WorldWideScience

Sample records for wireless neural signal

  1. Using Pulse Width Modulation for Wireless Transmission of Neural Signals in Multichannel Neural Recording Systems

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2013-01-01

    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-μm 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 ~ 2.26 Mb/s. PMID:19497823

  2. 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.

  3. Transmission of wireless neural signals through a 0.18 µm CMOS low-power amplifier.

    Science.gov (United States)

    Gazziro, M; Braga, C F R; Moreira, D A; Carvalho, A C P L F; Rodrigues, J F; Navarro, J S; Ardila, J C M; Mioni, D P; Pessatti, M; Fabbro, P; Freewin, C; Saddow, S E

    2015-01-01

    In the field of Brain Machine Interfaces (BMI) researchers still are not able to produce clinically viable solutions that meet the requirements of long-term operation without the use of wires or batteries. Another problem is neural compatibility with the electrode probes. One of the possible ways of approaching these problems is the use of semiconductor biocompatible materials (silicon carbide) combined with an integrated circuit designed to operate with low power consumption. This paper describes a low-power neural signal amplifier chip, named Cortex, fabricated using 0.18 μm CMOS process technology with all electronics integrated in an area of 0.40 mm(2). The chip has 4 channels, total power consumption of only 144 μW, and is impedance matched to silicon carbide biocompatible electrodes.

  4. 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.

  5. 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....

  6. Wireless Neural Recording With Single Low-Power Integrated Circuit

    Science.gov (United States)

    Harrison, Reid R.; Kier, Ryan J.; Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V.

    2010-01-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6-μm 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902–928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor. PMID:19497825

  7. Wireless neural recording with single low-power integrated circuit.

    Science.gov (United States)

    Harrison, Reid R; Kier, Ryan J; Chestek, Cynthia A; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V

    2009-08-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.

  8. 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.

  9. 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.

  10. Unpowered wireless transmission of ultrasound signals

    International Nuclear Information System (INIS)

    Huang, H; Paramo, D; Deshmukh, S

    2011-01-01

    This paper presents a wireless ultrasound sensing system that uses frequency conversion to convert the ultrasound signal to a microwave signal and transmit it directly without digitization. Constructed from a few passive microwave components, the sensor is able to sense, modulate, and transmit the full waveform of ultrasound signals wirelessly without requiring any local power source. The principle of operation of the unpowered wireless ultrasound sensor is described first, and this is followed by a detailed description of the implementation of the sensor and the sensor interrogation unit using commercially available antennas and microwave components. Validation of the sensing system using an ultrasound pitch–catch system and the power analysis model of the system are also presented

  11. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

    Science.gov (United States)

    Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Kim, Hyungmin; Youn, Inchan

    2017-12-21

    Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  12. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

    Full Text Available Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC-compliant power transmission circuit, a medical implant communication service (MICS-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  13. Wireless synapses in bio-inspired neural networks

    Science.gov (United States)

    Jannson, Tomasz; Forrester, Thomas; Degrood, Kevin

    2009-05-01

    Wireless (virtual) synapses represent a novel approach to bio-inspired neural networks that follow the infrastructure of the biological brain, except that biological (physical) synapses are replaced by virtual ones based on cellular telephony modeling. Such synapses are of two types: intracluster synapses are based on IR wireless ones, while intercluster synapses are based on RF wireless ones. Such synapses have three unique features, atypical of conventional artificial ones: very high parallelism (close to that of the human brain), very high reconfigurability (easy to kill and to create), and very high plasticity (easy to modify or upgrade). In this paper we analyze the general concept of wireless synapses with special emphasis on RF wireless synapses. Also, biological mammalian (vertebrate) neural models are discussed for comparison, and a novel neural lensing effect is discussed in detail.

  14. IR wireless cluster synapses of HYDRA very large neural networks

    Science.gov (United States)

    Jannson, Tomasz; Forrester, Thomas

    2008-04-01

    RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

  15. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  16. A Wireless Fully Passive Neural Recording Device for Unobtrusive Neuropotential Monitoring.

    Science.gov (United States)

    Kiourti, Asimina; Lee, Cedric W L; Chae, Junseok; Volakis, John L

    2016-01-01

    We propose a novel wireless fully passive neural recording device for unobtrusive neuropotential monitoring. Previous work demonstrated the feasibility of monitoring emulated brain signals in a wireless fully passive manner. In this paper, we propose a novel realistic recorder that is significantly smaller and much more sensitive. The proposed recorder utilizes a highly efficient microwave backscattering method and operates without any formal power supply or regulating elements. Also, no intracranial wires or cables are required. In-vitro testing is performed inside a four-layer head phantom (skin, bone, gray matter, and white matter). Compared to our former implementation, the neural recorder proposed in this study has the following improved features: 1) 59% smaller footprint, 2) up to 20-dB improvement in neuropotential detection sensitivity, and 3) encapsulation in biocompatible polymer. For the first time, temporal emulated neuropotentials as low as 63 μVpp can be detected in a wireless fully passive manner. Remarkably, the high-sensitivity achieved in this study implies reading of most neural signals generated by the human brain. The proposed recorder brings forward transformational possibilities in wireless fully passive neural detection for a very wide range of applications (e.g., epilepsy, Alzheimer's, mental disorders, etc.).

  17. Battery-Free Love-Wave-Based Neural Probe and Its Wireless Characterizations

    Science.gov (United States)

    Jung, In Ki; Fu, Chen; Lee, Keekeun

    2013-06-01

    A wireless Love-wave-based neural probe that utilizes a one-port reflective delay line was developed for both reading and stimulating neurons in the brain. Poly(methyl methacrylate) (PMMA) as a waveguide layer and gold (Au) electrodes were structured on the top of a 41° YX LiNbO3 piezoelectric substrate, following the parameters extracted from coupling-of-mode (COM) modeling. For a one-port reflective delay line, single-phase unidirectional transducers (SPUDTs) and three shorted grating reflectors were employed, which made possible the implementation of a wireless and battery-free neural probe. The fabricated Love-wave-based neural probes were wirelessly measured using two antennas with a 440 MHz central frequency and a network analyzer. Sharp reflection peaks with a high signal-to-noise ratio were observed from the reflection peaks. The probe was immersed in 0.9% saline solution while applying input DC voltages. Good linearity, high sensitivity, and reproducibility were observed depending on DC applied voltage, in the range from 0 to 500 mV. The sensitivity obtained from the DC firings (artificial neural firings) was ˜0.04 µs/VDC, indicating that this prototype probe is very promising for the wireless reading and stimulation of neural firings in in vivo animal testing.

  18. 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...... by allowing an increased reuse of network resources. To achieve this goal, one must first understand the nature of the problem and an introduction is therefore provided. In addition, the concept of graph-based models and approximations for wireless communications is introduced along with various Belief...... Propagation (BP) methods for detecting the transmitted information, including the Turbo principle. Having established a framework for the research, various approximate detection schemes are discussed. First, the general form of linear detection is presented and it is argued that this may be preferable...

  19. 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

    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. 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. 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. 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.

  20. 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.

  1. Measurements on wireless transmission of ECG signals

    International Nuclear Information System (INIS)

    Gabrielli, A.; Lax, I.

    2016-01-01

    The scope of this research is to design an electronic prototype, an operative system as a proof of concept, to transmit and receive biological parameters, in particular electrocardiogram signals, through dedicated wireless circuits. The apparatus features microelectronics chips that were developed for more general biomedical applications, here adapted to deal with cardiac signals. The paper mainly focuses on the electronic aspects, as in this study we do not face medical or clinical aspects of the system. The transmitter circuit uses a commercial instrumentation amplifier and the receiver has been equipped with wide-band amplifiers along with made-in-the-lab band-pass filters centered at the carrier. We have been able to mount the entire system prototype into a preliminary data acquisition chain that reads out the electrocardiogram signal. The prototype allows acquiring the waveform, converting it to a digital pattern and open the transmission through a series of high-frequency packets exploiting the Ultra Wide Band protocol. The sensor value is embedded in the transmission through the rate of the digital packets. In fact, these are sent wireless at a specific packet-frequency that depends on the sensor amplitude and are detected into a receiver circuit that recovers the information.

  2. Measurements on wireless transmission of ECG signals

    Science.gov (United States)

    Gabrielli, A.; Lax, I.

    2016-12-01

    The scope of this research is to design an electronic prototype, an operative system as a proof of concept, to transmit and receive biological parameters, in particular electrocardiogram signals, through dedicated wireless circuits. The apparatus features microelectronics chips that were developed for more general biomedical applications, here adapted to deal with cardiac signals. The paper mainly focuses on the electronic aspects, as in this study we do not face medical or clinical aspects of the system. The transmitter circuit uses a commercial instrumentation amplifier and the receiver has been equipped with wide-band amplifiers along with made-in-the-lab band-pass filters centered at the carrier. We have been able to mount the entire system prototype into a preliminary data acquisition chain that reads out the electrocardiogram signal. The prototype allows acquiring the waveform, converting it to a digital pattern and open the transmission through a series of high-frequency packets exploiting the Ultra Wide Band protocol. The sensor value is embedded in the transmission through the rate of the digital packets. In fact, these are sent wireless at a specific packet-frequency that depends on the sensor amplitude and are detected into a receiver circuit that recovers the information.

  3. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  4. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  5. Programming signal processing applications on heterogeneous wireless sensor platforms

    NARCIS (Netherlands)

    Buondonno, L.; Fortino, G.; Galzarano, S.; Giannantonio, R.; Giordano, A.; Gravina, R.; Guerrieri, A.

