring system.

    Science.gov (United States)

    Kowalski, Rebecca; Capan, Muge; Lodato, Peter; Mosby, Danielle; Thomas, Tamekia; Arnold, Ryan; Miller, Kristen

    2017-11-01

    Wearable vital sign monitors are a promising step towards optimal patient surveillance, providing continuous data to allow for early detection and treatment of patient deterioration. However, as wearable monitors become more widely adopted in healthcare, there is a corresponding need to carefully design the implementation of these tools to promote their integration into clinical workflows and defend against potential misuse and patient harm. Prior to the roll-out of these monitors, our multidisciplinary team of clinicians, clinical engineers, information technologists and research investigators conducted a modified Healthcare Failure Mode and Effect Analysis (HFMEA), a proactive evaluation of potential problems which could be encountered in the use of a wireless vital signs monitoring system. This evaluation was accomplished by focussing on the identification of procedures and actions that would be required during the devices' regular usage, as well as the implementation of the system as a comprehensive process. Using this method, the team identified challenges that would arise throughout the lifecycle of the device and developed recommendations to address them. This proactive risk assessment can guide the implementation of wearable patient monitors, optimising the use of innovative health information technology.

  6. A CMOS power-efficient low-noise current-mode front-end amplifier for neural signal recording.

    Science.gov (United States)

    Wu, Chung-Yu; Chen, Wei-Ming; Kuo, Liang-Ting

    2013-04-01

    In this paper, a new current-mode front-end amplifier (CMFEA) for neural signal recording systems is proposed. In the proposed CMFEA, a current-mode preamplifier with an active feedback loop operated at very low frequency is designed as the first gain stage to bypass any dc offset current generated by the electrode-tissue interface and to achieve a low high-pass cutoff frequency below 0.5 Hz. No reset signal or ultra-large pseudo resistor is required. The current-mode preamplifier has low dc operation current to enhance low-noise performance and decrease power consumption. A programmable current gain stage is adopted to provide adjustable gain for adaptive signal scaling. A following current-mode filter is designed to adjust the low-pass cutoff frequency for different neural signals. The proposed CMFEA is designed and fabricated in 0.18-μm CMOS technology and the area of the core circuit is 0.076 mm(2). The measured high-pass cutoff frequency is as low as 0.3 Hz and the low-pass cutoff frequency is adjustable from 1 kHz to 10 kHz. The measured maximum current gain is 55.9 dB. The measured input-referred current noise density is 153 fA /√Hz , and the power consumption is 13 μW at 1-V power supply. The fabricated CMFEA has been successfully applied to the animal test for recording the seizure ECoG of Long-Evan rats.

  7. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex.

    Science.gov (United States)

    Lang, Stefan; Dercksen, Vincent J; Sakmann, Bert; Oberlaender, Marcel

    2011-11-01

    The three-dimensional (3D) structure of neural circuits represents an essential constraint for information flow in the brain. Methods to directly monitor streams of excitation, at subcellular and millisecond resolution, are at present lacking. Here, we describe a pipeline of tools that allow investigating information flow by simulating electrical signals that propagate through anatomically realistic models of average neural networks. The pipeline comprises three blocks. First, we review tools that allow fast and automated acquisition of 3D anatomical data, such as neuron soma distributions or reconstructions of dendrites and axons from in vivo labeled cells. Second, we introduce NeuroNet, a tool for assembling the 3D structure and wiring of average neural networks. Finally, we introduce a simulation framework, NeuroDUNE, to investigate structure-function relationships within networks of full-compartmental neuron models at subcellular, cellular and network levels. We illustrate the pipeline by simulations of a reconstructed excitatory network formed between the thalamus and spiny stellate neurons in layer 4 (L4ss) of a cortical barrel column in rat vibrissal cortex. Exciting the ensemble of L4ss neurons with realistic input from an ensemble of thalamic neurons revealed that the location-specific thalamocortical connectivity may result in location-specific spiking of cortical cells. Specifically, a radial decay in spiking probability toward the column borders could be a general feature of signal flow in a barrel column. Our simulations provide insights of how anatomical parameters, such as the subcellular organization of synapses, may constrain spiking responses at the cellular and network levels. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Multiple excitatory and inhibitory neural signals converge to fine-tune Caenorhabditis elegans feeding to food availability

    Science.gov (United States)

    Dallière, Nicolas; Bhatla, Nikhil; Luedtke, Zara; Ma, Dengke K.; Woolman, Jonathan; Walker, Robert J.; Holden-Dye, Lindy; O’Connor, Vincent

    2016-01-01

    How an animal matches feeding to food availability is a key question for energy homeostasis. We addressed this in the nematode Caenorhabditis elegans, which couples feeding to the presence of its food (bacteria) by regulating pharyngeal activity (pumping). We scored pumping in the presence of food and over an extended time course of food deprivation in wild-type and mutant worms to determine the neural substrates of adaptive behavior. Removal of food initially suppressed pumping but after 2 h this was accompanied by intermittent periods of high activity. We show pumping is fine-tuned by context-specific neural mechanisms and highlight a key role for inhibitory glutamatergic and excitatory cholinergic/peptidergic drives in the absence of food. Additionally, the synaptic protein UNC-31 [calcium-activated protein for secretion (CAPS)] acts through an inhibitory pathway not explained by previously identified contributions of UNC-31/CAPS to neuropeptide or glutamate transmission. Pumping was unaffected by laser ablation of connectivity between the pharyngeal and central nervous system indicating signals are either humoral or intrinsic to the enteric system. This framework in which control is mediated through finely tuned excitatory and inhibitory drives resonates with mammalian hypothalamic control of feeding and suggests that fundamental regulation of this basic animal behavior may be conserved through evolution from nematode to human.—Dallière, N., Bhatla, N., Luedtke, Z., Ma, D. K., Woolman, J., Walker, R. J., Holden-Dye, L., O’Connor, V. Multiple excitatory and inhibitory neural signals converge to fine-tune Caenorhabditis elegans feeding to food availability. PMID:26514165

  9. Neural network activation during a stop-signal task discriminates cocaine-dependent from non-drug-abusing men.

    Science.gov (United States)

    Elton, Amanda; Young, Jonathan; Smitherman, Sonet; Gross, Robin E; Mletzko, Tanja; Kilts, Clinton D

    2014-05-01

    Cocaine dependence is defined by a loss of inhibitory control over drug-use behaviors, mirrored by measurable impairments in laboratory tasks of inhibitory control. The current study tested the hypothesis that deficits in multiple subprocesses of behavioral control are associated with reliable neural-processing alterations that define cocaine addiction. While undergoing functional magnetic resonance imaging (fMRI), 38 cocaine-dependent men and 27 healthy control men performed a stop-signal task of motor inhibition. An independent component analysis on fMRI time courses identified task-related neural networks attributed to motor, visual, cognitive and affective processes. The statistical associations of these components with five different stop-signal task conditions were selected for use in a linear discriminant analysis to define a classifier for cocaine addiction from a subsample of 26 cocaine-dependent men and 18 controls. Leave-one-out cross-validation accurately classified 89.5% (39/44; chance accuracy = 26/44 = 59.1%) of subjects with 84.6% (22/26) sensitivity and 94.4% (17/18) specificity. The remaining 12 cocaine-dependent and 9 control men formed an independent test sample, for which accuracy of the classifier was 81.9% (17/21; chance accuracy = 12/21 = 57.1%) with 75% (9/12) sensitivity and 88.9% (8/9) specificity. The cocaine addiction classification score was significantly correlated with a measure of impulsiveness as well as the duration of cocaine use for cocaine-dependent men. The results of this study support the ability of a pattern of multiple neural network alterations associated with inhibitory motor control to define a binary classifier for cocaine addiction. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

  10. Coherent demodulation of microwave signals by using optical heterodyne technique with applications to point to point indoor wireless communications systems

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Juarez, A; Gomez-Colin, M R; Rojas-Hernandez, A G [Universidad de Sonora (Mexico); Zaldivar-Huerta, I E; Aguayo-Rodriguez, G [Instituto Nacional de Astrofisica, Optica y Electronica (Mexico); Rodriguez-Asomoza, J, E-mail: agarcia@cifus.uson.mx [Universidad de las Americas-Puebla (Mexico)

    2011-01-01

    An optical communications system using a couple microstrip antennas for distributing point to point analog TV with coherent demodulation based on optical heterodyne in close vicinity is reported in this paper. In the proposed experimental setup, two optical waves at different wavelengths are mixed and applied to a photodetector. Then a beat signal with a frequency equivalent to the spacing of the two wavelengths is obtained at the output of the photodetector. This signal corresponds to a microwave signal located at 1.25 GHz, which it is used as a microwave carrier in the transmitter and as a local oscillator in the receiver of our optical communication system. The feasibility of this technique is demonstrated transmitting a TV signal of 66-72 MHz.

  11. Canonical Wnt/β-catenin signaling is required for maintenance but not activation of Pitx2 expression in neural crest during eye development.

    Science.gov (United States)

    Zacharias, Amanda L; Gage, Philip J

    2010-12-01

    Pitx2 is a paired-like homeodomain gene that acts as a key regulator of eye development. Despite its significance, upstream regulation of Pitx2 expression during eye development remains incompletely understood. We use neural crest-specific ablation of Ctnnb1 to demonstrate that canonical Wnt signaling is not required for initial activation of Pitx2 in neural crest. However, canonical Wnt signaling is subsequently required to maintain Pitx2 expression in the neural crest. Eye development in Ctnnb1-null mice appears grossly normal early but significant phenotypes emerge following loss of Pitx2 expression. LEF-1 and β-catenin bind Pitx2 promoter sequences in ocular neural crest, indicating a likely direct effect of canonical Wnt signaling on Pitx2 expression. Combining our data with previous reports, we propose a model wherein a sequential code of retinoic acid followed by canonical Wnt signaling are required for activation and maintenance of Pitx2 expression, respectively. Other key transcription factors in the neural crest, including Foxc1, do not require intact canonical Wnt signaling. Copyright © 2010 Wiley-Liss, Inc.

  12. Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals.

    Science.gov (United States)

    Fonseca, André; Boboeva, Vezha; Brederoo, Sanne; Baggio, Giosuè

    2015-04-16

    Converging evidence in neuroscience suggests that syntax and semantics are dissociable in brain space and time. However, it is possible that partly disjoint cortical networks, operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis, we collected EEG data while participants read sentences containing lexical semantic or morphosyntactic anomalies, resulting in N400 and P600 effects, respectively. Next, we reconstructed phase space trajectories from EEG time series, and we measured the complexity of the resulting dynamical orbits using sample entropy - an index of the rate at which the system generates or loses information over time. Disrupting morphosyntactic or lexical semantic processing had opposite effects on sample entropy: it increased in the N400 window for semantic anomalies, and it decreased in the P600 window for morphosyntactic anomalies. These findings point to a fundamental divergence in the neural computations supporting meaning and grammar in language. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

    OpenAIRE

    Chai Tong Yuen; Woo San San; Tan Ching Seong; Mohamed Rizon

    2009-01-01

    A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emot...

  14. Calcium-mediated repression of β-catenin and its transcriptional signaling mediates neural crest cell death in an avian model of fetal alcohol syndrome.

    Science.gov (United States)

    Flentke, George R; Garic, Ana; Amberger, Ed; Hernandez, Marcos; Smith, Susan M

    2011-07-01

    Fetal alcohol syndrome (FAS) is a common birth defect in many societies. Affected individuals have neurodevelopmental disabilities and a distinctive craniofacial dysmorphology. These latter deficits originate during early development from the ethanol-mediated apoptotic depletion of cranial facial progenitors, a population known as the neural crest. We showed previously that this apoptosis is caused because acute ethanol exposure activates G-protein-dependent intracellular calcium within cranial neural crest progenitors, and this calcium transient initiates the cell death. The dysregulated signals that reside downstream of ethanol's calcium transient and effect neural crest death are unknown. Here we show that ethanol's repression of the transcriptional effector β-catenin causes the neural crest losses. Clinically relevant ethanol concentrations (22-78 mM) rapidly deplete nuclear β-catenin from neural crest progenitors, with accompanying losses of β-catenin transcriptional activity and downstream genes that govern neural crest induction, expansion, and survival. Using forced expression studies, we show that β-catenin loss of function (via dominant-negative T cell transcription factor [TCF]) recapitulates ethanol's effects on neural crest apoptosis, whereas β-catenin gain-of-function in ethanol's presence preserves neural crest survival. Blockade of ethanol's calcium transient using Bapta-AM normalizes β-catenin activity and prevents the neural crest losses, whereas ionomycin treatment is sufficient to destabilize β-catenin. We propose that ethanol's repression of β-catenin causes the neural crest losses in this model of FAS. β-Catenin is a novel target for ethanol's teratogenicity. β-Catenin/Wnt signals participate in many developmental events and its rapid and persistent dysregulation by ethanol may explain why the latter is such a potent teratogen. Copyright © 2011 Wiley-Liss, Inc.

  15. The Calcium-Mediated Repression of β-Catenin and Its Transcriptional Signaling Mediates Neural Crest Cell Death in an Avian Model of Fetal Alcohol Syndrome

    Science.gov (United States)

    Flentke, George R.; Garic, Ana; Amberger, Ed; Hernandez, Marcos; Smith, Susan M.

    2016-01-01

    Fetal Alcohol Syndrome (FAS) is a common birth defect in many societies. Affected individuals have neurodevelopmental disabilities and a distinctive craniofacial dysmorphology. These latter deficits originate during early development from the ethanol-mediated apoptotic depletion of cranial facial progenitors, a population known as the neural crest. We showed previously that this apoptosis is caused because acute ethanol exposure activates a G protein-dependent intracellular calcium within cranial neural crest progenitors, and this calcium transient initiates the cell death. The dysregulated signals that reside downstream of ethanol’s calcium transient and effect neural crest death are unknown. Here we show that ethanol’s repression of the transcriptional effector β-catenin causes the neural crest losses. Clinically-relevant ethanol concentrations (22–78 mM) rapidly deplete nuclear β-catenin from neural crest progenitors, with accompanying losses of β-catenin transcriptional activity and downstream genes that govern neural crest induction, expansion and survival. Using forced expression studies we show that β-catenin loss of function (via dominant-negative TCF) recapitulates ethanol’s effects on neural crest apoptosis, whereas β-catenin gain-of-function in ethanol’s presence preserves neural crest survival. Blockade of ethanol’s calcium transient using Bapta-AM normalizes β-catenin activity and prevents the neural crest losses, whereas ionomycin treatment is sufficient to destabilize β-catenin. We propose that ethanol’s repression of β-catenin causes the neural crest losses in this model of FAS. β-Catenin is a novel target for ethanol’s teratogenicity. β-Catenin/Wnt signals participate in many developmental events and its rapid and persistent dysregulation by ethanol may explain why the latter is such a potent teratogen. PMID:21630427

  16. Simultaneous in vivo recording of local brain temperature and electrophysiological signals with a novel neural probe

    Science.gov (United States)

    Fekete, Z.; Csernai, M.; Kocsis, K.; Horváth, Á. C.; Pongrácz, A.; Barthó, P.