    2009-01-01

    This paper proposes the SPINE frameworks (SPINE1.x and SPINE2) for the programming of signal processing applications on heterogeneous wireless sensor platforms. In particular, two integrable approaches based on the proposed frameworks are described that allow to develop applications for wireless

  6. 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

  7. A wirelessly powered microspectrometer for neural probe-pin device

    Science.gov (United States)

    Choi, Sang H.; Kim, Min H.; Song, Kyo D.; Yoon, Hargsoon; Lee, Uhn

    2015-12-01

    Treatment of neurological anomalies, whether done invasively or not, places stringent demands on device functionality and size. We have developed a micro-spectrometer for use as an implantable neural probe to monitor neuro-chemistry in synapses. The micro-spectrometer, based on a NASA-invented miniature Fresnel grating, is capable of differentiating the emission spectra from various brain tissues. The micro-spectrometer meets the size requirements, and is able to probe the neuro-chemistry and suppression voltage typically associated with a neural anomaly. This neural probe-pin device (PPD) is equipped with wireless power technology (WPT) to enable operation in a continuous manner without requiring an implanted battery. The implanted neural PPD, together with a neural electronics interface and WPT, enable real-time measurement and control/feedback for remediation of neural anomalies. The design and performance of the combined PPD/WPT device for monitoring dopamine in a rat brain will be presented to demonstrate the current level of development. Future work on this device will involve the addition of an embedded expert system capable of performing semi-autonomous management of neural functions through a routine of sensing, processing, and control.

  8. Recycling signals in the neural crest

    OpenAIRE

    Taneyhill, Lisa A.; Bronner-Fraser, Marianne E.

    2006-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  9. Recycling signals in the neural crest.

    Science.gov (United States)

    Taneyhill, Lisa A; Bronner-Fraser, Marianne

    2005-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  10. 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....

  11. Signal Processing and Neural Network Simulator

    Science.gov (United States)

    Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

    1995-04-01

    The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

  12. Dynamic decomposition of spatiotemporal neural signals.

    Directory of Open Access Journals (Sweden)

    Luca Ambrogioni

    2017-05-01

    Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

  13. A simple miniature device for wireless stimulation of neural circuits in small behaving animals.

    Science.gov (United States)

    Zhang, Yisi; Langford, Bruce; Kozhevnikov, Alexay

    2011-10-30

    The use of wireless neural stimulation devices offers significant advantages for neural stimulation experiments in behaving animals. We demonstrate a simple, low-cost and extremely lightweight wireless neural stimulation device which is made from off-the-shelf components. The device has low power consumption and does not require a high-power RF preamplifier. Neural stimulation can be carried out in either a voltage source mode or a current source mode. Using the device, we carry out wireless stimulation in the premotor brain area HVC of a songbird and demonstrate that such stimulation causes rapid perturbations of the acoustic structure of the song. Published by Elsevier B.V.

  14. 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.

  15. 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 µV rms 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.

  16. Multichannel signal enhancement using a remote wireless microphone

    NARCIS (Netherlands)

    Bloemendal, Brian; Van De Laar, Jakob; Sommen, Piet

    2012-01-01

    A novel approach to multichannel signal enhancement is presented that exploits data from a remote wireless microphone (RWM). This RWM is placed near an interfering source and transmits only autocorrelation data of its observations to a host, i.e., not the entire signal. The host has access to the

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

    Directory of Open Access Journals (Sweden)

    Yujuan Zhao

    Full Text Available 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.

  18. Detection test of wireless network signal strength and GPS positioning signal in underground pipeline

    Science.gov (United States)

    Li, Li; Zhang, Yunwei; Chen, Ling

    2018-03-01

    In order to solve the problem of selecting positioning technology for inspection robot in underground pipeline environment, the wireless network signal strength and GPS positioning signal testing are carried out in the actual underground pipeline environment. Firstly, the strength variation of the 3G wireless network signal and Wi-Fi wireless signal provided by China Telecom and China Unicom ground base stations are tested, and the attenuation law of these wireless signals along the pipeline is analyzed quantitatively and described. Then, the receiving data of the GPS satellite signal in the pipeline are tested, and the attenuation of GPS satellite signal under underground pipeline is analyzed. The testing results may be reference for other related research which need to consider positioning in pipeline.

  19. Estimation of neural energy in microelectrode signals

    Science.gov (United States)

    Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

    2004-09-01

    We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

  20. 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.

  1. Subsurface event detection and classification using Wireless Signal Networks.

    Science.gov (United States)

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-11-05

    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.

  2. An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

    Science.gov (United States)

    Lee, Seung Bae; Lee, Byunghun; Kiani, Mehdi; Mahmoudi, Babak; Gross, Robert; Ghovanloo, Maysam

    2016-01-15

    An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-μm CMOS process, occupying 5.0 × 2.5 mm 2 and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 μW. In vivo experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).

  3. 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.

  4. Trade-offs in the distribution of neural networks in a wireless sensor network

    NARCIS (Netherlands)

    Holenderski, M.J.; Lukkien, J.J.; Tham, C.K.

    2005-01-01

    This article investigates the tradeoff between communication and memory usage in different methods of distributing neural networks in a Wireless Sensor Network. A structural approach is presented, categorized in two dimensions: horizontal and vertical decomposition. Horizontal decomposition turns

  5. Conditional Random Fields for Morphological Analysis of Wireless ECG Signals

    OpenAIRE

    Natarajan, Annamalai; Gaiser, Edward; Angarita, Gustavo; Malison, Robert; Ganesan, Deepak; Marlin, Benjamin

    2014-01-01

    Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel app...

  6. NASA Fuel Tank Wireless Power and Signal Study

    Science.gov (United States)

    Merrill, Garrick

    2015-01-01

    Hydro Technologies has developed a custom electronics and mechanical framework for interfacing with off-the-shelf sensors to achieve through barrier sensing solutions. The core project technology relies on Hydro Technologies Wireless Power and Signal Interface (Wi psi) System for transmitting data and power wirelessly using magnetic fields. To accomplish this, Wi psi uses a multi-frequency local magnetic field to produce magnetic fields capable of carrying data and power through almost any material such as metals, seawater, concrete, and air. It will also work through layers of multiple materials.

  7. 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.

  8. 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.

  9. Conditional Random Fields for Morphological Analysis of Wireless ECG Signals

    Science.gov (United States)

    Natarajan, Annamalai; Gaiser, Edward; Angarita, Gustavo; Malison, Robert; Ganesan, Deepak; Marlin, Benjamin

    2015-01-01

    Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit. PMID:26726321

  10. 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.

  11. 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....

  12. 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.

  13. 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.

  14. A dual slope charge sampling analog front-end for a wireless neural recording system.

    Science.gov (United States)

    Lee, Seung Bae; Lee, Byunghun; Gosselin, Benoit; Ghovanloo, Maysam

    2014-01-01

    This paper presents a novel dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which amplifies neural signals by taking advantage of the charge sampling concept for analog signal conditioning, such as amplification and filtering. The presented DSCS-AFE achieves amplification, filtering, and sampling in a simultaneous fashion, while consuming very small amount of power. The output of the DSCS-AFE produces a pulse width modulated (PWM) signal that is proportional to the input voltage amplitude. A circular shift register (CSR) utilizes time division multiplexing (TDM) of the PWM pulses to create a pseudo-digital TDM-PWM signal that can feed a wireless transmitter. The 8-channel system-on-a-chip was fabricated in a 0.35-μm CMOS process, occupying 2.4 × 2.1 mm(2) and consuming 255 μW from a 1.8V supply. Measured input-referred noise for the entire system, including the FPGA in order to recover PWM signal is 6.50 μV(rms) in the 288 Hz~10 kHz range. For each channel, sampling rate is 31.25 kHz, and power consumption is 31.8 μW.

  15. 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.

  16. Development of a wireless nuclear signal transmitter

    Energy Technology Data Exchange (ETDEWEB)

    Soodsakorn, A

    1994-12-31

    This thesis aims at the development of a nuclear signal transmission system using radio frequency as carrier. The system is helpful for long distance data transmission especially convenient in high level radiation area. The transmitting system comprises of pulse height ADCs with serial output, digital data modulation, frequency modulation and a l watt C B 27.125 MHz transmitter. The sequential data transmission is controlled by micro controller. The receiving system comprises of detector, noise filter and data demodulator where the signals in form of nuclear spectrum will be displayed on a micro-computer through R S-232{sub C} serial data transmission. It is found that the developed system can transmit a nuclear pulse height in the range of 0-10 V with the pulse width varying from 0.5-10 us. The linear correlation of the pulse height ADCs conversion is 0.998. The system can transmit a nuclear pulse rate of 600 cpm with the serial data of 1200 baud rate without error. At a l watt transmitted power, the system can on air cover an area of l km radius for continuous operation

  17. 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.

  18. Model for neural signaling leap statistics

    International Nuclear Information System (INIS)

    Chevrollier, Martine; Oria, Marcos

    2011-01-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.

  19. Characterization of Radar Signals Using Neural Networks

    Science.gov (United States)

    1990-12-01

    e***e*e*eeeeeeeeeeeesseeeeeese*eee*e*e************s /* Function Name: load.input.ptterns Number: 4.1 /* Description: This function determines wether ...XSE.last.layer Number: 8.5 */ /* Description: The function determines wether to backpropate the *f /* parameter by the sigmoidal or linear update...Sigmoidal Function," Mathematics of Control, Signals and Systems, 2:303-314 (March 1989). 6. Dayhoff, Judith E. Neural Network Architectures. New York: Van

  20. Model for neural signaling leap statistics

    Science.gov (United States)

    Chevrollier, Martine; Oriá, Marcos

    2011-03-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.

  1. Model for neural signaling leap statistics

    Energy Technology Data Exchange (ETDEWEB)

    Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)

    2011-03-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Neural network-based sensor signal accelerator.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  12. Robust Indoor Human Activity Recognition Using Wireless Signals.

    Science.gov (United States)

    Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

    2015-07-15

    Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

  13. 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

  14. Active voltammetric microsensors with neural signal processing

    Science.gov (United States)

    Vogt, Michael C.; Skubal, Laura R.