    2017-06-01

    Objective. Temperature is an important factor for neural function both in normal and pathological states, nevertheless, simultaneous monitoring of local brain temperature and neuronal activity has not yet been undertaken. Approach. In our work, we propose an implantable, calibrated multimodal biosensor that facilitates the complex investigation of thermal changes in both cortical and deep brain regions, which records multiunit activity of neuronal populations in mice. The fabricated neural probe contains four electrical recording sites and a platinum temperature sensor filament integrated on the same probe shaft within a distance of 30 µm from the closest recording site. The feasibility of the simultaneous functionality is presented in in vivo studies. The probe was tested in the thalamus of anesthetized mice while manipulating the core temperature of the animals. Main results. We obtained multiunit and local field recordings along with measurement of local brain temperature with accuracy of 0.14 °C. Brain temperature generally followed core body temperature, but also showed superimposed fluctuations corresponding to epochs of increased local neural activity. With the application of higher currents, we increased the local temperature by several degrees without observable tissue damage between 34-39 °C. Significance. The proposed multifunctional tool is envisioned to broaden our knowledge on the role of the thermal modulation of neuronal activity in both cortical and deeper brain regions.

  17. Wireless Optofluidic Systems for Programmable In Vivo Pharmacology and Optogenetics.

    Science.gov (United States)

    Jeong, Jae-Woong; McCall, Jordan G; Shin, Gunchul; Zhang, Yihui; Al-Hasani, Ream; Kim, Minku; Li, Shuo; Sim, Joo Yong; Jang, Kyung-In; Shi, Yan; Hong, Daniel Y; Liu, Yuhao; Schmitz, Gavin P; Xia, Li; He, Zhubin; Gamble, Paul; Ray, Wilson Z; Huang, Yonggang; Bruchas, Michael R; Rogers, John A

    2015-07-30

    In vivo pharmacology and optogenetics hold tremendous promise for dissection of neural circuits, cellular signaling, and manipulating neurophysiological systems in awake, behaving animals. Existing neural interface technologies, such as metal cannulas connected to external drug supplies for pharmacological infusions and tethered fiber optics for optogenetics, are not ideal for minimally invasive, untethered studies on freely behaving animals. Here, we introduce wireless optofluidic neural probes that combine ultrathin, soft microfluidic drug delivery with cellular-scale inorganic light-emitting diode (μ-ILED) arrays. These probes are orders of magnitude smaller than cannulas and allow wireless, programmed spatiotemporal control of fluid delivery and photostimulation. We demonstrate these devices in freely moving animals to modify gene expression, deliver peptide ligands, and provide concurrent photostimulation with antagonist drug delivery to manipulate mesoaccumbens reward-related behavior. The minimally invasive operation of these probes forecasts utility in other organ systems and species, with potential for broad application in biomedical science, engineering, and medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. KEAP1-modifying small molecule reveals muted NRF2 signaling responses in neural stem cells from Huntington's disease patients

    Science.gov (United States)

    Quinti, Luisa; Dayalan Naidu, Sharadha; Träger, Ulrike; Chen, Xiqun; Kegel-Gleason, Kimberly; Llères, David; Connolly, Colúm; Chopra, Vanita; Low, Cho; Moniot, Sébastien; Sapp, Ellen; Tousley, Adelaide R.; Vodicka, Petr; Van Kanegan, Michael J.; Kaltenbach, Linda S.; Crawford, Lisa A.; Fuszard, Matthew; Higgins, Maureen; Miller, James R. C.; Farmer, Ruth E.; Potluri, Vijay; Samajdar, Susanta; Meisel, Lisa; Zhang, Ningzhe; Snyder, Andrew; Stein, Ross; Hersch, Steven M.; Ellerby, Lisa M.; Schwarzschild, Michael A.; Steegborn, Clemens; Leavitt, Blair R.; Degterev, Alexei; Tabrizi, Sarah J.; Lo, Donald C.; DiFiglia, Marian; Thompson, Leslie M.; Dinkova-Kostova, Albena T.; Kazantsev, Aleksey G.

    2017-01-01

    The activity of the transcription factor nuclear factor-erythroid 2 p45-derived factor 2 (NRF2) is orchestrated and amplified through enhanced transcription of antioxidant and antiinflammatory target genes. The present study has characterized a triazole-containing inducer of NRF2 and elucidated the mechanism by which this molecule activates NRF2 signaling. In a highly selective manner, the compound covalently modifies a critical stress-sensor cysteine (C151) of the E3 ligase substrate adaptor protein Kelch-like ECH-associated protein 1 (KEAP1), the primary negative regulator of NRF2. We further used this inducer to probe the functional consequences of selective activation of NRF2 signaling in Huntington's disease (HD) mouse and human model systems. Surprisingly, we discovered a muted NRF2 activation response in human HD neural stem cells, which was restored by genetic correction of the disease-causing mutation. In contrast, selective activation of NRF2 signaling potently repressed the release of the proinflammatory cytokine IL-6 in primary mouse HD and WT microglia and astrocytes. Moreover, in primary monocytes from HD patients and healthy subjects, NRF2 induction repressed expression of the proinflammatory cytokines IL-1, IL-6, IL-8, and TNFα. Together, our results demonstrate a multifaceted protective potential of NRF2 signaling in key cell types relevant to HD pathology. PMID:28533375

  19. Energy-efficient digital and wireless IC design for wireless smart sensing

    Science.gov (United States)

    Zhou, Jun; Huang, Xiongchuan; Wang, Chao; Tae-Hyoung Kim, Tony; Lian, Yong

    2017-10-01

    Wireless smart sensing is now widely used in various applications such as health monitoring and structural monitoring. In conventional wireless sensor nodes, significant power is consumed in wirelessly transmitting the raw data. Smart sensing adds local intelligence to the sensor node and reduces the amount of wireless data transmission via on-node digital signal processing. While the total power consumption is reduced compared to conventional wireless sensing, the power consumption of the digital processing becomes as dominant as wireless data transmission. This paper reviews the state-of-the-art energy-efficient digital and wireless IC design techniques for reducing the power consumption of the wireless smart sensor node to prolong battery life and enable self-powered applications.

  20. The obtaining of statistical characteristics of informative features of signals in the Autonomous information systems using neural networks

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2014-01-01

    Full Text Available The article studies a neural network approach to obtain the statistical characteristics of the input vector implementations of signal and noise at ill-conditioned matrices of correlation moments to solve the problems to select and reduce the vector dimensions of informative features at detection and recognition of signals and noise based on regression methods.A scientific novelty is determined by applying neural network algorithms for the efficient solution of problems to select the informative features and determine the parameters of regression algorithms in terms of the degeneracy or ill-conditioned data with unknown expectation and covariance matrices.The article proposes to use a single-layer neural network with no zero weights and activation functions to calculate the initial regression characteristics and the mean-square value error of multiple initial regression representations, which are necessary to justify the selection of informative features, reduce a dimension of sign vectors and implement the regression algorithms. It is shown that when excluding direct links between the inputs and their corresponding neurons, in the training network the weight coefficients of neuron inputs are the coefficients of initial multiple regression, the error meansquare value of multiple initial regression representations is calculated at the outputs of neurons. The article considers conditionality of the problem to calculate the matrix that is inverse one for matrix of correlation moments. It defines a condition number, which characterizes the relative error of stated task.The problem concerning the matrix condition of the correlation moment of informative signal features and noise arises when solving the problem to find the multiple coefficients of initial regression (MCIR and the residual mean-square values of the multiple regression representations. For obtaining the MCIR and finding the residual mean-square values the matrix of correlation moments of

  1. Radio Relays Improve Wireless Products

    Science.gov (United States)

    2009-01-01

    Signal Hill, California-based XCOM Wireless Inc. developed radio frequency micromachine (RF MEMS) relays with a Phase II Small Business Innovation Research (SBIR) contract through NASA?s Jet Propulsion Laboratory. In order to improve satellite communication systems, XCOM produced wireless RF MEMS relays and tunable capacitors that use metal-to-metal contact and have the potential to outperform most semiconductor technologies while using less power. These relays are used in high-frequency test equipment and instrumentation, where increased speed can mean significant cost savings. Applications now also include mainstream wireless applications and greatly improved tactical radios.

  2. Simultaneous generation of independent wired and 60-GHz wireless signals in an integrated WDM-PON-RoF system based on frequency-sextupling and OCS-DPSK modulation.

    Science.gov (United States)

    Zhang, Liang; Hu, Xiaofeng; Cao, Pan; Chang, Qingjiang; Su, Yikai

    2012-06-18

    We propose a simple and cost-effective scheme to integrate a wavelength division multiplexed-passive optical network (WDM-PON) with a 60-GHz radio-over-fiber (RoF) system. In optical line terminal/central station (OLT/CS), 10-GHz electronic devices and single-drive Mach-Zehnder modulators (MZMs) are used to generate 60-GHz wireless signals based on frequency-sextupling and optical carrier suppression-differential phase shift keying (OCS-DPSK) modulation. By designing a new architecture, only N + 1 single-drive MZMs are required for an N-channel WDM-PON-RoF converged system. The proposed scheme is experimentally demonstrated with 1.25-Gb/s independent wired, wireless and upstream data. Error-free performances are achieved for all these signals after transmission of 25-km single mode fiber (SMF).

  3. Introduction to Ultra Wideband for Wireless Communications

    DEFF Research Database (Denmark)

    Nikookar, Homayoun; Prasad, Ramjee

    Ultra Wideband (UWB) Technology is the cutting edge technology for wireless communications with a wide range of applications. In Introduction to Ultra Wideband for Wireless Communications UWB principles and technologies for wireless communications are explained clearly. Key issues such as UWB...... wireless channels, interference, signal processing as well as applications and standardization activities are addressed. Introduction to Ultra Wideband for Wireless Communications provides easy-to-understand material to (graduate) students and researchers working in the field of commercial UWB wireless...... communications. Due to tutorial nature of the book it can also be adopted as a textbook on the subject in the Telecommunications Engineering curriculum. Problems at the end of each chapter extend the reader's understanding of the subject. Introduction to Ultra Wideband for Wireless Communications will aslo...

  4. Introduction to Ultra Wideband for Wireless Communications

    DEFF Research Database (Denmark)

    Nikookar, Homayoun; Prasad, Ramjee

    communications. Due to tutorial nature of the book it can also be adopted as a textbook on the subject in the Telecommunications Engineering curriculum. Problems at the end of each chapter extend the reader's understanding of the subject. Introduction to Ultra Wideband for Wireless Communications will aslo......Ultra Wideband (UWB) Technology is the cutting edge technology for wireless communications with a wide range of applications. In Introduction to Ultra Wideband for Wireless Communications UWB principles and technologies for wireless communications are explained clearly. Key issues such as UWB...... wireless channels, interference, signal processing as well as applications and standardization activities are addressed. Introduction to Ultra Wideband for Wireless Communications provides easy-to-understand material to (graduate) students and researchers working in the field of commercial UWB wireless...

  5. Development of an ex Vivo Method for Multi-unit Recording of Microbiota-Colonic-Neural Signaling in Real Time

    Directory of Open Access Journals (Sweden)

    Maria M. Buckley

    2018-02-01

    Full Text Available Background and Objectives: Bidirectional signaling between the gastrointestinal tract and the brain is vital for maintaining whole-body homeostasis. Moreover, emerging evidence implicates vagal afferent signaling in the modulation of host physiology by microbes, which are most abundant in the colon. This study aims to optimize and advance dissection and recording techniques to facilitate real-time recordings of afferent neural signals originating in the distal colon.New Protocol: This paper describes a dissection technique, which facilitates extracellular electrophysiological recordings from visceral pelvic, spinal and vagal afferent neurons in response to stimulation of the distal colon.Examples of Application: Focal application of 75 mM KCl to a section of distal colon with exposed submucosal or myenteric nerve cell bodies and sensory nerve endings evoked activity in the superior mesenteric plexus and the vagal nerve. Noradrenaline stimulated nerve activity in the superior mesenteric plexus, whereas application of carbachol stimulated vagal nerve activity. Exposure of an ex vivo section of distal colon with an intact colonic mucosa to peptidoglycan, but not lipopolysaccharide, evoked vagal nerve firing.Discussion: Previous studies have recorded vagal signaling evoked by bacteria in the small intestine. The technical advances of this dissection and recording technique facilitates recording of afferent nerve signals evoked in extrinsic sensory pathways by neuromodulatory reagents applied to the distal colon. Moreover, we have demonstrated vagal afferent activation evoked by bacterial products applied to the distal colonic mucosa. This protocol may contribute to our understanding of functional bowel disorders where gut-brain communication is dysfunctional, and facilitate real-time interrogation of microbiota-gut-brain signaling.

  6. Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.

    Science.gov (United States)

    Yang, C; Le Bouquin Jeannes, R; Faucon, G; Wendling, F

    2010-01-01

    Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.

  7. Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals.

    Science.gov (United States)

    Rajchakit, G; Saravanakumar, R; Ahn, Choon Ki; Karimi, Hamid Reza

    2017-02-01

    This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Application of Polynomial Neural Networks to Classification of Acoustic Warfare Signals

    Science.gov (United States)

    1993-04-01

    fcf’𔄁IC nf20 uqe o.’ C eouinq thil b4𔃺*e to yWash’nOntq .1u0tos Svverl’ t. O’at r ;0, rl’ -f’ Of 0ly ,"non s *( O no 4’ W~c’mt 1)$ ’T .c~qOuQ~O ’ llno...Report NOSC TD 1855, Naval Ocean Systems Center, San Diego, May, 1990. [34] Ghosh, J., L. Deuser, and S. Beck, "A neural network based hybrid system

  9. A low-power, low-noise neural-signal amplifier circuit in 90-nm CMOS.

    Science.gov (United States)

    Zarifi, M H; Frounchi, J; Farshchi, S; Judy, J W

    2008-01-01

    A fully-differential low-power low-noise preamplifier for biopotential and neural-recording applications is presented. This design, which has been simulated in a standard 90-nm CMOS process, consumes 30 microW from a 3-V power supply. The simulated integrated input-referred noise is 2.3 microV over 0.1 Hz to 20 kHz. The amplifier also provides an output swing of +/- 0.9 V with a THD of less than 0.1%

  10. Wireless Distributed Antenna MIMO

    DEFF Research Database (Denmark)

    2015-01-01

    The present disclosure relates to system applications of multicore optical fibers. One embodiment relates to a base transceiver station for a wireless telecommunication system comprising a plurality of antenna units arranged in a MIMO configuration and adapted for transmission and/or reception...... of radio-frequency signals, an optical transmitter in the form of an electro-optic conversion unit for each of said plurality of antenna units, each electro-optic conversion unit adapted for converting an RF signal into an optical signal, a plurality of a single core optical fibers for guiding the optical...

  11. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface.

    Science.gov (United States)

    Su, Yi; Routhu, Sudhamayee; Moon, Kee S; Lee, Sung Q; Youm, WooSub; Ozturk, Yusuf

    2016-09-24

    All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

  12. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

    Directory of Open Access Journals (Sweden)

    Yi Su

    2016-09-01

    Full Text Available All neural information systems (NIS rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP oscillation and stimulate the target area at the same time.

  13. Tags, wireless communication systems, tag communication methods, and wireless communications methods

    Science.gov (United States)

    Scott,; Jeff W. , Pratt; Richard, M [Richland, WA

    2006-09-12

    Tags, wireless communication systems, tag communication methods, and wireless communications methods are described. In one aspect, a tag includes a plurality of antennas configured to receive a plurality of first wireless communication signals comprising data from a reader, a plurality of rectifying circuits coupled with. respective individual ones of the antennas and configured to provide rectified signals corresponding to the first wireless communication signals, wherein the rectified signals are combined to produce a composite signal, an adaptive reference circuit configured to vary a reference signal responsive to the composite signal, a comparator coupled with the adaptive reference circuit and the rectifying circuits and configured to compare the composite signal with respect to the reference signal and to output the data responsive to the comparison, and processing circuitry configured to receive the data from the comparator and to process the data.

  14. Milling tool wear diagnosis by feed motor current signal using an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Khajavi, Mehrdad Nouri; Nasernia, Ebrahim; Rostaghi, Mostafa [Dept. of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of)

    2016-11-15

    In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system.

  15. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function.

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal-striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal-striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  16. Neural Correlates of Visual Spatial Attention in Electrocorticographic (ECoG Signals in Humans

    Directory of Open Access Journals (Sweden)

    Aysegul eGunduz

    2011-09-01

    Full Text Available Attention is a cognitive selection mechanism that allocates the limited processing resources of the brain to the sensory streams most relevant to our immediate goals, thereby enhancing responsiveness and behavioral performance. The underlying neural mechanisms of orienting attention are distributedacross a widespread cortical network. While aspects of this network have been extensively studied, details about the electrophysiological dynamics of this network are scarce. In this study, we investigated attentional networks using electrocorticographic (ECoG recordings from the surface ofthe brain, which combine broad spatial coverage with high temporal resolution, in five human subjects. ECoG was recorded when subjects covertly attended to a spatial location and responded to contrast changes in the presence of distractors in a modified Posner cueing task. ECoG amplitudes in the alpha, beta and gamma bands identified neural changes associated with covert attention and motor preparation/execution in the different stages of the task. The results show that attentional engagement was primarily associated with ECoG activity in the visual, prefrontal, premotor, and parietal cortices. Motor preparation/execution was associated with ECoG activity in premotor/sensorimotor cortices. In summary, our results illustrate rich and distributed cortical dynamics that are associated with orienting attention and the subsequent motor preparation and execution. These findings are largely consistent with and expand on primate studies using intracortical recordings and human functional neuroimaging studies.

  17. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Directory of Open Access Journals (Sweden)

    Karuna Subramaniam

    2015-01-01

    Full Text Available Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  18. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I.; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life. PMID:26413478

  19. Helmet Integrated Nanosensors, Signal Processing and Wireless Real Time Data Communication for Monitoring Blast Exposure to Battlefield Personnel

    Science.gov (United States)

    2009-12-01

    2009 IEEE International Conference on Multimedia and Expo, ICME 2009, unpublished. [25] J. Meythaler and T. Novack, “Post traumatic seizures...K.L. Watkin, “Pervasive embedded systems for detection of traumatic brain injury,” Proceedings of IEEE International Conference on Multimedia and...implementation would provide a suitable platform. With the development of mixed signal FPGA platforms such as Fusion from Actel, future work would focus on

  20. Nitric oxide from inflammatory origin impairs neural stem cell proliferation by inhibiting epidermal growth factor receptor signaling

    Directory of Open Access Journals (Sweden)

    Bruno Pereira Carreira

    2014-10-01

    Full Text Available Neuroinflammation is characterized by activation of microglial cells, followed by production of nitric oxide (NO, which may have different outcomes on neurogenesis, favoring or inhibiting this process. In the present study, we investigated how the inflammatory mediator NO can affect proliferation of neural stem cells (NSC, and explored possible mechanisms underlying this effect. We investigated which mechanisms are involved in the regulation of NSC proliferation following treatment with an inflammatory stimulus (LPS plus IFN-γ, using a culture system of subventricular zone (SVZ-derived NSC mixed with microglia cells obtained from wild-type mice (iNOS+/+ or from iNOS knockout mice (iNOS-/-. We found an impairment of NSC cell proliferation in iNOS+/+ mixed cultures, which was not observed in iNOS-/- mixed cultures. Furthermore, the increased release of NO by activated iNOS+/+ microglial cells decreased the activation of the ERK/MAPK signaling pathway, which was concomitant with an enhanced nitration of the EGF receptor. Preventing nitrogen reactive species formation with MnTBAP, a scavenger of peroxynitrite, or using the peroxynitrite degradation catalyst FeTMPyP, cell proliferation and ERK signaling were restored to basal levels in iNOS+/+ mixed cultures. Moreover, exposure to the NO donor NOC-18 (100 µM, for 48 h, inhibited SVZ-derived NSC proliferation. Regarding the antiproliferative effect of NO, we found that NOC-18 caused the impairment of signaling through the ERK/MAPK pathway, which may be related to increased nitration of the EGF receptor in NSC. Using MnTBAP nitration was prevented, maintaining ERK signaling, rescuing NSC proliferation. We show that NO from inflammatory origin leads to a decreased function of the EGF receptor, which compromised proliferation of NSC. We also demonstrated that NO-mediated nitration of the EGF receptor caused a decrease in its phosphorylation, thus preventing regular proliferation signaling through the

  1. R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network

    Science.gov (United States)

    Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.

    2014-01-01

    This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

  2. Multiple excitatory and inhibitory neural signals converge to fine-tune Caenorhabditis elegans feeding to food availability.

    Science.gov (United States)

    Dallière, Nicolas; Bhatla, Nikhil; Luedtke, Zara; Ma, Dengke K; Woolman, Jonathan; Walker, Robert J; Holden-Dye, Lindy; O'Connor, Vincent

    2016-02-01

    How an animal matches feeding to food availability is a key question for energy homeostasis. We addressed this in the nematode Caenorhabditis elegans, which couples feeding to the presence of its food (bacteria) by regulating pharyngeal activity (pumping). We scored pumping in the presence of food and over an extended time course of food deprivation in wild-type and mutant worms to determine the neural substrates of adaptive behavior. Removal of food initially suppressed pumping but after 2 h this was accompanied by intermittent periods of high activity. We show pumping is fine-tuned by context-specific neural mechanisms and highlight a key role for inhibitory glutamatergic and excitatory cholinergic/peptidergic drives in the absence of food. Additionally, the synaptic protein UNC-31 [calcium-activated protein for secretion (CAPS)] acts through an inhibitory pathway not explained by previously identified contributions of UNC-31/CAPS to neuropeptide or glutamate transmission. Pumping was unaffected by laser ablation of connectivity between the pharyngeal and central nervous system indicating signals are either humoral or intrinsic to the enteric system. This framework in which control is mediated through finely tuned excitatory and inhibitory drives resonates with mammalian hypothalamic control of feeding and suggests that fundamental regulation of this basic animal behavior may be conserved through evolution from nematode to human. © FASEB.

  3. E3D hand movement velocity reconstruction using power spectral density of EEG signals and neural network.

    Science.gov (United States)

    Korik, A; Siddique, N; Sosnik, R; Coyle, D

    2015-08-01

    Three dimensional (3D) limb motion trajectory is predictable with a non-invasive brain-computer interface (BCI). To date, most non-invasive motion trajectory prediction BCIs use potential values of electroencephalographic (EEG) signals as the input to a multiple linear regression (mLR) based kinetic data estimator. We investigated the possible improvement in accuracy of 3D hand movement prediction (i.e., the correlation of registered and reconstructed hand velocities) by replacing raw EEG potentials with spectrum power values of specific EEG bands. We also investigated if a non-linear neural network based estimator outperformed the mLR approach. The spectrum power model provided significantly higher accuracy (R~0.60) compared to the similar EEG potentials based approach (R~0.45). Additionally, when replacing the mLR based kinetic data estimation module with a feed-forward neural network (NN) we found the NN based spectrum power model provided higher accuracy (R~0.70) compared to the similar mLR based approach (R~0.60).

  4. Wireless gyroscope platform enabled by a portable media device for quantifying wobble board therapy.

    Science.gov (United States)

    LeMoyne, Robert; Mastroianni, Timothy

    2017-07-01

    The wobble board enables a therapy strategy for rehabilitation of the ankle foot complex. Quantification of therapy, such as through the use of a wobble board, can facilitate a therapist's acuity for advancing and optimizing the overall therapy strategy. The portable media device, such as an iPod, can be equipped with a software application to function as a wireless gyroscope platform. Integration of the wobble board with the portable media device functioning as a wireless gyroscope enables the potential for patient to therapist interaction through connectivity to the Internet. A patient can conduct wobble board therapy for the ankle foot complex from the convenient vantage point of a homebound setting with therapy data transmitted wirelessly as email attachments. The gyroscope signal of the wobble board therapy can be consolidated into a feature set for machine learning classification. Using a multilayer perceptron neural network considerable classification accuracy has been achieved for differentiating between a hemiplegic affected ankle and unaffected ankle while using a wobble board. The combination of machine learning, wireless systems, such as a portable media device functioning as a wireless gyroscope, and a conventional therapy device, such as a wobble board, are envisioned to advance the capability to optimally impact the rehabilitation experience.

  5. States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.

    Science.gov (United States)

    Gläscher, Jan; Daw, Nathaniel; Dayan, Peter; O'Doherty, John P

    2010-05-27

    Reinforcement learning (RL) uses sequential experience with situations ("states") and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior. Copyright 2010 Elsevier Inc. All rights reserved.

  6. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  7. Classification of EMG signals using artificial neural networks for virtual hand prosthesis control.

    Science.gov (United States)

    Mattioli, Fernando E R; Lamounier, Edgard A; Cardoso, Alexandre; Soares, Alcimar B; Andrade, Adriano O

    2011-01-01

    Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.

  8. Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

    Science.gov (United States)

    Mani-Varnosfaderani, Ahmad; Kanginejad, Atefeh; Gilany, Kambiz; Valadkhani, Abolfazl

    2016-10-12

    The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Striatal Activity and Reward Relativity: Neural Signals Encoding Dynamic Outcome Valuation.

    Science.gov (United States)

    Webber, Emily S; Mankin, David E; Cromwell, Howard C

    2016-01-01

    The striatum is a key brain region involved in reward processing. Striatal activity has been linked to encoding reward magnitude and integrating diverse reward outcome information. Recent work has supported the involvement of striatum in the valuation of outcomes. The present work extends this idea by examining striatal activity during dynamic shifts in value that include different levels and directions of magnitude disparity. A novel task was used to produce diverse relative reward effects on a chain of instrumental action. Rats (Rattus norvegicus) were trained to respond to cues associated with specific outcomes varying by food pellet magnitude. Animals were exposed to single-outcome sessions followed by mixed-outcome sessions, and neural activity was compared among identical outcome trials from the different behavioral contexts. Results recording striatal activity show that neural responses to different task elements reflect incentive contrast as well as other relative effects that involve generalization between outcomes or possible influences of outcome variety. The activity that was most prevalent was linked to food consumption and post-food consumption periods. Relative encoding was sensitive to magnitude disparity. A within-session analysis showed strong contrast effects that were dependent upon the outcome received in the immediately preceding trial. Significantly higher numbers of responses were found in ventral striatum linked to relative outcome effects. Our results support the idea that relative value can incorporate diverse relationships, including comparisons from specific individual outcomes to general behavioral contexts. The striatum contains these diverse relative processes, possibly enabling both a higher information yield concerning value shifts and a greater behavioral flexibility.

  10. Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models

    Science.gov (United States)

    Chang, Ni-Bin; Yang, Y. Jeffrey; Imen, Sanaz; Mullon, Lee

    2017-05-01

    Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intra-annual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

  11. Neural cross-correlation and signal decorrelation: insights into coding of auditory space.

    Science.gov (United States)

    Saberi, Kourosh; Petrosyan, Agavni

    2005-07-07

    The auditory systems of humans and many other species use the difference in the time of arrival of acoustic signals at the two ears to compute the lateral position of sound sources. This computation is assumed to initially occur in an assembly of neurons organized along a frequency-by-delay surface. Mathematically, the computations are equivalent to a two-dimensional cross-correlation of the input signals at the two ears, with the position of the peak activity along this surface designating the position of the source in space. In this study, partially correlated signals to the two ears are used to probe the mechanisms for encoding spatial cues in stationary or dynamic (moving) signals. It is demonstrated that a cross-correlation model of the auditory periphery coupled with statistical decision theory can predict the patterns of performance by human subjects for both stationary and motion stimuli as a function of stimulus decorrelation. Implications of these findings for the existence of a unique cortical motion system are discussed.

  12. A hardware model of the auditory periphery to transduce acoustic signals into neural activity

    Directory of Open Access Journals (Sweden)

    Takashi eTateno

    2013-11-01

    Full Text Available To improve the performance of cochlear implants, we have integrated a microdevice into a model of the auditory periphery with the goal of creating a microprocessor. We constructed an artificial peripheral auditory system using a hybrid model in which polyvinylidene difluoride was used as a piezoelectric sensor to convert mechanical stimuli into electric signals. To produce frequency selectivity, the slit on a stainless steel base plate was designed such that the local resonance frequency of the membrane over the slit reflected the transfer function. In the acoustic sensor, electric signals were generated based on the piezoelectric effect from local stress in the membrane. The electrodes on the resonating plate produced relatively large electric output signals. The signals were fed into a computer model that mimicked some functions of inner hair cells, inner hair cell–auditory nerve synapses, and auditory nerve fibers. In general, the responses of the model to pure-tone burst and complex stimuli accurately represented the discharge rates of high-spontaneous-rate auditory nerve fibers across a range of frequencies greater than 1 kHz and middle to high sound pressure levels. Thus, the model provides a tool to understand information processing in the peripheral auditory system and a basic design for connecting artificial acoustic sensors to the peripheral auditory nervous system. Finally, we discuss the need for stimulus control with an appropriate model of the auditory periphery based on auditory brainstem responses that were electrically evoked by different temporal pulse patterns with the same pulse number.

  13. Stochastic resonance can enhance information transmission of supra-threshold neural signals.

    Science.gov (United States)

    Kawaguchi, Minato; Mino, Hiroyuki; Momose, Keiko; Durand, Dominique M

    2009-01-01

    Stochastic resonance (SR) has been shown to improve detection of sub-threshold signals with additive uncor-related background noise, not only in a single hippocampal CA1 neuron model, but in a population of hippocampal CA1 neuron models (Array-Enhanced Stochastic Resonance; AESR). However, most of the information in the CNS is transmitted through supra-threshold signals and the effect of stochastic resonance in neurons on these signals is unknown. Therefore, we investigate through computer simulations whether information transmission of supra-threshold input signal can be improved by uncorrelated noise in a population of hippocampal CA1 neuron models by supra-threshold stochastic resonance (SSR). The mutual information was estimated as an index of information transmission via total and noise entropies from the inter-spike interval (ISI) histograms of the spike trains generated by gathering each of spike trains in a population of hippocampal CA1 neuron models at N = 1, 2, 4, 10, 20 and 50. It was shown that the mutual information was maximized at a specific amplitude of uncorrelated noise, i.e., a typical curve of SR was observed when the number of neurons was greater than 10 with SSR. However, SSR did not affect the information transfer with a small number of neurons. In conclusion, SSR may play an important role in processing information such as memory formation in a population of hippocampal neurons.