    1999-02-01

    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 detect, recognize, and

  15. A fuzzy neural network for sensor signal estimation

    International Nuclear Information System (INIS)

    Na, Man Gyun

    2000-01-01

    In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

  16. Toward Wireless Health Monitoring via an Analog Signal Compression-Based Biosensing Platform.

    Science.gov (United States)

    Zhao, Xueyuan; Sadhu, Vidyasagar; Le, Tuan; Pompili, Dario; Javanmard, Mehdi

    2018-06-01

    Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by the analog joint source-channel coding (AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.

  17. Batteryless wireless transmission system for electronic drum uses piezoelectric generator for play signal and power source

    International Nuclear Information System (INIS)

    Nishikawa, H; Yoshimi, A; Takemura, K; Tanaka, A; Douseki, T

    2015-01-01

    A batteryless self-powered wireless transmission system has been developed that sends a signal from a drum pad to a synthesizer. The power generated by a piezoelectric generator functions both as the “Play” signal for the synthesizer and as the power source for the transmitter. An FM transmitter, which theoretically operates with zero latency, and a receiver with quick-response squelch of the received signal were developed for wireless transmission with a minimum system delay. Experimental results for an electronic drum without any connecting wires fully demonstrated the feasibility of self-powered wireless transmission with a latency of 900 μs. (paper)

  18. Robust Indoor Human Activity Recognition Using Wireless Signals

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2015-07-01

    Full Text Available Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP and access points (AP. First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

  19. AKT signaling displays multifaceted functions in neural crest development.

    Science.gov (United States)

    Sittewelle, Méghane; Monsoro-Burq, Anne H

    2018-05-31

    AKT signaling is an essential intracellular pathway controlling cell homeostasis, cell proliferation and survival, as well as cell migration and differentiation in adults. Alterations impacting the AKT pathway are involved in many pathological conditions in human disease. Similarly, during development, multiple transmembrane molecules, such as FGF receptors, PDGF receptors or integrins, activate AKT to control embryonic cell proliferation, migration, differentiation, and also cell fate decisions. While many studies in mouse embryos have clearly implicated AKT signaling in the differentiation of several neural crest derivatives, information on AKT functions during the earliest steps of neural crest development had remained relatively scarce until recently. However, recent studies on known and novel regulators of AKT signaling demonstrate that this pathway plays critical roles throughout the development of neural crest progenitors. Non-mammalian models such as fish and frog embryos have been instrumental to our understanding of AKT functions in neural crest development, both in neural crest progenitors and in the neighboring tissues. This review combines current knowledge acquired from all these different vertebrate animal models to describe the various roles of AKT signaling related to neural crest development in vivo. We first describe the importance of AKT signaling in patterning the tissues involved in neural crest induction, namely the dorsal mesoderm and the ectoderm. We then focus on AKT signaling functions in neural crest migration and differentiation. Copyright © 2018 Elsevier Inc. All rights reserved.

  20. 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

  1. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

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

    1990-01-01

    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...

  2. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    1991-01-01

    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

  4. 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.

  5. 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.

  6. 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.

  7. Robo signaling regulates the production of cranial neural crest cells.

    Science.gov (United States)

    Li, Yan; Zhang, Xiao-Tan; Wang, Xiao-Yu; Wang, Guang; Chuai, Manli; Münsterberg, Andrea; Yang, Xuesong

    2017-12-01

    Slit/Robo signaling plays an important role in the guidance of developing neurons in developing embryos. However, it remains obscure whether and how Slit/Robo signaling is involved in the production of cranial neural crest cells. In this study, we examined Robo1 deficient mice to reveal developmental defects of mouse cranial frontal and parietal bones, which are derivatives of cranial neural crest cells. Therefore, we determined the production of HNK1 + cranial neural crest cells in early chick embryo development after knock-down (KD) of Robo1 expression. Detection of markers for pre-migratory and migratory neural crest cells, PAX7 and AP-2α, showed that production of both was affected by Robo1 KD. In addition, we found that the transcription factor slug is responsible for the aberrant delamination/EMT of cranial neural crest cells induced by Robo1 KD, which also led to elevated expression of E- and N-Cadherin. N-Cadherin expression was enhanced when blocking FGF signaling with dominant-negative FGFR1 in half of the neural tube. Taken together, we show that Slit/Robo signaling influences the delamination/EMT of cranial neural crest cells, which is required for cranial bone development. Copyright © 2017. Published by Elsevier Inc.

  8. 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...

  9. 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.

  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. 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.

  12. 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 successfull...... 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....

  13. Power Conditioning and Stimulation for Wireless Neural Interface ICs

    OpenAIRE

    Biederman, William

    2014-01-01

    Brain machine interfaces have the potential to revolutionize our understanding of the brain, restore motor function, and improve the quality of life to patients with neurological con- ditions. In recent human trials, control of robotic prostheses has been demonstrated using micro-electrode arrays, which provide high spatio-temporal resolution and an electrical feed- back path to the brain. However, after implantation, scar tissue degrades the recording signal-to-noise ratio and limits the use...

  14. 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.

  15. 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.

  16. Neural redundancy applied to the parity space for signal validation

    International Nuclear Information System (INIS)

    Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu; Martinez, Aquilino Senra

    2005-01-01

    The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

  17. Neural redundancy applied to the parity space for signal validation

    Energy Technology Data Exchange (ETDEWEB)

    Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: cmnap@ien.gov.br; Martinez, Aquilino Senra [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia]. E-mail: aquilino@lmp.br

    2005-07-01

    The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

  18. Adaptive quantization of local field potentials for wireless implants in freely moving animals: an open-source neural recording device

    Science.gov (United States)

    Martinez, Dominique; Clément, Maxime; Messaoudi, Belkacem; Gervasoni, Damien; Litaudon, Philippe; Buonviso, Nathalie

    2018-04-01

    Objective. Modern neuroscience research requires electrophysiological recording of local field potentials (LFPs) in moving animals. Wireless transmission has the advantage of removing the wires between the animal and the recording equipment but is hampered by the large number of data to be sent at a relatively high rate. Approach. To reduce transmission bandwidth, we propose an encoder/decoder scheme based on adaptive non-uniform quantization. Our algorithm uses the current transmitted codeword to adapt the quantization intervals to changing statistics in LFP signals. It is thus backward adaptive and does not require the sending of side information. The computational complexity is low and similar at the encoder and decoder sides. These features allow for real-time signal recovery and facilitate hardware implementation with low-cost commercial microcontrollers. Main results. As proof-of-concept, we developed an open-source neural recording device called NeRD. The NeRD prototype digitally transmits eight channels encoded at 10 kHz with 2 bits per sample. It occupies a volume of 2  ×  2  ×  2 cm3 and weighs 8 g with a small battery allowing for 2 h 40 min of autonomy. The power dissipation is 59.4 mW for a communication range of 8 m and transmission losses below 0.1%. The small weight and low power consumption offer the possibility of mounting the entire device on the head of a rodent without resorting to a separate head-stage and battery backpack. The NeRD prototype is validated in recording LFPs in freely moving rats at 2 bits per sample while maintaining an acceptable signal-to-noise ratio (>30 dB) over a range of noisy channels. Significance. Adaptive quantization in neural implants allows for lower transmission bandwidths while retaining high signal fidelity and preserving fundamental frequencies in LFPs.

  19. 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

    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....

  20. Optical-wireless-optical full link for polarization multiplexing quadrature amplitude/phase modulation signal transmission.

    Science.gov (United States)

    Li, Xinying; Yu, Jianjun; Chi, Nan; Zhang, Junwen

    2013-11-15

    We propose and experimentally demonstrate an optical wireless integration system at the Q-band, in which up to 40 Gb/s polarization multiplexing multilevel quadrature amplitude/phase modulation (PM-QAM) signal can be first transmitted over 20 km single-mode fiber-28 (SMF-28), then delivered over a 2 m 2 × 2 multiple-input multiple-output wireless link, and finally transmitted over another 20 km SMF-28. The PM-QAM modulated wireless millimeter-wave (mm-wave) signal at 40 GHz is generated based on the remote heterodyning technique, and demodulated by the radio-frequency transparent photonic technique based on homodyne coherent detection and baseband digital signal processing. The classic constant modulus algorithm equalization is used at the receiver to realize polarization demultiplexing of the PM-QAM signal. For the first time, to the best of our knowledge, we realize the conversion of the PM-QAM modulated wireless mm-wave signal to the optical signal as well as 20 km fiber transmission of the converted optical signal.

  1. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  2. 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.

  3. Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device.

    Science.gov (United States)

    Khanna, Preeya; Swann, Nicole C; de Hemptinne, Coralie; Miocinovic, Svjetlana; Miller, Andrew; Starr, Philip A; Carmena, Jose M

    2017-10-01

    Parkinson's disease (PD) is characterized by motor symptoms such as rigidity and bradykinesia that prevent normal movement. Beta band oscillations (13-30 Hz) in neural local field potentials (LFPs) have been associated with these motor symptoms. Here, three PD patients implanted with a therapeutic deep brain neural stimulator that can also record and wirelessly stream neural data played a neurofeedback game where they modulated their beta band power from sensorimotor cortical areas. Patients' beta band power was streamed in real-time to update the position of a cursor that they tried to drive into a cued target. After playing the game for 1-2 hours each, all three patients exhibited above chance-level performance regardless of subcortical stimulation levels. This study, for the first time, demonstrates using an invasive neural recording system for at-home neurofeedback training. Future work will investigate chronic neurofeedback training as a potentially therapeutic tool for patients with neurological disorders.

  4. 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.

  5. 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....

  6. Sphingosine-1-Phosphate (S1P) Signaling in Neural Progenitors.

    Science.gov (United States)

    Callihan, Phillip; Alqinyah, Mohammed; Hooks, Shelley B

    2018-01-01

    Sphingosine-1-phosphate (S1P) and its receptors are important in nervous system development. Reliable in vitro human model systems are needed to further define specific roles for S1P signaling in neural development. We have described S1P-regulated signaling, survival, and differentiation in a human embryonic stem cell-derived neuroepithelial progenitor cell line (hNP1) that expresses functional S1P receptors. These cells can be further differentiated to a neuronal cell type and therefore represent a good model system to study the role of S1P signaling in human neural development. The following sections describe in detail the culture and differentiation of hNP1 cells and two assays to measure S1P signaling in these cells.

  7. FPGA implementation of a ZigBee wireless network control interface to transmit biomedical signals

    Science.gov (United States)

    Gómez López, M. A.; Goy, C. B.; Bolognini, P. C.; Herrera, M. C.

    2011-12-01

    In recent years, cardiac hemodynamic monitors have incorporated new technologies based on wireless sensor networks which can implement different types of communication protocols. More precisely, a digital conductance catheter system recently developed adds a wireless ZigBee module (IEEE 802.15.4 standards) to transmit cardiac signals (ECG, intraventricular pressure and volume) which would allow the physicians to evaluate the patient's cardiac status in a noninvasively way. The aim of this paper is to describe a control interface, implemented in a FPGA device, to manage a ZigBee wireless network. ZigBee technology is used due to its excellent performance including simplicity, low-power consumption, short-range transmission and low cost. FPGA internal memory stores 8-bit signals with which the control interface prepares the information packets. These data were send to the ZigBee END DEVICE module that receives and transmits wirelessly to the external COORDINATOR module. Using an USB port, the COORDINATOR sends the signals to a personal computer for displaying. Each functional block of control interface was assessed by means of temporal diagrams. Three biological signals, organized in packets and converted to RS232 serial protocol, were sucessfully transmitted and displayed in a PC screen. For this purpose, a custom-made graphical software was designed using LabView.

  8. FPGA implementation of a ZigBee wireless network control interface to transmit biomedical signals

    International Nuclear Information System (INIS)

    López, M A Gómez; Goy, C B; Bolognini, P C; Herrera, M C

    2011-01-01

    In recent years, cardiac hemodynamic monitors have incorporated new technologies based on wireless sensor networks which can implement different types of communication protocols. More precisely, a digital conductance catheter system recently developed adds a wireless ZigBee module (IEEE 802.15.4 standards) to transmit cardiac signals (ECG, intraventricular pressure and volume) which would allow the physicians to evaluate the patient's cardiac status in a noninvasively way. The aim of this paper is to describe a control interface, implemented in a FPGA device, to manage a ZigBee wireless network. ZigBee technology is used due to its excellent performance including simplicity, low-power consumption, short-range transmission and low cost. FPGA internal memory stores 8-bit signals with which the control interface prepares the information packets. These data were send to the ZigBee END DEVICE module that receives and transmits wirelessly to the external COORDINATOR module. Using an USB port, the COORDINATOR sends the signals to a personal computer for displaying. Each functional block of control interface was assessed by means of temporal diagrams. Three biological signals, organized in packets and converted to RS232 serial protocol, were successfully transmitted and displayed in a PC screen. For this purpose, a custom-made graphical software was designed using LabView.

  9. 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.

  10. Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

    NARCIS (Netherlands)

    Hu, L.; Chen, Y.; Scanlon, W.G.

    2011-01-01

    The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that

  11. Neural signals of vicarious extinction learning.

    Science.gov (United States)

    Golkar, Armita; Haaker, Jan; Selbing, Ida; Olsson, Andreas

    2016-10-01

    Social transmission of both threat and safety is ubiquitous, but little is known about the neural circuitry underlying vicarious safety learning. This is surprising given that these processes are critical to flexibly adapt to a changeable environment. To address how the expression of previously learned fears can be modified by the transmission of social information, two conditioned stimuli (CS + s) were paired with shock and the third was not. During extinction, we held constant the amount of direct, non-reinforced, exposure to the CSs (i.e. direct extinction), and critically varied whether another individual-acting as a demonstrator-experienced safety (CS + vic safety) or aversive reinforcement (CS + vic reinf). During extinction, ventromedial prefrontal cortex (vmPFC) responses to the CS + vic reinf increased but decreased to the CS + vic safety This pattern of vmPFC activity was reversed during a subsequent fear reinstatement test, suggesting a temporal shift in the involvement of the vmPFC. Moreover, only the CS + vic reinf association recovered. Our data suggest that vicarious extinction prevents the return of conditioned fear responses, and that this efficacy is reflected by diminished vmPFC involvement during extinction learning. The present findings may have important implications for understanding how social information influences the persistence of fear memories in individuals suffering from emotional disorders. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  12. A wideband wireless neural stimulation platform for high-density microelectrode arrays.

    Science.gov (United States)

    Myers, Frank B; Simpson, Jim A; Ghovanloo, Maysam

    2006-01-01

    We describe a system that allows researchers to control an implantable neural microstimulator from a PC via a USB 2.0 interface and a novel dual-carrier wireless link, which provides separate data and power transmission. Our wireless stimulator, Interestim-2B (IS-2B), is a modular device capable of generating controlled-current stimulation pulse trains across 32 sites per module with support for a variety of stimulation schemes (biphasic/monophasic, bipolar/monopolar). We have developed software to generate multi-site stimulation commands for the IS-2B based on streaming data from artificial sensory devices such as cameras and microphones. For PC interfacing, we have developed a USB 2.0 microcontroller-based interface. Data is transmitted using frequency-shift keying (FSK) at 6/12 MHz to achieve a data rate of 3 Mb/s via a pair of rectangular coils. Power is generated using a class-E power amplifier operating at 1 MHz and transmitted via a separate pair of spiral planar coils which are oriented perpendicular to the data coils to minimize cross-coupling. We have successfully demonstrated the operation of the system by applying it as a visual prosthesis. Pulse-frequency modulated stimuli are generated in real-time based on a grayscale image from a webcam. These pulses are projected onto an 11x11 LED matrix that represents a 2D microelectrode array.

  13. A wireless integrated circuit for 100-channel charge-balanced neural stimulation.

    Science.gov (United States)

    Thurgood, B K; Warren, D J; Ledbetter, N M; Clark, G A; Harrison, R R

    2009-12-01

    The authors present the design of an integrated circuit for wireless neural stimulation, along with benchtop and in - vivo experimental results. The chip has the ability to drive 100 individual stimulation electrodes with constant-current pulses of varying amplitude, duration, interphasic delay, and repetition rate. The stimulation is performed by using a biphasic (cathodic and anodic) current source, injecting and retracting charge from the nervous system. Wireless communication and power are delivered over a 2.765-MHz inductive link. Only three off-chip components are needed to operate the stimulator: a 10-nF capacitor to aid in power-supply regulation, a small capacitor (power and command reception. The chip was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process. The chip was able to activate motor fibers to produce muscle twitches via a Utah Slanted Electrode Array implanted in cat sciatic nerve, and to activate sensory fibers to recruit evoked potentials in somatosensory cortex.

  14. 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.

  15. Performance of the Wavelet Transform-Neural Network Based Receiver for DPIM in Diffuse Indoor Optical Wireless Links in Presence of Artificial Light Interference

    Directory of Open Access Journals (Sweden)

    Sujan Rajbhandari

    2009-06-01

    Full Text Available Artificial neural network (ANN has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT with both the time and the frequency resolution provides the exact representation of signal in both domains. Applying these signal processing tools for channel compensation and noise reduction can provide an enhanced performance compared to the traditional tools. In this paper, the slot error rate (SER performance of digital pulse interval modulation (DPIM in diffuse indoor optical wireless (OW links subjected to the artificial light interference (ALI is reported with new receiver structure based on the discrete WT (DWT and ANN. Simulation results show that the DWT-ANN based receiver is very effective in reducing the effect of multipath induced inter-symbol interference (ISI and ALI.

  16. 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.

  17. Wireless Energy Harvesting Using Signals from Multiple Fading Channels

    KAUST Repository

    Chen, Yunfei; Zhao, Nan; Alouini, Mohamed-Slim

    2017-01-01

    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

  18. 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.

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

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    ) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up......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...

  20. Application of neural computing paradigms for signal validation

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Eryurek, E.; Mathai, G.

    1989-01-01

    Signal validation and process monitoring problems often require the prediction of one or more process variables in a system. The feasibility of applying neural network paradigms to relate one variable with a set of other related variables is studied. The backpropagation network (BPN) is applied to develop models of signals from both a commercial power plant and the EBR-II. Modification of the BPN algorithm is studied with emphasis on the speed of network training and the accuracy of prediction. The prediction of process variables in a Westinghouse PWR is presented in this paper

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Magnus Falk

    Full Text Available 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.

  3. Up to 40 Gb/s wireless signal generation and demodulation in 75-110 GHz band using photonic techniques

    DEFF Research Database (Denmark)

    Sambaraju, R.; Zibar, Darko; Caballero Jambrina, Antonio

    2010-01-01

    Record wireless signal capacity of up to 40 Gb/s is demonstrated in the 75-110 GHz band. All-optical OFDM and photonic up-conversion are used for generation and digital coherent detection for demodulation....

  4. Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

    International Nuclear Information System (INIS)

    Huang Jin-Wang; Feng Jiu-Chao

    2014-01-01

    For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)

  5. A digitally assisted, signal folding neural recording amplifier.

    Science.gov (United States)

    Chen, Yi; Basu, Arindam; Liu, Lei; Zou, Xiaodan; Rajkumar, Ramamoorthy; Dawe, Gavin Stewart; Je, Minkyu

    2014-08-01

    A novel signal folding and reconstruction scheme for neural recording applications that exploits the 1/f(n) characteristics of neural signals is described in this paper. The amplified output is 'folded' into a predefined range of voltages by using comparison and reset circuits along with the core amplifier. After this output signal is digitized and transmitted, a reconstruction algorithm can be applied in the digital domain to recover the amplified signal from the folded waveform. This scheme enables the use of an analog-to-digital convertor with less number of bits for the same effective dynamic range. It also reduces the transmission data rate of the recording chip. Both of these features allow power and area savings at the system level. Other advantages of the proposed topology are increased reliability due to the removal of pseudo-resistors, lower harmonic distortion and low-voltage operation. An analysis of the reconstruction error introduced by this scheme is presented along with a behavioral model to provide a quick estimate of the post reconstruction dynamic range. Measurement results from two different core amplifier designs in 65 nm and 180 nm CMOS processes are presented to prove the generality of the proposed scheme in the neural recording applications. Operating from a 1 V power supply, the amplifier in 180 nm CMOS has a gain of 54.2 dB, bandwidth of 5.7 kHz, input referred noise of 3.8 μVrms and power dissipation of 2.52 μW leading to a NEF of 3.1 in spike band. It exhibits a dynamic range of 66 dB and maximum SNDR of 43 dB in LFP band. It also reduces system level power (by reducing the number of bits in the ADC by 2) as well as data rate to 80% of a conventional design. In vivo measurements validate the ability of this amplifier to simultaneously record spike and LFP signals.