  14. Design and implementation of underwater wireless electromagnetic communication system

    Science.gov (United States)

    Wu, Xiangshang; Xue, Wei; Shu, Xin

    2017-08-01

    This paper introduces the design and implementation of the underwater wireless electromagnetic communication system based on the current field theory. The system realizes the wireless transmission of underwater voice signal, and has a good application prospect in underwater short-range wireless communication.

  15. Wireless Transceiver Design for High Velocity Scenarios

    NARCIS (Netherlands)

    Xu, T.

    2013-01-01

    This thesis is dedicated to transceiver designs for high data-rate wireless communication systems with rapidly moving terminals. The challenges are two-fold. On the one hand, more spectral bandwidth of the transmitted signals is required by future wireless systems to obtain higher transmission

  16. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

    Science.gov (United States)

    Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

    2015-01-01

    In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

  17. The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome.

    Science.gov (United States)

    Marcos, J V; Hornero, R; Alvarez, D; Nabney, I T; Del Campo, F; Zamarrón, C

    2010-03-01

    In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO(2)) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO(2) signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.

  18. Noncanonical transforming growth factor β (TGFβ) signaling in cranial neural crest cells causes tongue muscle developmental defects.

    Science.gov (United States)

    Iwata, Jun-ichi; Suzuki, Akiko; Pelikan, Richard C; Ho, Thach-Vu; Chai, Yang

    2013-10-11

    Microglossia is a congenital birth defect in humans and adversely impacts quality of life. In vertebrates, tongue muscle derives from the cranial mesoderm, whereas tendons and connective tissues in the craniofacial region originate from cranial neural crest (CNC) cells. Loss of transforming growth factor β (TGFβ) type II receptor in CNC cells in mice (Tgfbr2(fl/fl);Wnt1-Cre) causes microglossia due to a failure of cell-cell communication between cranial mesoderm and CNC cells during tongue development. However, it is still unclear how TGFβ signaling in CNC cells regulates the fate of mesoderm-derived myoblasts during tongue development. Here we show that activation of the cytoplasmic and nuclear tyrosine kinase 1 (ABL1) cascade in Tgfbr2(fl/fl);Wnt1-Cre mice results in a failure of CNC-derived cell differentiation followed by a disruption of TGFβ-mediated induction of growth factors and reduction of myogenic cell proliferation and differentiation activities. Among the affected growth factors, the addition of fibroblast growth factor 4 (FGF4) and neutralizing antibody for follistatin (FST; an antagonist of bone morphogenetic protein (BMP)) could most efficiently restore cell proliferation, differentiation, and organization of muscle cells in the tongue of Tgfbr2(fl/fl);Wnt1-Cre mice. Thus, our data indicate that CNC-derived fibroblasts regulate the fate of mesoderm-derived myoblasts through TGFβ-mediated regulation of FGF and BMP signaling during tongue development.

  19. Disruption of CXCR4 signaling in pharyngeal neural crest cells causes DiGeorge syndrome-like malformations.

    Science.gov (United States)

    Escot, Sophie; Blavet, Cédrine; Faure, Emilie; Zaffran, Stéphane; Duband, Jean-Loup; Fournier-Thibault, Claire

    2016-02-15

    DiGeorge syndrome (DGS) is a congenital disease causing cardiac outflow tract anomalies, craniofacial dysmorphogenesis, thymus hypoplasia, and mental disorders. It results from defective development of neural crest cells (NCs) that colonize the pharyngeal arches and contribute to lower jaw, neck and heart tissues. Although TBX1 has been identified as the main gene accounting for the defects observed in human patients and mouse models, the molecular mechanisms underlying DGS etiology are poorly identified. The recent demonstrations that the SDF1/CXCR4 axis is implicated in NC chemotactic guidance and impaired in cortical interneurons of mouse DGS models prompted us to search for genetic interactions between Tbx1, Sdf1 (Cxcl12) and Cxcr4 in pharyngeal NCs and to investigate the effect of altering CXCR4 signaling on the ontogeny of their derivatives, which are affected in DGS. Here, we provide evidence that Cxcr4 and Sdf1 are genetically downstream of Tbx1 during pharyngeal NC development and that reduction of CXCR4 signaling causes misrouting of pharyngeal NCs in chick and dramatic morphological alterations in the mandibular skeleton, thymus and cranial sensory ganglia. Our results therefore support the possibility of a pivotal role for the SDF1/CXCR4 axis in DGS etiology. © 2016. Published by The Company of Biologists Ltd.

  20. SEMICONDUCTOR INTEGRATED CIRCUITS: A four-channel microelectronic system for neural signal regeneration

    Science.gov (United States)

    Shushan, Xie; Zhigong, Wang; Xiaoying, Lü; Wenyuan, Li; Haixian, Pan

    2009-12-01

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-gm RTR input and output OPA. The system has been realized in CSMC 0.5-μm CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration.

  1. Lymphovascular and neural regulation of metastasis: Shared tumour signalling pathways and novel therapeutic approaches

    Science.gov (United States)

    Le, C.P.; Karnezis, T.; Achen, M. G.; Stacker, S.A.; Sloan, E.K.

    2014-01-01

    The progression of cancer is supported by a wide variety of non-neoplastic cell types which make up the tumour stroma, including immune cells, endothelial cells, cancer-associated fibroblasts and nerve fibres. These host cells contribute molecular signals that enhance primary tumour growth and provide physical avenues for metastatic dissemination. This article provides an overview of the role of blood vessels, lymphatic vessels and nerve fibres in the tumour microenvironment, and highlights the interconnected molecular signalling pathways that control their development and activation in cancer. Further the review highlights the known pharmacological agents which target these pathways and discusses the potential therapeutic uses of drugs that target angiogenesis, lymphangiogenesis and stress response pathways in the different stages of cancer care. PMID:24267548

  2. Simplest relationship between local field potential and intracellular signals in layered neural tissue.

    Science.gov (United States)

    Chizhov, Anton V; Sanchez-Aguilera, Alberto; Rodrigues, Serafim; de la Prida, Liset Menendez

    2015-12-01

    The relationship between the extracellularly measured electric field potential resulting from synaptic activity in an ensemble of neurons and intracellular signals in these neurons is an important but still open question. Based on a model neuron with a cylindrical dendrite and lumped soma, we derive a formula that substantiates a proportionality between the local field potential and the total somatic transmembrane current that emerges from the difference between the somatic and dendritic membrane potentials. The formula is tested by intra- and extracellular recordings of evoked synaptic responses in hippocampal slices. Additionally, the contribution of different membrane currents to the field potential is demonstrated in a two-population mean-field model. Our formalism, which allows for a simple estimation of unknown dendritic currents directly from somatic measurements, provides an interpretation of the local field potential in terms of intracellularly measurable synaptic signals. It is also applicable to the study of cortical activity using two-compartment neuronal population models.

  3. Introduction to Ultra Wideband for Wireless Communications

    DEFF Research Database (Denmark)

    Nikookar, Homayoun; Prasad, Ramjee

    communications. Due to tutorial nature of the book it can also be adopted as a textbook on the subject in the Telecommunications Engineering curriculum. Problems at the end of each chapter extend the reader's understanding of the subject. Introduction to Ultra Wideband for Wireless Communications will aslo...... wireless channels, interference, signal processing as well as applications and standardization activities are addressed. Introduction to Ultra Wideband for Wireless Communications provides easy-to-understand material to (graduate) students and researchers working in the field of commercial UWB wireless...

  4. Wireless communication devices and movement monitoring methods

    Science.gov (United States)

    Skorpik, James R.

    2006-10-31

    Wireless communication devices and movement monitoring methods are described. In one aspect, a wireless communication device includes a housing, wireless communication circuitry coupled with the housing and configured to communicate wireless signals, movement circuitry coupled with the housing and configured to provide movement data regarding movement sensed by the movement circuitry, and event processing circuitry coupled with the housing and the movement circuitry, wherein the event processing circuitry is configured to process the movement data, and wherein at least a portion of the event processing circuitry is configured to operate in a first operational state having a different power consumption rate compared with a second operational state.

  5. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    Science.gov (United States)

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  6. Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

    Science.gov (United States)

    Clawson, Wesley Patrick

    Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

  7. A Sub-millimeter, Inductively Powered Neural Stimulator

    Directory of Open Access Journals (Sweden)

    Daniel K. Freeman

    2017-11-01

    Full Text Available Wireless neural stimulators are being developed to address problems associated with traditional lead-based implants. However, designing wireless stimulators on the sub-millimeter scale (<1 mm3 is challenging. As device size shrinks, it becomes difficult to deliver sufficient wireless power to operate the device. Here, we present a sub-millimeter, inductively powered neural stimulator consisting only of a coil to receive power, a capacitor to tune the resonant frequency of the receiver, and a diode to rectify the radio-frequency signal to produce neural excitation. By replacing any complex receiver circuitry with a simple rectifier, we have reduced the required voltage levels that are needed to operate the device from 0.5 to 1 V (e.g., for CMOS to ~0.25–0.5 V. This reduced voltage allows the use of smaller receive antennas for power, resulting in a device volume of 0.3–0.5 mm3. The device was encapsulated in epoxy, and successfully passed accelerated lifetime tests in 80°C saline for 2 weeks. We demonstrate a basic proof-of-concept using stimulation with tens of microamps of current delivered to the sciatic nerve in rat to produce a motor response.

  8. Optimization of signal-to-noise ratio for wireless light-emitting diode communication in modern lighting layouts

    Science.gov (United States)

    Azizan, Luqman A.; Ab-Rahman, Mohammad S.; Hassan, Mazen R.; Bakar, A. Ashrif A.; Nordin, Rosdiadee

    2014-04-01

    White light-emitting diodes (LEDs) are predicted to be widely used in domestic applications in the future, because they are becoming widespread in commercial lighting applications. The ability of LEDs to be modulated at high speeds offers the possibility of using them as sources for communication instead of illumination. The growing interest in using these devices for both illumination and communication requires attention to combine this technology with modern lighting layouts. A dual-function system is applied to three models of modern lighting layouts: the hybrid corner lighting layout (HCLL), the hybrid wall lighting layout (HWLL), and the hybrid edge lighting layout (HELL). Based on the analysis, the relationship between the space adversity and the signal-to-noise ratio (SNR) performance is demonstrated for each model. The key factor that affects the SNR performance of visible light communication is the reliance on the design parameter that is related to the number and position of LED lights. The model of HWLL is chosen as the best layout, since 61% of the office area is considered as an excellent communication area and the difference between the area classification, Δp, is 22%. Thus, this system is applicable to modern lighting layouts.

  9. Wireless Communications

    Science.gov (United States)

    1991-01-01

    A technology utilization project led to the commercial adaptation of a Space Shuttle Orbiter wireless infrared voice communications system. The technology was adapted to a LAN system by Wilton Industries, one of the participants. Because the system is cable-free, installation charges are saved, and it can be used where cable is impractical. Resultant products include the IRplex 6000. Transceivers can be located anywhere and can include mobile receivers. The system provides wireless LAN coverage up to 44,000 square feet. applications include stock exchange communications, trade shows, emergency communications, etc.

  10. AKT signaling mediates IGF-I survival actions on otic neural progenitors.

    Directory of Open Access Journals (Sweden)

    Maria R Aburto

    Full Text Available BACKGROUND: Otic neurons and sensory cells derive from common progenitors whose transition into mature cells requires the coordination of cell survival, proliferation and differentiation programmes. Neurotrophic support and survival of post-mitotic otic neurons have been intensively studied, but the bases underlying the regulation of programmed cell death in immature proliferative otic neuroblasts remains poorly understood. The protein kinase AKT acts as a node, playing a critical role in controlling cell survival and cell cycle progression. AKT is activated by trophic factors, including insulin-like growth factor I (IGF-I, through the generation of the lipidic second messenger phosphatidylinositol 3-phosphate by phosphatidylinositol 3-kinase (PI3K. Here we have investigated the role of IGF-dependent activation of the PI3K-AKT pathway in maintenance of otic neuroblasts. METHODOLOGY/PRINCIPAL FINDINGS: By using a combination of organotypic cultures of chicken (Gallus gallus otic vesicles and acoustic-vestibular ganglia, Western blotting, immunohistochemistry and in situ hybridization, we show that IGF-I-activation of AKT protects neural progenitors from programmed cell death. IGF-I maintains otic neuroblasts in an undifferentiated and proliferative state, which is characterised by the upregulation of the forkhead box M1 (FoxM1 transcription factor. By contrast, our results indicate that post-mitotic p27(Kip-positive neurons become IGF-I independent as they extend their neuronal processes. Neurons gradually reduce their expression of the Igf1r, while they increase that of the neurotrophin receptor, TrkC. CONCLUSIONS/SIGNIFICANCE: Proliferative otic neuroblasts are dependent on the activation of the PI3K-AKT pathway by IGF-I for survival during the otic neuronal progenitor phase of early inner ear development.

  11. Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal.

    Science.gov (United States)

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2014-12-01

    Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov-Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Lithium promotes neural precursor cell proliferation: evidence for the involvement of the non-canonical GSK-3β-NF-AT signaling

    Directory of Open Access Journals (Sweden)

    Qu Zhaoxia

    2011-05-01

    Full Text Available Abstract Lithium, a drug that has long been used to treat bipolar disorder and some other human pathogenesis, has recently been shown to stimulate neural precursor growth. However, the involved mechanism is not clear. Here, we show that lithium induces proliferation but not survival of neural precursor cells. Mechanistic studies suggest that the effect of lithium mainly involved activation of the transcription factor NF-AT and specific induction of a subset of proliferation-related genes. While NF-AT inactivation by specific inhibition of its upstream activator calcineurin antagonized the effect of lithium on the proliferation of neural precursor cells, specific inhibition of the NF-AT inhibitor GSK-3β, similar to lithium treatment, promoted neural precursor cell proliferation. One important function of lithium appeared to increase inhibitory phosphorylation of GSK-3β, leading to GSK-3β suppression and subsequent NF-AT activation. Moreover, lithium-induced proliferation of neural precursor cells was independent of its role in inositol depletion. These findings not only provide mechanistic insights into the clinical effects of lithium, but also suggest an alternative therapeutic strategy for bipolar disorder and other neural diseases by targeting the non-canonical GSK-3β-NF-AT signaling.

  13. Three Tctn proteins are functionally conserved in the regulation of neural tube patterning and Gli3 processing but not ciliogenesis and Hedgehog signaling in the mouse.