  6. Reducing Downlink Signaling Traffic in Wireless Systems Using Mobile-Assisted Scheduling

    OpenAIRE

    Moosavi, Reza; Larsson, Erik G.

    2010-01-01

    We present an idea to reduce the part of the downlink signaling traffic in wireless multiple access systems that contains scheduling information. The theoretical basis of the scheme is that the scheduling decisions made by the base station are correlated with the CSI reports from the mobiles. This correlation can be exploited by the source coding scheme that is used to compress the scheduling maps before they are sent to the mobiles. In the proposed scheme, this idea is implemented by letting...

  7. 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...

  8. 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.

  9. 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).

  10. 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%.

  11. 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 demodulatio...

  12. 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.

  13. 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.

  14. Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

    Science.gov (United States)

    Flekova, L.; Schott, M.

    2017-10-01

    Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.

  15. NMDA Receptor Signaling Is Important for Neural Tube Formation and for Preventing Antiepileptic Drug-Induced Neural Tube Defects.

    Science.gov (United States)

    Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N

    2018-05-16

    Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.

  16. 40-Gb/s PDM-QPSK signal transmission over 160-m wireless distance at W-band.

    Science.gov (United States)

    Xiao, Jiangnan; Yu, Jianjun; Li, Xinying; Xu, Yuming; Zhang, Ziran; Chen, Long

    2015-03-15

    We experimentally demonstrate a W-band optical-wireless transmission system over 160-m wireless distance with a bit rate up to 40 Gb/s. The optical-wireless transmission system adopts optical polarization-division-multiplexing (PDM), multiple-input multiple-output (MIMO) reception and antenna polarization diversity. Using this system, we experimentally demonstrate the 2×2 MIMO wireless delivery of 20- and 40-Gb/s PDM quadrature-phase-shift-keying (PDM-QPSK) signals over 640- and 160-m wireless links, respectively. The bit-error ratios (BERs) of these transmission systems are both less than the forward-error-correction (FEC) threshold of 3.8×10-3.

  17. Faulty node detection in wireless sensor networks using a recurrent neural network

    Science.gov (United States)

    Atiga, Jamila; Mbarki, Nour Elhouda; Ejbali, Ridha; Zaied, Mourad

    2018-04-01

    The wireless sensor networks (WSN) consist of a set of sensors that are more and more used in surveillance applications on a large scale in different areas: military, Environment, Health ... etc. Despite the minimization and the reduction of the manufacturing costs of the sensors, they can operate in places difficult to access without the possibility of reloading of battery, they generally have limited resources in terms of power of emission, of processing capacity, data storage and energy. These sensors can be used in a hostile environment, such as, for example, on a field of battle, in the presence of fires, floods, earthquakes. In these environments the sensors can fail, even in a normal operation. It is therefore necessary to develop algorithms tolerant and detection of defects of the nodes for the network of sensor without wires, therefore, the faults of the sensor can reduce the quality of the surveillance if they are not detected. The values that are measured by the sensors are used to estimate the state of the monitored area. We used the Non-linear Auto- Regressive with eXogeneous (NARX), the recursive architecture of the neural network, to predict the state of a node of a sensor from the previous values described by the functions of time series. The experimental results have verified that the prediction of the State is enhanced by our proposed model.

  18. 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.

    2009-01-01

    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. PMID:19524612

  19. Convolutional neural network-based classification system design with compressed wireless sensor network images.

    Science.gov (United States)

    Ahn, Jungmo; Park, JaeYeon; Park, Donghwan; Paek, Jeongyeup; Ko, JeongGil

    2018-01-01

    With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

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

    International Nuclear Information System (INIS)

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

    2017-01-01

    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. (paper)

  1. 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.

  2. 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.

  3. A Wireless Biomedical Signal Interface System-on-Chip for Body Sensor Networks.

    Science.gov (United States)

    Lei Wang; Guang-Zhong Yang; Jin Huang; Jinyong Zhang; Li Yu; Zedong Nie; Cumming, D R S

    2010-04-01

    Recent years have seen the rapid development of biosensor technology, system-on-chip design, wireless technology. and ubiquitous computing. When assembled into an autonomous body sensor network (BSN), the technologies become powerful tools in well-being monitoring, medical diagnostics, and personal connectivity. In this paper, we describe the first demonstration of a fully customized mixed-signal silicon chip that has most of the attributes required for use in a wearable or implantable BSN. Our intellectual-property blocks include low-power analog sensor interface for temperature and pH, a data multiplexing and conversion module, a digital platform based around an 8-b microcontroller, data encoding for spread-spectrum wireless transmission, and a RF section requiring very few off-chip components. The chip has been fully evaluated and tested by connection to external sensors, and it satisfied typical system requirements.

  4. 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.

  5. 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.

  6. A Wireless and Batteryless Microsystem with Implantable Grid Electrode/3-Dimensional Probe Array for ECoG and Extracellular Neural Recording in Rats

    Directory of Open Access Journals (Sweden)

    Chih-Wei Chang

    2013-04-01

    Full Text Available This paper presents the design and implementation of an integrated wireless microsystem platform that provides the possibility to support versatile implantable neural sensing devices in free laboratory rats. Inductive coupled coils with low dropout regulator design allows true long-term recording without limitation of battery capacity. A 16-channel analog front end chip located on the headstage is designed for high channel account neural signal conditioning with low current consumption and noise. Two types of implantable electrodes including grid electrode and 3D probe array are also presented for brain surface recording and 3D biopotential acquisition in the implanted target volume of tissue. The overall system consumes less than 20 mA with small form factor, 3.9 × 3.9 cm2 mainboard and 1.8 × 3.4 cm2 headstage, is packaged into a backpack for rats. Practical in vivo recordings including auditory response, brain resection tissue and PZT-induced seizures recording demonstrate the correct function of the proposed microsystem. Presented achievements addressed the aforementioned properties by combining MEMS neural sensors, low-power circuit designs and commercial chips into system-level integration.

  7. Neural Correlates of Success and Failure Signals During Neurofeedback Learning.

    Science.gov (United States)

    Radua, Joaquim; Stoica, Teodora; Scheinost, Dustin; Pittenger, Christopher; Hampson, Michelle

    2018-05-15

    Feedback-driven learning, observed across phylogeny and of clear adaptive value, is frequently operationalized in simple operant conditioning paradigms, but it can be much more complex, driven by abstract representations of success and failure. This study investigates the neural processes involved in processing success and failure during feedback learning, which are not well understood. Data analyzed were acquired during a multisession neurofeedback experiment in which ten participants were presented with, and instructed to modulate, the activity of their orbitofrontal cortex with the aim of decreasing their anxiety. We assessed the regional blood-oxygenation-level-dependent response to the individualized neurofeedback signals of success and failure across twelve functional runs acquired in two different magnetic resonance sessions in each of ten individuals. Neurofeedback signals of failure correlated early during learning with deactivation in the precuneus/posterior cingulate and neurofeedback signals of success correlated later during learning with deactivation in the medial prefrontal/anterior cingulate cortex. The intensity of the latter deactivations predicted the efficacy of the neurofeedback intervention in the reduction of anxiety. These findings indicate a role for regulation of the default mode network during feedback learning, and suggest a higher sensitivity to signals of failure during the early feedback learning and to signals of success subsequently. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  8. Analysing 21cm signal with artificial neural network

    Science.gov (United States)

    Shimabukuro, Hayato; a Semelin, Benoit

    2018-05-01

    The 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

  9. 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.

  10. All-Optical envelope detection and fiber transmission of wireless signals by external injection of a DFB laser

    DEFF Research Database (Denmark)

    Prince, Kamau; Tafur Monroy, Idelfonso

    2008-01-01

    We outline a novel method for all-optical envelope detection of wireless signals by exploiting cross-gain modulation effects in a distributed feedback laser operating with optical injection. We successfully demonstrate envelope detection of a 20-GHz carrier amplitude-shift-keying modulated signal...

  11. Simultaneous multichannel signal transfers via chaos in a recurrent neural network.

    Science.gov (United States)

    Soma, Ken-ichiro; Mori, Ryota; Sato, Ryuichi; Furumai, Noriyuki; Nara, Shigetoshi

    2015-05-01

    We propose neural network model that demonstrates the phenomenon of signal transfer between separated neuron groups via other chaotic neurons that show no apparent correlations with the input signal. The model is a recurrent neural network in which it is supposed that synchronous behavior between small groups of input and output neurons has been learned as fragments of high-dimensional memory patterns, and depletion of neural connections results in chaotic wandering dynamics. Computer experiments show that when a strong oscillatory signal is applied to an input group in the chaotic regime, the signal is successfully transferred to the corresponding output group, although no correlation is observed between the input signal and the intermediary neurons. Signal transfer is also observed when multiple signals are applied simultaneously to separate input groups belonging to different memory attractors. In this sense simultaneous multichannel communications are realized, and the chaotic neural dynamics acts as a signal transfer medium in which the signal appears to be hidden.

  12. 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.

  13. Seamless integration of 57.2-Gb/s signal wireline transmission and 100-GHz wireless delivery.

    Science.gov (United States)

    Li, Xinying; Yu, Jianjun; Dong, Ze; Cao, Zizheng; Chi, Nan; Zhang, Junwen; Shao, Yufeng; Tao, Li

    2012-10-22

    We experimentally demonstrated the seamless integration of 57.2-Gb/s signal wireline transmission and 100-GHz wireless delivery adopting polarization-division-multiplexing quadrature-phase-shift-keying (PDM-QPSK) modulation with 400-km single-mode fiber-28 (SMF-28) transmission and 1-m wireless delivery. The X- and Y-polarization components of optical PDM-QPSK baseband signal are simultaneously up-converted to 100 GHz by optical polarization-diversity heterodyne beating, and then independently transmitted and received by two pairs of transmitter and receiver antennas, which make up a 2x2 multiple-input multiple-output (MIMO) wireless link based on microwave polarization multiplexing. At the wireless receiver, a two-stage down conversion is firstly done in analog domain based on balanced mixer and sinusoidal radio frequency (RF) signal, and then in digital domain based on digital signal processing (DSP). Polarization de-multiplexing is realized by constant modulus algorithm (CMA) based on DSP in heterodyne coherent detection. Our experimental results show that more taps are required for CMA when the X- and Y-polarization antennas have different wireless distance.

  14. 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.

  15. 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.

  16. Regulation of adult neural progenitor cell functions by purinergic signaling.

    Science.gov (United States)

    Tang, Yong; Illes, Peter

    2017-02-01

    Extracellular purines are signaling molecules in the neurogenic niches of the brain and spinal cord, where they activate cell surface purinoceptors at embryonic neural stem cells (NSCs) and adult neural progenitor cells (NPCs). Although mRNA and protein are expressed at NSCs/NPCs for almost all subtypes of the nucleotide-sensitive P2X/P2Y, and the nucleoside-sensitive adenosine receptors, only a few of those have acquired functional significance. ATP is sequentially degraded by ecto-nucleotidases to ADP, AMP, and adenosine with agonistic properties for distinct receptor-classes. Nucleotides/nucleosides facilitate or inhibit NSC/NPC proliferation, migration and differentiation. The most ubiquitous effect of all agonists (especially of ATP and ADP) appears to be the facilitation of cell proliferation, usually through P2Y1Rs and sometimes through P2X7Rs. However, usually P2X7R activation causes necrosis/apoptosis of NPCs. Differentiation can be initiated by P2Y2R-activation or P2X7R-blockade. A key element in the transduction mechanism of either receptor is the increase of the intracellular free Ca 2+ concentration, which may arise due to its release from intracellular storage sites (G protein-coupling; P2Y) or due to its passage through the receptor-channel itself from the extracellular space (ATP-gated ion channel; P2X). Further research is needed to clarify how purinergic signaling controls NSC/NPC fate and how the balance between the quiescent and activated states is established with fine and dynamic regulation. GLIA 2017;65:213-230. © 2016 Wiley Periodicals, Inc.

  17. 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.

  18. Neuroendocrine signaling modulates specific neural networks relevant to migraine.

    Science.gov (United States)

    Martins-Oliveira, Margarida; Akerman, Simon; Holland, Philip R; Hoffmann, Jan R; Tavares, Isaura; Goadsby, Peter J

    2017-05-01

    Migraine is a disabling brain disorder involving abnormal trigeminovascular activation and sensitization. Fasting or skipping meals is considered a migraine trigger and altered fasting glucose and insulin levels have been observed in migraineurs. Therefore peptides involved in appetite and glucose regulation including insulin, glucagon and leptin could potentially influence migraine neurobiology. We aimed to determine the effect of insulin (10U·kg -1 ), glucagon (100μg·200μl -1 ) and leptin (0.3, 1 and 3mg·kg -1 ) signaling on trigeminovascular nociceptive processing at the level of the trigeminocervical-complex and hypothalamus. Male rats were anesthetized and prepared for craniovascular stimulation. In vivo electrophysiology was used to determine changes in trigeminocervical neuronal responses to dural electrical stimulation, and phosphorylated extracellular signal-regulated kinases 1 and 2 (pERK1/2) immunohistochemistry to determine trigeminocervical and hypothalamic neural activity; both in response to intravenous administration of insulin, glucagon, leptin or vehicle control in combination with blood glucose analysis. Blood glucose levels were significantly decreased by insulin (pneuronal firing in the trigeminocervical-complex was significantly inhibited by insulin (pmetabolic homeostasis may occur through disturbed glucose regulation and a transient hypothalamic dysfunction. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  19. 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.

  20. 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.

  1. Neural Signaling of Food Healthiness Associated with Emotion Processing.

    Science.gov (United States)

    Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B; Siegrist, Michael

    2016-01-01

    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 regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog 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 signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  2. 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

  3. 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.

  4. 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.

  5. Weak correlations between hemodynamic signals and ongoing neural activity during the resting state

    Science.gov (United States)

    Winder, Aaron T.; Echagarruga, Christina; Zhang, Qingguang; Drew, Patrick J.

    2017-01-01

    Spontaneous fluctuations in hemodynamic signals in the absence of a task or overt stimulation are used to infer neural activity. We tested this coupling by simultaneously measuring neural activity and changes in cerebral blood volume (CBV) in the somatosensory cortex of awake, head-fixed mice during periods of true rest, and during whisker stimulation and volitional whisking. Here we show that neurovascular coupling was similar across states, and large spontaneous CBV changes in the absence of sensory input were driven by volitional whisker and body movements. Hemodynamic signals during periods of rest were weakly correlated with neural activity. Spontaneous fluctuations in CBV and vessel diameter persisted when local neural spiking and glutamatergic input was blocked, and during blockade of noradrenergic receptors, suggesting a non-neuronal origin for spontaneous CBV fluctuations. Spontaneous hemodynamic signals reflect a combination of behavior, local neural activity, and putatively non-neural processes. PMID:29184204

  6. Compressed sensing of ECG signal for wireless system with new fast iterative method.

    Science.gov (United States)

    Tawfic, Israa; Kayhan, Sema

    2015-12-01

    Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  7. 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.

  8. 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

  9. Two time-delay dynamic model on the transmission of malicious signals in wireless sensor network

    International Nuclear Information System (INIS)

    Keshri, Neha; Mishra, Bimal Kumar

    2014-01-01

    Highlights: • Role of time delay to reduce the adversary effect in WSN is explored. • Model with two time delays is proposed to analyse spread of malicious signal in WSN. • Dynamical behaviour of worm-free equilibrium and endemic equilibrium is shown. • Threshold condition for switch of stability are obtained analytically. • Relation between stability and the two time delays is also explored. - Abstract: Deployed in a hostile environment, motes of a Wireless sensor network (WSN) could be easily compromised by the attackers because of several constraints such as limited processing capabilities, memory space, and limited battery life time etc. While transmitting the data to their neighbour motes within the network, motes are easily compromised due to resource constraints. Here time delay can play an efficient role to reduce the adversary effect on motes. In this paper, we propose an epidemic model SEIR (Susceptible–Exposed–Infectious–Recovered) with two time delays to describe the transmission dynamics of malicious signals in wireless sensor network. The first delay accounts for an exposed (latent) period while the second delay is for the temporary immunity period due to multiple worm outbreaks. The dynamical behaviour of worm-free equilibrium and endemic equilibrium is shown from the point of stability which switches under some threshold condition specified by the basic reproduction number. Our results show that the global properties of equilibria also depends on the threshold condition and that latent and temporary immunity period in a mote does not affect the stability, but they play a positive role to control malicious attack. Moreover, numerical simulations are given to support the theoretical analysis

  10. Electrocardiogram (ECG Signal Modeling and Noise Reduction Using Hopfield Neural Networks

    Directory of Open Access Journals (Sweden)

    F. Bagheri

    2013-02-01

    Full Text Available The Electrocardiogram (ECG signal is one of the diagnosing approaches to detect heart disease. In this study the Hopfield Neural Network (HNN is applied and proposed for ECG signal modeling and noise reduction. The Hopfield Neural Network (HNN is a recurrent neural network that stores the information in a dynamic stable pattern. This algorithm retrieves a pattern stored in memory in response to the presentation of an incomplete or noisy version of that pattern. Computer simulation results show that this method can successfully model the ECG signal and remove high-frequency noise.

  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...... transmission of a capacity of 60 Gb/s for four channels (15 Gb/s per channel) at 400-GHz band is successfully achieved, which pushes the data rates enabled by optoelectronics approach beyond the envelope in the frequency range above 300 GHz. Besides those, this study also validates the potential of bridging...

  12. Seismic signal auto-detecing from different features by using Convolutional Neural Network

    Science.gov (United States)

    Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

    2017-12-01

    We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

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

    Science.gov (United States)

    Park, Hyeong-Dong; Tallon-Baudry, Catherine

    2014-05-05

    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.

  14. 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.

  15. Least Squares Neural Network-Based Wireless E-Nose System Using an SnO₂ Sensor Array.

    Science.gov (United States)

    Shahid, Areej; Choi, Jong-Hyeok; Rana, Abu Ul Hassan Sarwar; Kim, Hyun-Seok

    2018-05-06

    Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH₄) and carbon monoxide (CO), using an array of SnO₂ gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH₄ and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.

  16. Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

    Science.gov (United States)

    Hoomod, Haider K.; Kareem Jebur, Tuka

    2018-05-01

    Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

  17. 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.

  18. 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.

  19. Application of neural networks to signal prediction in nuclear power plant

    International Nuclear Information System (INIS)

    Wan Joo Kim; Soon Heung Chang; Byung Ho Lee

    1993-01-01

    This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well

  20. Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

    Science.gov (United States)

    Handayani, N.; Akbar, Y.; Khotimah, S. N.; Haryanto, F.; Arif, I.; Taruno, W. P.

    2016-03-01

    This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons.