    Science.gov (United States)

    Wang, Chengbing; Li, Jia; Meng, Qing; Wang, Baolin

    2017-10-01

    Tctn1, Tctn2, and Tctn3 are membrane proteins that localize at the transition zone of primary cilia. Tctn1 and Tctn2 mutations have been reported in both humans and mice, but Tctn3 mutations have been reported only in humans. It is also not clear whether the three Tctn proteins are functionally conserved with respect to ciliogenesis and Hedgehog (Hh) signaling. In the present study, we report that loss of Tctn3 gene function in mice results in a decrease in ciliogenesis and Hh signaling. Consistent with this, Tctn3 mutant mice exhibit holoprosencephaly and randomized heart looping and lack the floor plate in the neural tube, the phenotypes similar to those of Tctn1 and Tctn2 mutants. We also show that overexpression of Tctn3, but not Tctn1 or Tctn2, can rescue ciliogenesis in Tctn3 mutant cells. Similarly, replacement of Tctn3 with Tctn1 or Tctn2 in the Tctn3 gene locus results in reduced ciliogenesis and Hh signaling, holoprosencephaly, and randomized heart looping. Surprisingly, however, the neural tube patterning and the proteolytic processing of Gli3 (a transcription regulator for Hh signaling) into a repressor, both of which are usually impaired in ciliary gene mutants, are normal. These results suggest that Tctn1, Tctn2, and Tctn3 are functionally divergent with respect to their role in ciliogenesis and Hh signaling but conserved in neural tube patterning and Gli3 processing. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. SOX1 links the function of neural patterning and Notch signalling in the ventral spinal cord during the neuron-glial fate switch

    Energy Technology Data Exchange (ETDEWEB)

    Genethliou, Nicholas; Panayiotou, Elena [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia (Cyprus); Panayi, Helen; Orford, Michael; Mean, Richard; Lapathitis, George; Gill, Herman; Raoof, Sahir [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Gasperi, Rita De; Elder, Gregory [James J. Peters VA Medical Center, Research and Development (3F22), 130 West Kingsbridge Road, Bronx, NY 10468 (United States); Kessaris, Nicoletta; Richardson, William D. [Wolfson Institute for Biomedical Research and Research Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT (United Kingdom); Malas, Stavros, E-mail: smalas@cing.ac.cy [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia (Cyprus)

    2009-12-25

    During neural development the transition from neurogenesis to gliogenesis, known as the neuron-glial ({Nu}/G) fate switch, requires the coordinated function of patterning factors, pro-glial factors and Notch signalling. How this process is coordinated in the embryonic spinal cord is poorly understood. Here, we demonstrate that during the N/G fate switch in the ventral spinal cord (vSC) SOX1 links the function of neural patterning and Notch signalling. We show that, SOX1 expression in the vSC is regulated by PAX6, NKX2.2 and Notch signalling in a domain-specific manner. We further show that SOX1 regulates the expression of Hes1 and that loss of Sox1 leads to enhanced production of oligodendrocyte precursors from the pMN. Finally, we show that Notch signalling functions upstream of SOX1 during this fate switch and is independently required for the acquisition of the glial fate perse by regulating Nuclear Factor I A expression in a PAX6/SOX1/HES1/HES5-independent manner. These data integrate functional roles of neural patterning factors, Notch signalling and SOX1 during gliogenesis.

  15. Distinct steps of neural induction revealed by Asterix, Obelix and TrkC, genes induced by different signals from the organizer.

    Directory of Open Access Journals (Sweden)

    Sonia Pinho

    2011-04-01

    Full Text Available The amniote organizer (Hensen's node can induce a complete nervous system when grafted into a peripheral region of a host embryo. Although BMP inhibition has been implicated in neural induction, non-neural cells cannot respond to BMP antagonists unless previously exposed to a node graft for at least 5 hours before BMP inhibitors. To define signals and responses during the first 5 hours of node signals, a differential screen was conducted. Here we describe three early response genes: two of them, Asterix and Obelix, encode previously undescribed proteins of unknown function but Obelix appears to be a nuclear RNA-binding protein. The third is TrkC, a neurotrophin receptor. All three genes are induced by a node graft within 4-5 hours but they differ in the extent to which they are inducible by FGF: FGF is both necessary and sufficient to induce Asterix, sufficient but not necessary to induce Obelix and neither sufficient nor necessary for induction of TrkC. These genes are also not induced by retinoic acid, Noggin, Chordin, Dkk1, Cerberus, HGF/SF, Somatostatin or ionomycin-mediated Calcium entry. Comparison of the expression and regulation of these genes with other early neural markers reveals three distinct "epochs", or temporal waves, of gene expression accompanying neural induction by a grafted organizer, which are mirrored by specific stages of normal neural plate development. The results are consistent with neural induction being a cascade of responses elicited by different signals, culminating in the formation of a patterned nervous system.

  16. Neural coding merges sex and habitat chemosensory signals in an insect herbivore.

    Science.gov (United States)

    Trona, Federica; Anfora, Gianfranco; Balkenius, Anna; Bengtsson, Marie; Tasin, Marco; Knight, Alan; Janz, Niklas; Witzgall, Peter; Ignell, Rickard

    2013-06-07

    Understanding the processing of odour mixtures is a focus in olfaction research. Through a neuroethological approach, we demonstrate that different odour types, sex and habitat cues are coded together in an insect herbivore. Stronger flight attraction of codling moth males, Cydia pomonella, to blends of female sex pheromone and plant odour, compared with single compounds, was corroborated by functional imaging of the olfactory centres in the insect brain, the antennal lobes (ALs). The macroglomerular complex (MGC) in the AL, which is dedicated to pheromone perception, showed an enhanced response to blends of pheromone and plant signals, whereas the response in glomeruli surrounding the MGC was suppressed. Intracellular recordings from AL projection neurons that transmit odour information to higher brain centres, confirmed this synergistic interaction in the MGC. These findings underscore that, in nature, sex pheromone and plant odours are perceived as an ensemble. That mating and habitat cues are coded as blends in the MGC of the AL highlights the dual role of plant signals in habitat selection and in premating sexual communication. It suggests that the MGC is a common target for sexual and natural selection in moths, facilitating ecological speciation.

  17. Ascorbic acid alters cell fate commitment of human neural progenitors in a WNT/β-catenin/ROS signaling dependent manner.

    Science.gov (United States)

    Rharass, Tareck; Lantow, Margareta; Gbankoto, Adam; Weiss, Dieter G; Panáková, Daniela; Lucas, Stéphanie

    2017-10-16

    Improving the neuronal yield from in vitro cultivated neural progenitor cells (NPCs) is an essential challenge in transplantation therapy in neurological disorders. In this regard, Ascorbic acid (AA) is widely used to expand neurogenesis from NPCs in cultures although the mechanisms of its action remain unclear. Neurogenesis from NPCs is regulated by the redox-sensitive WNT/β-catenin signaling pathway. We therefore aimed to investigate how AA interacts with this pathway and potentiates neurogenesis. Effects of 200 μM AA were compared with the pro-neurogenic reagent and WNT/β-catenin signaling agonist lithium chloride (LiCl), and molecules with antioxidant activities i.e. N-acetyl-L-cysteine (NAC) and ruthenium red (RuR), in differentiating neural progenitor ReNcell VM cells. Cells were supplemented with reagents for two periods of treatment: a full period encompassing the whole differentiation process versus an early short period that is restricted to the cell fate commitment stage. Intracellular redox balance and reactive oxygen species (ROS) metabolism were examined by flow cytometry using redox and ROS sensors. Confocal microscopy was performed to assess cell viability, neuronal yield, and levels of two proteins: Nucleoredoxin (NXN) and the WNT/β-catenin signaling component Dishevelled 2 (DVL2). TUBB3 and MYC gene responses were evaluated by quantitative real-time PCR. DVL2-NXN complex dissociation was measured by fluorescence resonance energy transfer (FRET). In contrast to NAC which predictably exhibited an antioxidant effect, AA treatment enhanced ROS metabolism with no cytotoxic induction. Both drugs altered ROS levels only at the early stage of the differentiation as no changes were held beyond the neuronal fate commitment stage. FRET studies showed that AA treatment accelerated the redox-dependent release of the initial pool of DVL2 from its sequestration by NXN, while RuR treatment hampered the dissociation of the two proteins. Accordingly, AA

  18. Wireless Power Transfer Strategies for Implantable Bioelectronics: Methodological Review.

    Science.gov (United States)

    Agarwal, Kush; Jegadeesan, Rangarajan; Guo, Yong-Xin; Thakor, Nitish V

    2017-03-16

    Neural implants have emerged over the last decade as highly effective solutions for the treatment of dysfunctions and disorders of the nervous system. These implants establish a direct, often bidirectional, interface to the nervous system, both sensing neural signals and providing therapeutic treatments. As a result of the technological progress and successful clinical demonstrations, completely implantable solutions have become a reality and are now commercially available for the treatment of various functional disorders. Central to this development is the wireless power transfer (WPT) that has enabled implantable medical devices (IMDs) to function for extended durations in mobile subjects. In this review, we present the theory, link design, and challenges, along with their probable solutions for the traditional near-field resonant inductively coupled WPT, capacitively coupled short ranged WPT, and more recently developed ultrasonic, mid-field and far-field coupled WPT technologies for implantable applications. A comparison of various power transfer methods, based on their power budgets and WPT range follows. Power requirements of specific implants like cochlear, retinal, cortical and peripheral are also considered and currently available IMD solutions are discussed. Patient's safety concerns with respect to electrical, biological, physical, electromagnetic interference and cyber security from an implanted neurotech device are also explored in this review. Finally, we discuss and anticipate future developments that will enhance the capabilities of current-day wirelessly powered implants and make them more efficient and integrable with other electronic components in IMDs.

  19. Wireless Seismometer for Venus

    Science.gov (United States)

    Ponchak, George E.; Scardelletti, Maximilian C.; Taylor, Brandt; Beard, Steve; Clougherty, Brian; Meredith, Roger D.; Beheim, Glenn M.; Kiefer, Walter S.; Hunter, Gary W.

    2014-01-01

    Measuring the seismic activity of Venus is critical to understanding its composition and interior dynamics. Because Venus has an average surface temperature of 462 C and the challenge of providing cooling to multiple seismometers, a high temperature, wireless sensor using a wide bandgap semiconductor is an attractive option. This paper presents progress towards a seismometer sensor with wireless capabilities for Venus applications. A variation in inductance of a coil caused by a 1 cm movement of a ferrite probe held in the coil and attached to a balanced leaf-spring seismometer causes a variation of 80 MHz in the transmitted signal from the oscillator sensor system at 420 C, which correlates to a 10 kHz mm sensitivity when the ferrite probe is located at the optimum location in the coil.

  20. USP9X deubiquitylating enzyme maintains RAPTOR protein levels, mTORC1 signalling and proliferation in neural progenitors

    OpenAIRE

    Caitlin R. Bridges; Men-Chee Tan; Susitha Premarathne; Devathri Nanayakkara; Bernadette Bellette; Dusan Zencak; Deepti Domingo; Jozef Gecz; Mariyam Murtaza; Jolly, Lachlan A.; Wood, Stephen A.

    2017-01-01

    USP9X, is highly expressed in neural progenitors and, essential for neural development in mice. In humans, mutations in USP9X are associated with neurodevelopmental disorders. To understand USP9X?s role in neural progenitors, we studied the effects of altering its expression in both the human neural progenitor cell line, ReNcell VM, as well as neural stem and progenitor cells derived from Nestin-cre conditionally deleted Usp9x mice. Decreasing USP9X resulted in ReNcell VM cells arresting in G...

  1. PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans.

    Science.gov (United States)

    Barrios, Arantza; Ghosh, Rajarshi; Fang, Chunhui; Emmons, Scott W; Barr, Maureen M

    2012-12-01

    Appetitive behaviors require complex decision making that involves the integration of environmental stimuli and physiological needs. C. elegans mate searching is a male-specific exploratory behavior regulated by two competing needs: food and reproductive appetite. We found that the pigment dispersing factor receptor (PDFR-1) modulates the circuit that encodes the male reproductive drive that promotes male exploration following mate deprivation. PDFR-1 and its ligand, PDF-1, stimulated mate searching in the male, but not in the hermaphrodite. pdf-1 was required in the gender-shared interneuron AIM, and the receptor acted in internal and external environment-sensing neurons of the shared nervous system (URY, PQR and PHA) to produce mate-searching behavior. Thus, the pdf-1 and pdfr-1 pathway functions in non-sex-specific neurons to produce a male-specific, goal-oriented exploratory behavior. Our results indicate that secretin neuropeptidergic signaling is involved in regulating motivational internal states.

  2. Design and measurements of 64-channel ASIC for neural signal recording.

    Science.gov (United States)

    Kmon, P; Zoladz, M; Grybos, P; Szczygiel, R

    2009-01-01

    This paper presents the design and measurements of a low noise multi-channel front-end electronics for recording extra-cellular neuronal signals using microelectrode arrays. The integrated circuit contains 64 readout channels and was fabricated in CMOS 0.18 microm technology. A single readout channel is built of an AC coupling circuit at the input, a low noise preamplifier, a band-pass filter and a second amplifier. In order to reduce the number of output lines, the 64 analog signals from readout channels are multiplexed to a single output by an analog multiplexer. The chip is optimized for low noise and matching performance with the possibility of cut-off frequencies tuning. The low cut-off frequency can be tuned in the 1 Hz-60 Hz range and the high cut-off frequency can be tuned in the 3.5 kHz-15 kHz range. For the nominal gain setting at 44 dB and power dissipation per single channel of 220 microW the equivalent input noise is in the range from 6 microV-11 microV rms depending on the band-pass filter settings. The chip has good uniformity concerning the spread of its electrical parameters from channel to channel. The spread of gain calculated as standard deviation to mean value is about 4.4% and the spread of the low cut-off frequency is on the same level. The chip occupies 5x2.3 mm(2) of silicon area.

  3. Learning contrast-invariant cancellation of redundant signals in neural systems.

    Directory of Open Access Journals (Sweden)

    Jorge F Mejias

    Full Text Available Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

  4. When the brain simulates stopping: Neural activity recorded during real and imagined stop-signal tasks.

    Science.gov (United States)

    González-Villar, Alberto J; Bonilla, F Mauricio; Carrillo-de-la-Peña, María T

    2016-10-01

    It has been suggested that mental rehearsal activates brain areas similar to those activated by real performance. Although inhibition is a key function of human behavior, there are no previous reports of brain activity during imagined response cancellation. We analyzed event-related potentials (ERPs) and time-frequency data associated with motor execution and inhibition during real and imagined performance of a stop-signal task. The ERPs characteristic of stop trials-that is, the stop-N2 and stop-P3-were also observed during covert performance of the task. Imagined stop (IS) trials yielded smaller stop-N2 amplitudes than did successful stop (SS) and unsuccessful stop (US) trials, but midfrontal theta power similar to that in SS trials. The stop-P3 amplitude for IS was intermediate between those observed for SS and US. The results may be explained by the absence of error-processing and correction processes during imagined performance. For go trials, real execution was associated with higher mu and beta desynchronization over motor areas, which confirms previous reports of lower motor activation during imagined execution and also with larger P3b amplitudes, probably indicating increased top-down attention to the real task. The similar patterns of activity observed for imagined and real performance suggest that imagination tasks may be useful for training inhibitory processes. Nevertheless, brain activation was generally weaker during mental rehearsal, probably as a result of the reduced engagement of top-down mechanisms and limited error processing.