  1. Dangerous mating systems: signal complexity, signal content and neural capacity in spiders.

    Science.gov (United States)

    Herberstein, M E; Wignall, A E; Hebets, E A; Schneider, J M

    2014-10-01

    Spiders are highly efficient predators in possession of exquisite sensory capacities for ambushing prey, combined with machinery for launching rapid and determined attacks. As a consequence, any sexually motivated approach carries a risk of ending up as prey rather than as a mate. Sexual selection has shaped courtship to effectively communicate the presence, identity, motivation and/or quality of potential mates, which help ameliorate these risks. Spiders communicate this information via several sensory channels, including mechanical (e.g. vibrational), visual and/or chemical, with examples of multimodal signalling beginning to emerge in the literature. The diverse environments that spiders inhabit have further shaped courtship content and form. While our understanding of spider neurobiology remains in its infancy, recent studies are highlighting the unique and considerable capacities of spiders to process and respond to complex sexual signals. As a result, the dangerous mating systems of spiders are providing important insights into how ecology shapes the evolution of communication systems, with future work offering the potential to link this complex communication with its neural processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. [A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

    Science.gov (United States)

    Zhang, Y; Liu, A; Yu, K

    1999-06-01

    A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

  3. A GI Proposal to Display ECG Digital Signals Wirelessly Real-time Transmitted onto a Remote PC

    Directory of Open Access Journals (Sweden)

    Marius Corneliu Rosu

    2018-03-01

    Full Text Available The sensors, as wireless communication system, comply the 7-layer model Open Systems Interconnection (OSI. In this paper, a point-to-point transmission model was used. The ECG signal is transmitted from the Router Sensor (RS to an end Coordinator Node (CN plugged-in to the laptop via USB port; RS acquires ECG signal in analogical mode, and is also responsible with sampling, quantization and sending it wirelessly direct to CN. The distance between RS and CN is a single-hop transmission, and does not exceed the range of the XBeeS2Pro transceivers. The communication protocol is ZigBee. Remote viewing of the transmitted signal is performed on a Graphical Interface (GI written under MATLAB, after the signal has been digitized; the choice of MATLAB was motivated by future developments. Particular aspects will be highlighted, so that the reader to be edified about the results obtained during laboratory experiments. Recording demonstrate that the purpose exposed in title has been reached: Direct link in Real-Time was established, and the digital ECG signal received is reconstituted accurately on MATLAB GI; signal received on laptop is compared with the analog signal displayed on oscilloscope.

  4. 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.

  5. Detecting modulated signals in modulated noise: (II) neural thresholds in the songbird forebrain.

    Science.gov (United States)

    Bee, Mark A; Buschermöhle, Michael; Klump, Georg M

    2007-10-01

    Sounds in the real world fluctuate in amplitude. The vertebrate auditory system exploits patterns of amplitude fluctuations to improve signal detection in noise. One experimental paradigm demonstrating these general effects has been used in psychophysical studies of 'comodulation detection difference' (CDD). The CDD effect refers to the fact that thresholds for detecting a modulated, narrowband noise signal are lower when the envelopes of flanking bands of modulated noise are comodulated with each other, but fluctuate independently of the signal compared with conditions in which the envelopes of the signal and flanking bands are all comodulated. Here, we report results from a study of the neural correlates of CDD in European starlings (Sturnus vulgaris). We manipulated: (i) the envelope correlations between a narrowband noise signal and a masker comprised of six flanking bands of noise; (ii) the signal onset delay relative to masker onset; (iii) signal duration; and (iv) masker spectrum level. Masked detection thresholds were determined from neural responses using signal detection theory. Across conditions, the magnitude of neural CDD ranged between 2 and 8 dB, which is similar to that reported in a companion psychophysical study of starlings [U. Langemann & G.M. Klump (2007) Eur. J. Neurosci., 26, 1969-1978]. We found little evidence to suggest that neural CDD resulted from the across-channel processing of auditory grouping cues related to common envelope fluctuations and synchronous onsets between the signal and flanking bands. We discuss a within-channel model of peripheral processing that explains many of our results.

  6. 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.

  7. Calcium signaling mediates five types of cell morphological changes to form neural rosettes.

    Science.gov (United States)

    Hříbková, Hana; Grabiec, Marta; Klemová, Dobromila; Slaninová, Iva; Sun, Yuh-Man

    2018-02-12

    Neural rosette formation is a critical morphogenetic process during neural development, whereby neural stem cells are enclosed in rosette niches to equipoise proliferation and differentiation. How neural rosettes form and provide a regulatory micro-environment remains to be elucidated. We employed the human embryonic stem cell-based neural rosette system to investigate the structural development and function of neural rosettes. Our study shows that neural rosette formation consists of five types of morphological change: intercalation, constriction, polarization, elongation and lumen formation. Ca 2+ signaling plays a pivotal role in the five steps by regulating the actions of the cytoskeletal complexes, actin, myosin II and tubulin during intercalation, constriction and elongation. These, in turn, control the polarizing elements, ZO-1, PARD3 and β-catenin during polarization and lumen production for neural rosette formation. We further demonstrate that the dismantlement of neural rosettes, mediated by the destruction of cytoskeletal elements, promotes neurogenesis and astrogenesis prematurely, indicating that an intact rosette structure is essential for orderly neural development. © 2018. Published by The Company of Biologists Ltd.

  8. 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.

  9. 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

  10. Canonical Wnt signaling transiently stimulates proliferation and enhances neurogenesis in neonatal neural progenitor cultures

    International Nuclear Information System (INIS)

    Hirsch, Cordula; Campano, Louise M.; Woehrle, Simon; Hecht, Andreas

    2007-01-01

    Canonical Wnt signaling triggers the formation of heterodimeric transcription factor complexes consisting of β-catenin and T cell factors, and thereby controls the execution of specific genetic programs. During the expansion and neurogenic phases of embryonic neural development canonical Wnt signaling initially controls proliferation of neural progenitor cells, and later neuronal differentiation. Whether Wnt growth factors affect neural progenitor cells postnatally is not known. Therefore, we have analyzed the impact of Wnt signaling on neural progenitors isolated from cerebral cortices of newborn mice. Expression profiling of pathway components revealed that these cells are fully equipped to respond to Wnt signals. However, Wnt pathway activation affected only a subset of neonatal progenitors and elicited a limited increase in proliferation and neuronal differentiation in distinct subsets of cells. Moreover, Wnt pathway activation only transiently stimulated S-phase entry but did not support long-term proliferation of progenitor cultures. The dampened nature of the Wnt response correlates with the predominant expression of inhibitory pathway components and the rapid actuation of negative feedback mechanisms. Interestingly, in differentiating cell cultures activation of canonical Wnt signaling reduced Hes1 and Hes5 expression suggesting that during postnatal neural development, Wnt/β-catenin signaling enhances neurogenesis from progenitor cells by interfering with Notch pathway activity

  11. Filtering and spectral processing of 1-D signals using cellular neural networks

    NARCIS (Netherlands)

    Moreira-Tamayo, O.; Pineda de Gyvez, J.

    1996-01-01

    This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This

  12. Protein signaling pathways in differentiation of neural stem cells

    Czech Academy of Sciences Publication Activity Database

    Skalníková, Helena; Vodička, Petr; Pelech, S.; Motlík, Jan; Gadher, S. J.; Kovářová, Hana

    2008-01-01

    Roč. 8, - (2008), s. 4547-4559 ISSN 1615-9853 R&D Projects: GA MŠk 1M0538 Institutional research plan: CEZ:AV0Z50450515 Keywords : antibody microarray * differentiation * neural stem cells Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.586, year: 2008

  13. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  14. 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.

  15. Neural network committees for finger joint angle estimation from surface EMG signals

    Directory of Open Access Journals (Sweden)

    Reddy Narender P

    2009-01-01

    Full Text Available Abstract Background In virtual reality (VR systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models. Methodology SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects. Results There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion. Conclusion Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.

  16. The development of a PZT-based microdrive for neural signal recording

    International Nuclear Information System (INIS)

    Park, Sangkyu; Yoon, Euisung; Park, Sukho; Lee, Sukchan; Shin, Hee-sup; Park, Hyunjun; Kim, Byungkyu; Kim, Daesoo; Park, Jongoh

    2008-01-01

    A hand-controlled microdrive has been used to obtain neural signals from rodents such as rats and mice. However, it places severe physical stress on the rodents during its manipulation, and this stress leads to alertness in the mice and low efficiency in obtaining neural signals from the mice. To overcome this issue, we developed a novel microdrive, which allows one to adjust the electrodes by a piezoelectric device (PZT) with high precision. Its mass is light enough to install on the mouse's head. The proposed microdrive has three H-type PZT actuators and their guiding structure. The operation principle of the microdrive is based on the well known inchworm mechanism. When the three PZT actuators are synchronized, linear motion of the electrode is produced along the guiding structure. The electrodes used for the recording of the neural signals from neuron cells were fixed at one of the PZT actuators. Our proposed microdrive has an accuracy of about 400 nm and a long stroke of about 5 mm. In response to formalin-induced pain, single unit activities are robustly measured at the thalamus with electrodes whose vertical depth is adjusted by the microdrive under urethane anesthesia. In addition, the microdrive was efficient in detecting neural signals from mice that were moving freely. Thus, the present study suggests that the PZT-based microdrive could be an alternative for the efficient detection of neural signals from mice during behavioral states without any stress to the mice. (technical note)

  17. 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...... difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens)....

  18. ADAM13 Induces Cranial Neural Crest by Cleaving Class B Ephrins and Regulating Wnt Signaling

    OpenAIRE

    Wei, Shuo; Xu, Guofeng; Bridges, Lance C.; Williams, Phoebe; White, Judith M.; DeSimone, Douglas W.

    2010-01-01

    The cranial neural crest (CNC) are multipotent embryonic cells that contribute to craniofacial structures and other cells and tissues of the vertebrate head. During embryogenesis, CNC is induced at the neural plate boundary through the interplay of several major signaling pathways. Here we report that the metalloproteinase activity of ADAM13 is required for early induction of CNC in Xenopus. In both cultured cells and X. tropicalis embryos, membrane-bound Ephrins (Efns) B1 and B2 were identif...

  19. 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.