  5. Notch signaling and proneural genes work together to control the neural building blocks for the initial scaffold in the hypothalamus

    Science.gov (United States)

    Ware, Michelle; Hamdi-Rozé, Houda; Dupé, Valérie

    2014-01-01

    The vertebrate embryonic prosencephalon gives rise to the hypothalamus, which plays essential roles in sensory information processing as well as control of physiological homeostasis and behavior. While patterning of the hypothalamus has received much attention, initial neurogenesis in the developing hypothalamus has mostly been neglected. The first differentiating progenitor cells of the hypothalamus will give rise to neurons that form the nucleus of the tract of the postoptic commissure (nTPOC) and the nucleus of the mammillotegmental tract (nMTT). The formation of these neuronal populations has to be highly controlled both spatially and temporally as these tracts will form part of the ventral longitudinal tract (VLT) and act as a scaffold for later, follower axons. This review will cumulate and summarize the existing data available describing initial neurogenesis in the vertebrate hypothalamus. It is well-known that the Notch signaling pathway through the inhibition of proneural genes is a key regulator of neurogenesis in the vertebrate central nervous system. It has only recently been proposed that loss of Notch signaling in the developing chick embryo causes an increase in the number of neurons in the hypothalamus, highlighting an early function of the Notch pathway during hypothalamus formation. Further analysis in the chick and mouse hypothalamus confirms the expression of Notch components and Ascl1 before the appearance of the first differentiated neurons. Many newly identified proneural target genes were also found to be expressed during neuronal differentiation in the hypothalamus. Given the critical role that hypothalamic neural circuitry plays in maintaining homeostasis, it is particularly important to establish the targets downstream of this Notch/proneural network. PMID:25520625

  6. Cadherin-6B stimulates an epithelial mesenchymal transition and the delamination of cells from the neural ectoderm via LIMK/cofilin mediated non-canonical BMP receptor signaling

    Science.gov (United States)

    Park, Ki-Sook; Gumbiner, Barry M.

    2012-01-01

    We previously provided evidence that cadherin-6B induces de-epithelialization of the neural crest prior to delamination and is required for the overall epithelial mesenchymal transition (EMT). Furthermore, de-epithelialization induced by cadherin-6B was found to be mediated by BMP receptor signaling independent of BMP. We now find that de-epithelialization is mediated by non-canonical BMP signaling through the BMP type II receptor (BMPRII) and not by canonical Smad dependent signaling through BMP Type I receptor. The LIM kinase/cofilin pathway mediates non-canonical BMPRII induced de-epithelialization, in response to either cadherin-6B or BMP. LIMK1 induces de-epithelialization in the neural tube and dominant negative LIMK1 decreases de-epithelialization induced by either cadherin-6B or BMP. Cofilin is the major known LIMK1 target and a S3A phosphorylation deficient mutated cofilin inhibits de-epithelialization induced by cadherin-6B as well as LIMK1. Importantly, LIMK1 as well as cadherin-6B can trigger ectopic delamination when co-expressed with the competence factor SOX9, showing that this cadherin-6B stimulated signaling pathway can mediate the full EMT in the appropriate context. These findings suggest that the de-epithelialization step of the neural crest EMT by cadherin-6B/BMPRII involves regulation of actin dynamics via LIMK/cofilin. PMID:22537493

  7. Neural Substrates of Social Emotion Regulation: A fMRI Study on Imitation and Expressive Suppression to Dynamic Facial Signals

    Directory of Open Access Journals (Sweden)

    Pascal eVrticka

    2013-02-01

    Full Text Available Emotion regulation is crucial for successfully engaging in social interactions. Yet, little is known about the neural mechanisms controlling behavioral responses to emotional expressions perceived in the face of other people, which constitute a key element of interpersonal communication. Here, we investigated brain systems involved in social emotion perception and regulation, using functional magnetic resonance imaging (fMRI in 20 healthy participants who saw dynamic facial expressions of either happiness or sadness, and were asked to either imitate the expression or to suppress any expression on their own face (in addition to a gender judgment control task. fMRI results revealed higher activity in regions associated with emotion (e.g., the insula, motor function (e.g., motor cortex, and theory of mind during imitation. Activity in dorsal cingulate cortex was also increased during imitation, possibly reflecting greater action monitoring or conflict with own feeling states. In addition, premotor regions were more strongly activated during both imitation and suppression, suggesting a recruitment of motor control for both the production and inhibition of emotion expressions. Expressive suppression produced increases in dorsolateral and lateral prefrontal cortex typically related to cognitive control. These results suggest that voluntary imitation and expressive suppression modulate brain responses to emotional signals perceived from faces, by up- and down-regulating activity in distributed subcortical and cortical networks that are particularly involved in emotion, action monitoring, and cognitive control.

  8. Chondroitin sulfate proteoglycans regulate the growth, differentiation and migration of multipotent neural precursor cells through the integrin signaling pathway

    Directory of Open Access Journals (Sweden)

    Lü He-Zuo

    2009-10-01

    Full Text Available Abstract Background Neural precursor cells (NPCs are defined by their ability to proliferate, self-renew, and retain the potential to differentiate into neurons and glia. Deciphering the factors that regulate their behaviors will greatly aid in their use as potential therapeutic agents or targets. Chondroitin sulfate proteoglycans (CSPGs are prominent components of the extracellular matrix (ECM in the central nervous system (CNS and are assumed to play important roles in controlling neuronal differentiation and development. Results In the present study, we demonstrated that CSPGs were constitutively expressed on the NPCs isolated from the E16 rat embryonic brain. When chondroitinase ABC was used to abolish the function of endogenous CSPGs on NPCs, it induced a series of biological responses including the proliferation, differentiation and migration of NPCs, indicating that CSPGs may play a critical role in NPC development and differentiation. Finally, we provided evidence suggesting that integrin signaling pathway may be involved in the effects of CSPGs on NPCs. Conclusion The present study investigating the influence and mechanisms of CSPGs on the differentiation and migration of NPCs should help us to understand the basic biology of NPCs during CNS development and provide new insights into developing new strategies for the treatment of the neurological disorders in the CNS.

  9. The Dlx5-FGF10 signaling cascade controls cranial neural crest and myoblast interaction during oropharyngeal patterning and development.

    Science.gov (United States)

    Sugii, Hideki; Grimaldi, Alexandre; Li, Jingyuan; Parada, Carolina; Vu-Ho, Thach; Feng, Jifan; Jing, Junjun; Yuan, Yuan; Guo, Yuxing; Maeda, Hidefumi; Chai, Yang

    2017-11-01

    Craniofacial development depends on cell-cell interactions, coordinated cellular movement and differentiation under the control of regulatory gene networks, which include the distal-less (Dlx) gene family. However, the functional significance of Dlx5 in patterning the oropharyngeal region has remained unknown. Here, we show that loss of Dlx5 leads to a shortened soft palate and an absence of the levator veli palatini, palatopharyngeus and palatoglossus muscles that are derived from the 4th pharyngeal arch (PA); however, the tensor veli palatini, derived from the 1st PA, is unaffected. Dlx5-positive cranial neural crest (CNC) cells are in direct contact with myoblasts derived from the pharyngeal mesoderm, and Dlx5 disruption leads to altered proliferation and apoptosis of CNC and muscle progenitor cells. Moreover, the FGF10 pathway is downregulated in Dlx5-/- mice, and activation of FGF10 signaling rescues CNC cell proliferation and myogenic differentiation in these mutant mice. Collectively, our results indicate that Dlx5 plays crucial roles in the patterning of the oropharyngeal region and development of muscles derived from the 4th PA mesoderm in the soft palate, likely via interactions between CNC-derived and myogenic progenitor cells. © 2017. Published by The Company of Biologists Ltd.

  10. High capacity wireless data links in the W-band using hybrid photonics-electronic techniques for signal generation and detection

    DEFF Research Database (Denmark)

    Vegas Olmos, Juan José; Tafur Monroy, Idelfonso

    2014-01-01

    Seamless convergence of fiber-optic and the wireless networks is of great interest for enabling transparent delivery of broadband services to users in different locations, including both metropolitan and rural areas. Current demand of bandwidth by end-users, especially using mobile devices......, is seeding the need to use bands located at the millimeter-wave region (30–300 GHz), mainly because of its inherent broadband nature. In our lab, we have conducted extensive research on high-speed photonic-wireless links in the V-band (50–75GHz) and the W-band (75–110GHz). In this paper, we will present our...

  11. Thyroid hormone signaling: Contribution to neural function, cognition, and relationship to nicotine

    Science.gov (United States)

    Leach, Prescott T.; Gould, Thomas J.

    2015-01-01

    Cigarette smoking is common despite its adverse effects on health, such as cardiovascular disease and stroke. Understanding the mechanisms that contribute to the addictive properties of nicotine makes it possible to target them to prevent the initiation of smoking behavior and/or increase the chance of successful quit attempts. While highly addictive, nicotine is not generally considered to be as reinforcing as other drugs of abuse. There are likely other mechanisms at work that contribute to the addictive liability of nicotine. Nicotine modulates aspects of the endocrine system, including the thyroid, which is critical for normal cognitive functioning. It is possible that nicotine’s effects on thyroid function may alter learning and memory, and this may underlie some of its addictive potential. Here, we review the literature on thyroid function and cognition, with a focus on how nicotine alters thyroid hormone signaling and the potential impact on cognition. Changes in cognition are a major symptom of nicotine addiction. Current anti-smoking therapies have modest success at best. If some of the cognitive effects of nicotine are mediated through the thyroid hormone system, then thyroid hormone agonists may be novel treatments for smoking cessation therapies. The content of this review is important because it clarifies the relationship between smoking and thyroid function, which has been ill-defined in the past. This review is timely because the reduction in smoking rates we have seen in recent decades, due to public awareness campaigns and public smoking bans, has leveled off in recent years. Therefore, novel treatment approaches are needed to help reduce smoking rates further. PMID:26344666

  12. Approximate Inference for Wireless Communications

    DEFF Research Database (Denmark)

    Hansen, Morten

    This thesis investigates signal processing techniques for wireless communication receivers. The aim is to improve the performance or reduce the computationally complexity of these, where the primary focus area is cellular systems such as Global System for Mobile communications (GSM) (and extensions...

  13. Breaking Free with Wireless Networks.

    Science.gov (United States)

    Fleischman, John

    2002-01-01

    Discusses wireless local area networks (LANs) which typically consist of laptop computers that connect to fixed access points via infrared or radio signals. Topics include wide area networks; personal area networks; problems, including limitations of available bandwidth, interference, and security concerns; use in education; interoperability;…

  14. Co-Design Method and Wafer-Level Packaging Technique of Thin-Film Flexible Antenna and Silicon CMOS Rectifier Chips for Wireless-Powered Neural Interface Systems.

    Science.gov (United States)

    Okabe, Kenji; Jeewan, Horagodage Prabhath; Yamagiwa, Shota; Kawano, Takeshi; Ishida, Makoto; Akita, Ippei

    2015-12-16

    In this paper, a co-design method and a wafer-level packaging technique of a flexible antenna and a CMOS rectifier chip for use in a small-sized implantable system on the brain surface are proposed. The proposed co-design method optimizes the system architecture, and can help avoid the use of external matching components, resulting in the realization of a small-size system. In addition, the technique employed to assemble a silicon large-scale integration (LSI) chip on the very thin parylene film (5 μm) enables the integration of the rectifier circuits and the flexible antenna (rectenna). In the demonstration of wireless power transmission (WPT), the fabricated flexible rectenna achieved a maximum efficiency of 0.497% with a distance of 3 cm between antennas. In addition, WPT with radio waves allows a misalignment of 185% against antenna size, implying that the misalignment has a less effect on the WPT characteristics compared with electromagnetic induction.

  15. Co-Design Method and Wafer-Level Packaging Technique of Thin-Film Flexible Antenna and Silicon CMOS Rectifier Chips for Wireless-Powered Neural Interface Systems

    Directory of Open Access Journals (Sweden)

    Kenji Okabe

    2015-12-01

    Full Text Available In this paper, a co-design method and a wafer-level packaging technique of a flexible antenna and a CMOS rectifier chip for use in a small-sized implantable system on the brain surface are proposed. The proposed co-design method optimizes the system architecture, and can help avoid the use of external matching components, resulting in the realization of a small-size system. In addition, the technique employed to assemble a silicon large-scale integration (LSI chip on the very thin parylene film (5 μm enables the integration of the rectifier circuits and the flexible antenna (rectenna. In the demonstration of wireless power transmission (WPT, the fabricated flexible rectenna achieved a maximum efficiency of 0.497% with a distance of 3 cm between antennas. In addition, WPT with radio waves allows a misalignment of 185% against antenna size, implying that the misalignment has a less effect on the WPT characteristics compared with electromagnetic induction.

  16. Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System Following Neural Injury

    Science.gov (United States)

    2017-08-01

    AWARD NUMBER: W81XWH-12-1-0051 TITLE: Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System ...Central Nervous System Following Neural Injury 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0051 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Robert...mature mammalian central nervous system (CNS), unlike the peripheral nervous system (PNS), is incapable of axon regeneration. There are currently two

  17. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  18. Embracing interference in wireless systems

    CERN Document Server

    Gollakota, Shyamnath

    2014-01-01

    The wireless medium is a shared resource. If nearby devices transmit at thesame time, their signals interfere, resulting in a collision. In traditionalnetworks, collisions cause the loss of the transmitted information. For thisreason, wireless networks have been designed with the assumption thatinterference is intrinsically harmful and must be avoided.This book, a revised version of the author's award-winning Ph.D.dissertation, takes an alternate approach: Instead of viewing interferenceas an inherently counterproductive phenomenon that should to be avoided, wedesign practical systems that tra

  19. Future Wireless Networks and Information Systems Volume 1

    CERN Document Server

    2012-01-01

    This volume contains revised and extended research articles written by prominent researchers participating in ICFWI 2011 conference. The 2011 International Conference on Future Wireless Networks and Information Systems (ICFWI 2011) has been held on November 30 ~ December 1, 2011, Macao, China. Topics covered include Wireless Information Networks, Wireless Networking Technologies, Mobile Software and Services, intelligent computing, network management, power engineering, control engineering, Signal and Image Processing, Machine Learning, Control Systems and Applications, The book will offer the states of arts of tremendous advances in Wireless Networks and Information Systems and also serve as an excellent reference work for researchers and graduate students working on Wireless Networks and Information Systems.

  20. Cold atmospheric plasma (CAP), a novel physicochemical source, induces neural differentiation through cross-talk between the specific RONS cascade and Trk/Ras/ERK signaling pathway.

    Science.gov (United States)

    Jang, Ja-Young; Hong, Young June; Lim, Junsup; Choi, Jin Sung; Choi, Eun Ha; Kang, Seongman; Rhim, Hyangshuk

    2018-02-01

    Plasma, formed by ionization of gas molecules or atoms, is the most abundant form of matter and consists of highly reactive physicochemical species. In the physics and chemistry fields, plasma has been extensively studied; however, the exact action mechanisms of plasma on biological systems, including cells and humans, are not well known. Recent evidence suggests that cold atmospheric plasma (CAP), which refers to plasma used in the biomedical field, may regulate diverse cellular processes, including neural differentiation. However, the mechanism by which these physicochemical signals, elicited by reactive oxygen and nitrogen species (RONS), are transmitted to biological system remains elusive. In this study, we elucidated the physicochemical and biological (PCB) connection between the CAP cascade and Trk/Ras/ERK signaling pathway, which resulted in neural differentiation. Excited atomic oxygen in the plasma phase led to the formation of RONS in the PCB network, which then interacted with reactive atoms in the extracellular liquid phase to form nitric oxide (NO). Production of large amounts of superoxide radical (O2-) in the mitochondria of cells exposed to CAP demonstrated that extracellular NO induced the reversible inhibition of mitochondrial complex IV. We also demonstrated that cytosolic hydrogen peroxide, formed by O2- dismutation, act as an intracellular messenger to specifically activate the Trk/Ras/ERK signaling pathway. This study is the first to elucidate the mechanism linking physicochemical signals from the CAP cascade to the intracellular neural differentiation signaling pathway, providing physical, chemical and biological insights into the development of therapeutic techniques to treat neurological diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Transforming Growth Factor-Beta Signaling in the Neural Stem Cell Niche: A Therapeutic Target for Huntington's Disease

    Directory of Open Access Journals (Sweden)

    Mahesh Kandasamy

    2011-01-01

    Full Text Available The neural stem cell niches possess the regenerative capacity to generate new functional neurons in the adult brain, suggesting the possibility of endogenous neuronal replacement after injury or disease. Huntington disease (HD is a neurodegenerative disease and characterized by neuronal loss in the basal ganglia, leading to motor, cognitive, and psychological disabilities. Apparently, in order to make use of the neural stem cell niche as a therapeutic concept for repair strategies in HD, it is important to understand the cellular and molecular composition of the neural stem cell niche under such neurodegenerative conditions. This paper mainly discusses the current knowledge on the regulation of the hippocampal neural stem cell niche in the adult brain and by which mechanism it might be compromised in the case of HD.

  2. Household wireless electroencephalogram hat

    Science.gov (United States)

    Szu, Harold; Hsu, Charles; Moon, Gyu; Yamakawa, Takeshi; Tran, Binh

    2012-06-01

    We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

  3. Existing PON Infrastructure Supported Hybrid Fiber-Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Yu, Xianbin; Zhao, Ying; Deng, Lei

    2012-01-01

    We propose a hybrid fiber wireless sensor network based on the existing PON infrastructure. The feasibility of remote sensing and PON convergence is experimentally proven by transmitting direct-sequence spread-spectrum wireless sensing and 2.5Gbps GPON signals.......We propose a hybrid fiber wireless sensor network based on the existing PON infrastructure. The feasibility of remote sensing and PON convergence is experimentally proven by transmitting direct-sequence spread-spectrum wireless sensing and 2.5Gbps GPON signals....

  4. Neural Differentiation of Human Adipose Tissue-Derived Stem Cells Involves Activation of the Wnt5a/JNK Signalling

    Directory of Open Access Journals (Sweden)

    Sujeong Jang

    2015-01-01

    Full Text Available Stem cells are a powerful resource for cell-based transplantation therapies, but understanding of stem cell differentiation at the molecular level is not clear yet. We hypothesized that the Wnt pathway controls stem cell maintenance and neural differentiation. We have characterized the transcriptional expression of Wnt during the neural differentiation of hADSCs. After neural induction, the expressions of Wnt2, Wnt4, and Wnt11 were decreased, but the expression of Wnt5a was increased compared with primary hADSCs in RT-PCR analysis. In addition, the expression levels of most Fzds and LRP5/6 ligand were decreased, but not Fzd3 and Fzd5. Furthermore, Dvl1 and RYK expression levels were downregulated in NI-hADSCs. There were no changes in the expression of ß-catenin and GSK3ß. Interestingly, Wnt5a expression was highly increased in NI-hADSCs by real time RT-PCR analysis and western blot. Wnt5a level was upregulated after neural differentiation and Wnt3, Dvl2, and Naked1 levels were downregulated. Finally, we found that the JNK expression was increased after neural induction and ERK level was decreased. Thus, this study shows for the first time how a single Wnt5a ligand can activate the neural differentiation pathway through the activation of Wnt5a/JNK pathway by binding Fzd3 and Fzd5 and directing Axin/GSK-3ß in hADSCs.

  5. High-level activation of cyclic AMP signaling attenuates bone morphogenetic protein 2-induced sympathoadrenal lineage development and promotes melanogenesis in neural crest cultures.

    Science.gov (United States)

    Ji, Ming; Andrisani, Ourania M

    2005-06-01

    The intensity of cyclic AMP (cAMP) signaling is a differential instructive signal in neural crest (NC) cell specification. By an unknown mechanism, sympathoadrenal lineage specification is suppressed by high-level activation of cAMP signaling. In NC cultures, high-level activation of cAMP signaling mediates protein kinase A (PKA)-dependent Rap1-B-Raf-ERK1/2 activation, leading to cytoplasmic accumulation of phospho-Smad1, thus terminating bone morphogenetic protein 2 (BMP2)-induced sympathoadrenal cell development. Concurrently, cAMP signaling induces transcription of the melanocyte-determining transcription factor Mitf and melanogenesis. dnACREB and E1A inhibit Mitf expression and melanogenesis, supporting the notion that CREB activation is necessary for melanogenesis. However, constitutively active CREB(DIEDML) without PKA activation is insufficient for Mitf expression and melanogenesis, indicating PKA regulates additional aspects of Mitf transcription. Thus, high-level activation of cAMP signaling plays a dual role in NC cell differentiation: attenuation of BMP2-induced sympathoadrenal cell development and induction of melanogenesis. We conclude the intensity of activation of signal transduction cascades determines cell lineage segregation mechanisms.

  6. Efficient Integrated Circuits for Wideband Wireless Transceivers

    OpenAIRE

    Duong, Quoc-Tai

    2016-01-01

    The proliferation of portable communication devices combined with the relentless demand for higher data rates has spurred the development of wireless communication standards which can support wide signal bandwidths. Benefits of the complementary metal oxide semiconductor (CMOS) process such as high device speeds and low manufacturing cost have rendered it the technology of choice for implementing wideband wireless transceiver integrated circuits (ICs). This dissertation addresses the key chal...

  7. Concise Review: Reprogramming, Behind the Scenes: Noncanonical Neural Stem Cell Signaling Pathways Reveal New, Unseen Regulators of Tissue Plasticity With Therapeutic Implications.

    Science.gov (United States)

    Poser, Steven W; Chenoweth, Josh G; Colantuoni, Carlo; Masjkur, Jimmy; Chrousos, George; Bornstein, Stefan R; McKay, Ronald D; Androutsellis-Theotokis, Andreas

    2015-11-01

    Interest is great in the new molecular concepts that explain, at the level of signal transduction, the process of reprogramming. Usually, transcription factors with developmental importance are used, but these approaches give limited information on the signaling networks involved, which could reveal new therapeutic opportunities. Recent findings involving reprogramming by genetic means and soluble factors with well-studied downstream signaling mechanisms, including signal transducer and activator of transcription 3 (STAT3) and hairy and enhancer of split 3 (Hes3), shed new light into the molecular mechanisms that might be involved. We examine the appropriateness of common culture systems and their ability to reveal unusual (noncanonical) signal transduction pathways that actually operate in vivo. We then discuss such novel pathways and their importance in various plastic cell types, culminating in their emerging roles in reprogramming mechanisms. We also discuss a number of reprogramming paradigms (mouse induced pluripotent stem cells, direct conversion to neural stem cells, and in vivo conversion of acinar cells to β-like cells). Specifically for acinar-to-β-cell reprogramming paradigms, we discuss the common view of the underlying mechanism (involving the Janus kinase-STAT pathway that leads to STAT3-tyrosine phosphorylation) and present alternative interpretations that implicate STAT3-serine phosphorylation alone or serine and tyrosine phosphorylation occurring in sequential order. The implications for drug design and therapy are important given that different phosphorylation sites on STAT3 intercept different signaling pathways. We introduce a new molecular perspective in the field of reprogramming with broad implications in basic, biotechnological, and translational research. Reprogramming is a powerful approach to change cell identity, with implications in both basic and applied biology. Most efforts involve the forced expression of key transcription

  8. Wired and wireless convergent extended-reach optical access network using direct-detection of all-optical OFDM super-channel signal.

    Science.gov (United States)

    Chow, C W; Yeh, C H; Sung, J Y; Hsu, C W

    2014-12-15

    We propose and demonstrate the feasibility of using all-optical orthogonal frequency division multiplexing (AO-OFDM) for the convergent optical wired and wireless access networks. AO-OFDM relies on all-optically generated orthogonal subcarriers; hence, high data rate (> 100 Gb/s) can be easily achieved without hitting the speed limit of electronic digital-to-analog and analog-to-digital converters (DAC/ADC). A proof-of-concept convergent access network using AO-OFDM super-channel (SC) is demonstrated supporting 40 - 100 Gb/s wired and gigabit/s 100 GHz millimeter-wave (MMW) ROF transmissions.

  9. (AJST) IMPROVED TIMING RECOVERY IN WIRELESS MOBILE ...

    African Journals Online (AJOL)

    phase estimation algorithm for wireless mobile receivers in low signal to noise ratios. ... In low signal to noise ratio environments, achieving good bandwidth and power transmission efficiencies as well as a jitter free timing phase estimation is a challenging task [3]. ... processing complexities which fail in low SNR conditions.

  10. Model development for wireless propagation in forested environments

    OpenAIRE

    Zegarra, Jesus

    2015-01-01

    Approved for public release; distribution is unlimited Wireless propagation modeling is a necessary task in the design of countless applications. Wireless signals attenuate at different rates according to the propagation environment. Given that vegetation is an unavoidable feature for most outdoor wireless channels, propagation models in forested environments are in high demand. The characterization of radio waves propagating through foliage is particularly complex due to the random charac...

  11. A Wireless Sensor Enabled by Wireless Power

    Science.gov (United States)

    Lee, Da-Sheng; Liu, Yu-Hong; Lin, Chii-Ruey

    2012-01-01

    Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network. PMID:23443370

  12. A low noise remotely controllable wireless telemetry system for single-unit recording in rats navigating in a vertical maze.

    Science.gov (United States)

    Chen, Hsin-Yung; Wu, Jin-Shang; Hyland, Brian; Lu, Xiao-Dong; Chen, Jia Jin Jason

    2008-08-01

    The use of cables for recording neural activity limits the scope of behavioral tests used in conscious free-moving animals. Particularly, cable attachments make it impossible to record in three-dimensional (3D) mazes where levels are vertically stacked or in enclosed spaces. Such environments are of particular interest in investigations of hippocampal place cells, in which neural activity is correlated with spatial position in the environment. We developed a flexible miniaturized Bluetooth-based wireless data acquisition system. The wireless module included an 8-channel analogue front end, digital controller, and Bluetooth transceiver mounted on a backpack. Our bidirectional wireless design allowed all data channels to be previewed at 1 kHz sample rate, and one channel, selected by remote control, to be sampled at 10 kHz. Extracellular recordings of neuronal activity are highly susceptible to ambient electrical noise due to the high electrode impedance. Through careful design of appropriate shielding and hardware configuration to avoid ground loops, mains power and Bluetooth hopping frequency noise were reduced sufficiently to yield signal quality comparable to those recorded by wired systems. With this system we were able to obtain single-unit recordings of hippocampal place cells in rats running an enclosed vertical maze, over a range of 5 m.

  13. Adults with high social anhedonia have altered neural connectivity with ventral lateral prefrontal cortex when processing positive social signals

    Directory of Open Access Journals (Sweden)

    Hong eYin

    2015-08-01

    Full Text Available Social anhedonia (SA is a debilitating characteristic of schizophrenia and a vulnerability for developing schizophrenia among people at risk. Prior work (Hooker et al, 2014 has revealed neural deficits in ventral lateral prefrontal cortex (VLPFC during processing of positive emotion in a community sample of people with high social anhedonia. Deficits in VLPFC neural activity are related to worse self-reported schizophrenia-spectrum symptoms and worse mood and behavior after social stress. In the current study, psychophysiological interaction (PPI analysis was applied to investigate the neural mechanisms mediated by VLPFC during emotion processing. PPI analysis revealed that, compared to low SA controls, participants with high SA displayed reduced VLPFC integration, specifically reduced connectivity between VLPFC and premotor cortex, inferior parietal and posterior temporal regions when viewing positive relative to neutral emotion. Across all participants, connectivity between VLPFC and inferior parietal region when viewing positive (versus neutral emotion was significantly correlated with measures of emotion management and attentional control. Additionally connectivity between VLPFC and superior temporal sulcus was related to reward and pleasure anticipation, and connectivity between VLPFC and inferior temporal sulcus correlated with attentional control measure. Our results suggest that impairments to VLPFC mediated neural circuitry underlie the cognitive and emotional deficits.

  14. Experimental validation of wireless communication with chaos

    Science.gov (United States)

    Ren, Hai-Peng; Bai, Chao; Liu, Jian; Baptista, Murilo S.; Grebogi, Celso

    2016-08-01

    The constraints of a wireless physical media, such as multi-path propagation and complex ambient noises, prevent information from being communicated at low bit error rate. Surprisingly, it has only recently been shown that, from a theoretical perspective, chaotic signals are optimal for communication. It maximises the receiver signal-to-noise performance, consequently minimizing the bit error rate. This work demonstrates numerically and experimentally that chaotic systems can in fact be used to create a reliable and efficient wireless communication system. Toward this goal, we propose an impulsive control method to generate chaotic wave signals that encode arbitrary binary information signals and an integration logic together with the match filter capable of decreasing the noise effect over a wireless channel. The experimental validation is conducted by inputting the signals generated by an electronic transmitting circuit to an electronic circuit that emulates a wireless channel, where the signals travel along three different paths. The output signal is decoded by an electronic receiver, after passing through a match filter.

  15. Experimental validation of wireless communication with chaos.

    Science.gov (United States)

    Ren, Hai-Peng; Bai, Chao; Liu, Jian; Baptista, Murilo S; Grebogi, Celso

    2016-08-01

    The constraints of a wireless physical media, such as multi-path propagation and complex ambient noises, prevent information from being communicated at low bit error rate. Surprisingly, it has only recently been shown that, from a theoretical perspective, chaotic signals are optimal for communication. It maximises the receiver signal-to-noise performance, consequently minimizing the bit error rate. This work demonstrates numerically and experimentally that chaotic systems can in fact be used to create a reliable and efficient wireless communication system. Toward this goal, we propose an impulsive control method to generate chaotic wave signals that encode arbitrary binary information signals and an integration logic together with the match filter capable of decreasing the noise effect over a wireless channel. The experimental validation is conducted by inputting the signals generated by an electronic transmitting circuit to an electronic circuit that emulates a wireless channel, where the signals travel along three different paths. The output signal is decoded by an electronic receiver, after passing through a match filter.

  16. Adaptive Wireless Transceiver Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Many wireless technologies are already available for sensor applications. It is inevitable that many non-interoperable wireless technologies between 400 MHz and 5.8...

  17. Advancing profiling sensors with a wireless approach.

    Science.gov (United States)

    Galvis, Alex; Russomanno, David J

    2012-11-22

    The notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.

  18. Wireless ATM : handover issues

    OpenAIRE

    Jiang, Fan; Käkölä, Timo

    1998-01-01

    Basic aspects of cellular systems and the ATM transmission technology are introduced. Wireless ATM is presented as a combination of radio ATM and mobile ATM. Radio ATM is a wireless extension of an ATM connection while mobile ATM contains the necessary extensions to ATM to support mobility. Because the current ATM technology does not support mobility, handover becomes one of the most important research issues for wireless ATM. Wireless ATM handover requirements are thus analysed. A handover s...

  19. Terahertz wireless communications based on photonics technologies.

    Science.gov (United States)

    Nagatsuma, Tadao; Horiguchi, Shogo; Minamikata, Yusuke; Yoshimizu, Yasuyuki; Hisatake, Shintaro; Kuwano, Shigeru; Yoshimoto, Naoto; Terada, Jun; Takahashi, Hiroyuki

    2013-10-07

    There has been an increasing interest in the application of terahertz (THz) waves to broadband wireless communications. In particular, use of frequencies above 275 GHz is one of the strong concerns among radio scientists and engineers, because these frequency bands have not yet been allocated at specific active services, and there is a possibility to employ extremely large bandwidths for ultra-broadband wireless communications. Introduction of photonics technologies for signal generation, modulation and detection is effective not only to enhance the bandwidth and/or the data rate, but also to combine fiber-optic (wired) and wireless networks. This paper reviews recent progress in THz wireless communications using telecom-based photonics technologies towards 100 Gbit/s.

  20. Wireless communication for hearing aid system

    DEFF Research Database (Denmark)

    Nour, Baqer

    This thesis focuses on the wireless coupling between hearing aids close to a human head. Hearing aids constitute devices withadvanced technology and the wireless communication enables the introduction of a range of completely new functionalities. Such devices are small and the available power...... the ear-to-ear wireless communication channel by understanding the mechanisms that control the propagations of the signals and the losses. The second objective isto investigate the properties of magneto-dielectric materials and their potential in antenna miniaturization. There are three approaches...... to study the ear-to-ear wireless communication link; a theoretical approach models the human head asa sphere that has the electrical properties of the head, a numerical approach implements a more realistic geometry of the head, and an experimental approach measures directly the coupling between...

  1. Voice over IP in Wireless Heterogeneous Networks

    DEFF Research Database (Denmark)

    Fathi, Hanane; Chakraborty, Shyam; Prasad, Ramjee

    with the deployment of wireless heterogeneous systems, both speech and data traffic are carrried over wireless links by the same IP-based packet-switched infrastructure. However, this combination faces some challenges due to the inherent properties of the wireless network. The requirements for good quality VoIP...... communications are difficult to achieve in a time-varying environment due to channel errors and traffic congestion and across different systems. The provision of VoIP in wireless heterogeneous networks requires a set of time-efficient control mechanisms to support a VoIP session with acceptable quality....... The focus of Voice over IP in Wierless Heterogeneous Networks is on mechanisms that affect the VoIP user satisfaction  while not explicitly involved in the media session. This relates to the extra delays introduced by the security and the signaling protocols used to set up an authorized VoIP session...

  2. Getting ahead of the curve in wireless communications | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Robert Schober is a Full Professor in the Department of Electrical and Computer Engineering of the University of British Columbia and Canada Research Chair in Wireless Communications. His research interests fall into the broad areas of communication theory, wireless communications, and statistical signal processing.

  3. Statistical modeling and analysis of interference in wireless networks

    NARCIS (Netherlands)

    Wildemeersch, Matthias; Wildemeersch, Matthias

    2013-01-01

    In current wireless networks, interference is the main performance-limiting factor. The quality of a wireless link depends on the signal and interference power, which is strongly related to the spatial distribution of the concurrently transmitting network nodes, shortly denominated as the network

  4. Implantable radio frequency identification sensors: wireless power and communication.

    Science.gov (United States)

    Hutchens, Chriswell; Rennaker, Robert L; Venkataraman, Srinivasan; Ahmed, Rehan; Liao, Ran; Ibrahim, Tamer

    2011-01-01

    There are significant technical challenges in the development of a fully implantable wirelessly powered neural interface. Challenges include wireless transmission of sufficient power to the implanted device to ensure reliable operation for decades without replacement, minimizing tissue heating, and adequate reliable communications bandwidth. Overcoming these challenges is essential for the development of implantable closed loop system for the treatment of disorders ranging from epilepsy, incontinence, stroke and spinal cord injury. We discuss the development of the wireless power, communication and control for a Radio-Frequency Identification Sensor (RFIDS) system with targeted power range for a 700 mV, 30 to 40 uA load attained at -2 dBm.

  5. Community Wireless Networks

    Science.gov (United States)

    Feld, Harold

    2005-01-01

    With increasing frequency, communities are seeing the arrival of a new class of noncommercial broadband providers: community wireless networks (CWNs). Utilizing the same wireless technologies that many colleges and universities have used to create wireless networks on campus, CWNs are creating broadband access for free or at costs well below…

  6. The Prospects of Ultra-Broadband THz Wireless Communications

    DEFF Research Database (Denmark)

    Yu, Xianbin; Chen, Ying; Galili, Michael

    2014-01-01

    to a few THz bandwidths. This paper overviews the prospects of Tbit/s wireless data rate and their potential applications. Technically, this talk reviews the key technologies and challenges to achieve an ultrafast wireless system operating in the THz frequency band, from viewpoint of communication......Wireless communications have entered into a path towards Terabit era, to accommodate the increasing demands on fast wireless access, e.g. huge data file transferring and fast mobile data access. Terahertz (THz) technology is considered feasible to carry ultrafast data signals, as it offers up...

  7. Towards Ultrahigh Speed Impulse Radio THz Wireless Communications

    DEFF Research Database (Denmark)

    Yu, Xianbin; Galili, Michael; Morioka, Toshio

    2015-01-01

    evaluate the realistic throughput and accessible wireless range of a THz impulse radio communication link based on a uni-travelling photodiode (UTC-PD) as THz emitter and a photoconductive antenna (PCA) as THz receiver. The impact of highly frequency-selective THz channel and the noise in the system......THz impulse radio technologies promise a new paradigm of fast wireless access with simplified wireless reception. However, huge loss of propagating broad bandwidth THz impulse radio signals limits THz wireless transmission distance and reduces the achievable link data rates. In this paper, we...

  8. Neural interface methods and apparatus to provide artificial sensory capabilities to a subject

    Energy Technology Data Exchange (ETDEWEB)

    Buerger, Stephen P.; Olsson, III, Roy H.; Wojciechowski, Kenneth E.; Novick, David K.; Kholwadwala, Deepesh K.

    2017-01-24

    Embodiments of neural interfaces according to the present invention comprise sensor modules for sensing environmental attributes beyond the natural sensory capability of a subject, and communicating the attributes wirelessly to an external (ex-vivo) portable module attached to the subject. The ex-vivo module encodes and communicates the attributes via a transcutaneous inductively coupled link to an internal (in-vivo) module implanted within the subject. The in-vivo module converts the attribute information into electrical neural stimuli that are delivered to a peripheral nerve bundle within the subject, via an implanted electrode. Methods and apparatus according to the invention incorporate implantable batteries to power the in-vivo module allowing for transcutaneous bidirectional communication of low voltage (e.g. on the order of 5 volts) encoded signals as stimuli commands and neural responses, in a robust, low-error rate, communication channel with minimal effects to the subjects' skin.

  9. Neural interface methods and apparatus to provide artificial sensory capabilities to a subject

    Science.gov (United States)

    Buerger, Stephen P.; Olsson, III, Roy H.; Wojciechowski, Kenneth E.; Novick, David K.; Kholwadwala, Deepesh K.

    2017-01-24

    Embodiments of neural interfaces according to the present invention comprise sensor modules for sensing environmental attributes beyond the natural sensory capability of a subject, and communicating the attributes wirelessly to an external (ex-vivo) portable module attached to the subject. The ex-vivo module encodes and communicates the attributes via a transcutaneous inductively coupled link to an internal (in-vivo) module implanted within the subject. The in-vivo module converts the attribute information into electrical neural stimuli that are delivered to a peripheral nerve bundle within the subject, via an implanted electrode. Methods and apparatus according to the invention incorporate implantable batteries to power the in-vivo module allowing for transcutaneous bidirectional communication of low voltage (e.g. on the order of 5 volts) encoded signals as stimuli commands and neural responses, in a robust, low-error rate, communication channel with minimal effects to the subjects' skin.

  10. Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus

    Directory of Open Access Journals (Sweden)

    C. Sanchez-Vazquez

    2014-06-01

    Full Text Available Recently, several mathematical models have been developed to study and explain the way information is processed in the brain. The models published account for a myriad of perspectives from single neuron segments to neural networks, and lately, with the use of supercomputing facilities, to the study of whole environments of nuclei interacting for massive stimuli and processing. Some of the most complex neural structures -and also most studied- are basal ganglia nuclei in the brain; amongst which we can find the Neostriatum. Currently, just a few papers about high scale biological-based computational modeling of this region have been published. It has been demonstrated that the Basal Ganglia region contains functions related to learning and decision making based on rules of the action-selection type, which are of particular interest for the machine autonomous-learning field. This knowledge could be clearly transferred between areas of research. The present work proposes a model of information processing, by integrating knowledge generated from widely accepted experiments in both morphology and biophysics, through integrating theories such as the compartmental electrical model, the Rall’s cable equation, and the Hodking-Huxley particle potential regulations, among others. Additionally, the leaky integrator framework is incorporated in an adapted function. This was accomplished through a computational environment prepared for high scale neural simulation which delivers data output equivalent to that from the original model, and that can not only be analyzed as a Bayesian problem, but also successfully compared to the biological specimen.

  11. Wireless Nanoionic-Based Radio Frequency Switch

    Science.gov (United States)

    Nessel, James A. (Inventor); Miranda, Felix A (Inventor)

    2017-01-01

    A nanoionic switch connected to one or more rectenna modules is disclosed. The rectenna module is configured to receive a wireless signal and apply a first bias to change a state of the nanoionic switch from a first state to a second state. The rectenna module can receive a second wireless signal and apply a second bias to change the nanoionic switch from the second state back to the first state. The first bias is generally opposite of the first bias. The rectenna module accordingly permits operation of the nanoionic switch without onboard power.

  12. Species, sex and individual differences in the vasotocin/vasopressin system: relationship to neurochemical signaling in the social behavior neural network.

    Science.gov (United States)

    Albers, H Elliott

    2015-01-01

    Arginine-vasotocin (AVT)/arginine vasopressin (AVP) are members of the AVP/oxytocin (OT) superfamily of peptides that are involved in the regulation of social behavior, social cognition and emotion. Comparative studies have revealed that AVT/AVP and their receptors are found throughout the "social behavior neural network (SBNN)" and display the properties expected from a signaling system that controls social behavior (i.e., species, sex and individual differences and modulation by gonadal hormones and social factors). Neurochemical signaling within the SBNN likely involves a complex combination of synaptic mechanisms that co-release multiple chemical signals (e.g., classical neurotransmitters and AVT/AVP as well as other peptides) and non-synaptic mechanisms (i.e., volume transmission). Crosstalk between AVP/OT peptides and receptors within the SBNN is likely. A better understanding of the functional properties of neurochemical signaling in the SBNN will allow for a more refined examination of the relationships between this peptide system and species, sex and individual differences in sociality. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Sonic Hedgehog Signaling Mediates Resveratrol to Increase Proliferation of Neural Stem Cells After Oxygen-Glucose Deprivation/Reoxygenation Injury in Vitro

    Directory of Open Access Journals (Sweden)

    Wei Cheng

    2015-03-01

    Full Text Available Background/Aims: There is interest in drugs and rehabilitation methods to enhance neurogenesis and improve neurological function after brain injury or degeneration. Resveratrol may enhance hippocampal neurogenesis and improve hippocampal atrophy in chronic fatigue mice and prenatally stressed rats. However, its effect and mechanism of neurogenesis after stroke is less well understood. Sonic hedgehog (Shh signaling is crucial for neurogenesis in the embryonic and adult brain, but relatively little is known about the role of Shh signaling in resveratrol-enhanced neurogenesis after stroke. Methods: Neural stem cells (NSCs before oxygen-glucose deprivation/reoxygenation (OGD/R in vitro were pretreated with resveratrol with or without cyclopamine. Survival and proliferation of NSCs was assessed by the CCK8 assay and BrdU immunocytochemical staining. The expressions and activity of signaling proteins and mRNAs were detected by immunocytochemistry, Western blotting, and RT-PCR analysis. Results: Resveratrol significantly increased NSCs survival and proliferation in a concentration-dependent manner after OGD/R injury in vitro. At the same time, the expression of Patched-1, Smoothened (Smo, and Gli-1 proteins and mRNAs was upregulated, and Gli-1 entered the nucleus, which was inhibited by cyclopamine, a Smo inhibitor. Conclusion: Shh signaling mediates resveratrol to increase NSCs proliferation after OGD/R injury in vitro.

  14. CCNA Wireless Study Guide

    CERN Document Server

    Lammle, Todd

    2010-01-01

    A complete guide to the CCNA Wireless exam by leading networking authority Todd Lammle. The CCNA Wireless certification is the most respected entry-level certification in this rapidly growing field. Todd Lammle is the undisputed authority on networking, and this book focuses exclusively on the skills covered in this Cisco certification exam. The CCNA Wireless Study Guide joins the popular Sybex study guide family and helps network administrators advance their careers with a highly desirable certification.: The CCNA Wireless certification is the most respected entry-level wireless certification

  15. The Wireless ATM Architecture

    Directory of Open Access Journals (Sweden)

    R. Palitefka

    1998-06-01

    Full Text Available An overview of the proposed wireless ATM structure is provided. Wireless communication have been developed to a level where offered services can now be extended beyond voice and data. There are already wireless LANs, cordless systems offering data services and mobile data. Wireless LAN systems are basically planned for local, on-promises and in-house networking providing short distance radio or infrared links between computer system. The main challenge of wireless ATM is to harmonise the development of broadband wireless system with service B -ISDN/ATM and ATM LANs, and offer multimedia multiservice features for the support of time-sensitive voice communication, video, desktop multimedia applications, and LAN data traffic for the wireless user.

  16. EMG amplifier with wireless data transmission

    Science.gov (United States)

    Kowalski, Grzegorz; Wildner, Krzysztof

    2017-08-01

    Wireless medical diagnostics is a trend in modern technology used in medicine. This paper presents a concept of realization, architecture of hardware and software implementation of an elecromyography signal (EMG) amplifier with wireless data transmission. This amplifier consists of three components: analogue processing of bioelectric signal module, micro-controller circuit and an application enabling data acquisition via a personal computer. The analogue bioelectric signal processing circuit receives electromyography signals from the skin surface, followed by initial analogue processing and preparation of the signals for further digital processing. The second module is a micro-controller circuit designed to wirelessly transmit the electromyography signals from the analogue signal converter to a personal computer. Its purpose is to eliminate the need for wired connections between the patient and the data logging device. The third block is a computer application designed to display the transmitted electromyography signals, as well as data capture and analysis. Its purpose is to provide a graphical representation of the collected data. The entire device has been thoroughly tested to ensure proper functioning. In use, the device displayed the captured electromyography signal from the arm of the patient. Amplitude- frequency characteristics were set in order to investigate the bandwidth and the overall gain of the device.