  20. Hepatocyte growth factor/scatter factor-MET signaling in neural crest-derived melanocyte development.

    Science.gov (United States)

    Kos, L; Aronzon, A; Takayama, H; Maina, F; Ponzetto, C; Merlino, G; Pavan, W

    1999-02-01

    The mechanisms governing development of neural crest-derived melanocytes, and how alterations in these pathways lead to hypopigmentation disorders, are not completely understood. Hepatocyte growth factor/scatter factor (HGF/SF) signaling through the tyrosine-kinase receptor, MET, is capable of promoting the proliferation, increasing the motility, and maintaining high tyrosinase activity and melanin synthesis of melanocytes in vitro. In addition, transgenic mice that ubiquitously overexpress HGF/SF demonstrate hyperpigmentation in the skin and leptomenigenes and develop melanomas. To investigate whether HGF/ SF-MET signaling is involved in the development of neural crest-derived melanocytes, transgenic embryos, ubiquitously overexpressing HGF/SF, were analyzed. In HGF/SF transgenic embryos, the distribution of melanoblasts along the characteristic migratory pathway was not affected. However, additional ectopically localized melanoblasts were also observed in the dorsal root ganglia and neural tube, as early as 11.5 days post coitus (p.c.). We utilized an in vitro neural crest culture assay to further explore the role of HGF/SF-MET signaling in neural crest development. HGF/SF added to neural crest cultures increased melanoblast number, permitted differentiation into pigmented melanocytes, promoted melanoblast survival, and could replace mast-cell growth factor/Steel factor (MGF) in explant cultures. To examine whether HGF/SF-MET signaling is required for the proper development of melanocytes, embryos with a targeted Met null mutation (Met-/-) were analysed. In Met-/- embryos, melanoblast number and location were not overtly affected up to 14 days p.c. These results demonstrate that HGF/SF-MET signaling influences, but is not required for, the initial development of neural crest-derived melanocytes in vivo and in vitro.

  1. A Spike Neural Controller for Traffic Load Parameter with Priority-Based Rate in Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Nadia Adnan Shiltagh

    2015-11-01

    Full Text Available Wireless Multimedia Sensor Networks (WMSNs are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC. The Modify Spike Neural Network controller (MSNC can calculate the appropriate traffic load parameter μ for each parent node and then use in the EWPBRC algorithm to estimate the transmission rate of parent nodes and then assign a suitable transmission rate for each child node. A comparative study between (MSNTLP with EWBPRC and fuzzy logic controller for traffic load parameter with Exponential Weight of Priority Based Rate Control algorithm (FTLP with EWBPRC algorithm shows that the (MSNTLP with EWBPRC is more efficient than (FTLP with EWBPRC algorithm in terms of packet loss, queue delay and throughput. Another comparative study between (MSNTLP with EWBPRC and EWBPRC with fixed traffic load parameter (µ shows that the MSNTLP with EWBPRC is more efficient than EWBPRC with fixed traffic load parameter (µ in terms of packet loss ratio and queue delay. A simulation process is developed and tested using the network simulator _2 (NS2 in a computer having the following properties: windows 7 (64-bit, core i7, RAM 8GB, hard 1TB.

  2. Genetic deletion of Rnd3 in neural stem cells promotes proliferation via upregulation of Notch signaling.

    Science.gov (United States)

    Dong, Huimin; Lin, Xi; Li, Yuntao; Hu, Ronghua; Xu, Yang; Guo, Xiaojie; La, Qiong; Wang, Shun; Fang, Congcong; Guo, Junli; Li, Qi; Mao, Shanping; Liu, Baohui

    2017-10-31

    Rnd3, a Rho GTPase, is involved in the inhibition of actin cytoskeleton dynamics through the Rho kinase-dependent signaling pathway. We previously demonstrated that mice with genetic deletion of Rnd3 developed a markedly larger brain compared with wild-type mice. Here, we demonstrate that Rnd3 knockout mice developed an enlarged subventricular zone, and we identify a novel role for Rnd3 as an inhibitor of Notch signaling in neural stem cells. Rnd3 deficiency, both in vivo and in vitro , resulted in increased levels of Notch intracellular domain protein. This led to enhanced Notch signaling and promotion of aberrant neural stem cell growth, thereby resulting in a larger subventricular zone and a markedly larger brain. Inhibition of Notch activity abrogated this aberrant neural stem cell growth.

  3. SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

    Science.gov (United States)

    Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

    2013-04-30

    The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

  4. ETOH inhibits embryonic neural stem/precursor cell proliferation via PLD signaling

    International Nuclear Information System (INIS)

    Fujita, Yuko; Hiroyama, Masami; Sanbe, Atsushi; Yamauchi, Junji; Murase, Shoko; Tanoue, Akito

    2008-01-01

    While a mother's excessive alcohol consumption during pregnancy is known to have adverse effects on fetal neural development, little is known about the underlying mechanism of these effects. In order to investigate these mechanisms, we investigated the toxic effect of ethanol (ETOH) on neural stem/precursor cell (NSC) proliferation. In cultures of NSCs, phospholipase D (PLD) is activated following stimulation with epidermal growth factor (EGF) and fibroblast growth factor 2 (FGF2). Exposure of NSCs to ETOH suppresses cell proliferation, while it has no effect on cell death. Phosphatidic acid (PA), which is a signaling messenger produced by PLD, reverses ETOH inhibition of NSC proliferation. Blocking the PLD signal by 1-butanol suppresses the proliferation. ETOH-induced suppression of NSC proliferation and the protective effect of PA for ETOH-induced suppression are mediated through extracellular signal-regulated kinase signaling. These results indicate that exposure to ETOH impairs NSC proliferation by altering the PLD signaling pathway

  5. Modeling and Characterization of Capacitive Elements With Tissue as Dielectric Material for Wireless Powering of Neural Implants.

    Science.gov (United States)

    Erfani, Reza; Marefat, Fatemeh; Sodagar, Amir M; Mohseni, Pedram

    2018-05-01

    This paper reports on the modeling and characterization of capacitive elements with tissue as the dielectric material, representing the core building block of a capacitive link for wireless power transfer to neural implants. Each capacitive element consists of two parallel plates that are aligned around the tissue layer and incorporate a grounded, guarded, capacitive pad to mitigate the adverse effect of stray capacitances and shield the plates from external interfering electric fields. The plates are also coated with a biocompatible, insulating, coating layer on the inner side of each plate in contact with the tissue. A comprehensive circuit model is presented that accounts for the effect of the coating layers and is validated by measurements of the equivalent capacitance as well as impedance magnitude/phase of the parallel plates over a wide frequency range of 1 kHz-10 MHz. Using insulating coating layers of Parylene-C at a thickness of and Parylene-N at a thickness of deposited on two sets of parallel plates with different sizes and shapes of the guarded pad, our modeling and characterization results accurately capture the effect of the thickness and electrical properties of the coating layers on the behavior of the capacitive elements over frequency and with different tissues.

  6. Using neural networks to enhance the Higgs boson signal at hadron colliders

    International Nuclear Information System (INIS)

    Field, R.D.; Kanev, Y.; Tayebnejad, M.; Griffin, P.A.

    1995-01-01

    Neural networks are used to help distinguish the ZZ → ell + ell - -jet-jet signal produced by the decay of a 400 GeV Higgs boson at a proton-proton collider energy of 15 TeV from the ''ordinary'' QCD Z + jets background. The ideal case where only one event at a time enters the detector (no pile-up) and the case of multiple interactions per beam crossing (pile-up) are examined. In both cases, when used in conjunction with the standard cuts, neural networks provide an additional signal to background enhancement

  7. Neural signal processing for identifying failed fuel rods in nuclear reactors

    International Nuclear Information System (INIS)

    Seixas, Jose M. de; Soares Filho, William; Pereira, Wagner C.A.; Teles, Claudio C.B.

    2002-01-01

    Ultrasonic pulses were used for automatic detection of failed nuclear fuel rods. For experimental tests of the proposed method, an assembly prototype of 16 x 16 rods was built by using genuine rods but without fuel inside (just air). Some rods were partially filled with water to simulate cracked rods. Using neural signal processing on the received echoes of the emitted ultrasonic pulses, a detection efficiency of 97% was obtained. Neural detection is shown to outperform other classical discriminating methods and can also reveal important features of the signal structure of the received echoes. (author)

  8. A low-power current-reuse dual-band analog front-end for multi-channel neural signal recording.

    Science.gov (United States)

    Sepehrian, H; Gosselin, B

    2014-01-01

    Thoroughly studying the brain activity of freely moving subjects requires miniature data acquisition systems to measure and wirelessly transmit neural signals in real time. In this application, it is mandatory to simultaneously record the bioelectrical activity of a large number of neurons to gain a better knowledge of brain functions. However, due to limitations in transferring the entire raw data to a remote base station, employing dedicated data reduction techniques to extract the relevant part of neural signals is critical to decrease the amount of data to transfer. In this work, we present a new dual-band neural amplifier to separate the neuronal spike signals (SPK) and the local field potential (LFP) simultaneously in the analog domain, immediately after the pre-amplification stage. By separating these two bands right after the pre-amplification stage, it is possible to process LFP and SPK separately. As a result, the required dynamic range of the entire channel, which is determined by the signal-to-noise ratio of the SPK signal of larger bandwidth, can be relaxed. In this design, a new current-reuse low-power low-noise amplifier and a new dual-band filter that separates SPK and LFP while saving capacitors and pseudo resistors. A four-channel dual-band (SPK, LFP) analog front-end capable of simultaneously separating SPK and LFP is implemented in a TSMC 0.18 μm technology. Simulation results present a total power consumption per channel of 3.1 μw for an input referred noise of 3.28 μV and a NEF for 2.07. The cutoff frequency of the LFP band is fc=280 Hz, and fL=725 Hz and fL=11.2 KHz for SPK, with 36 dB gain for LFP band 46 dB gain for SPK band.

  9. Full-duplex bidirectional transmission of 10-Gb/s millimeter-wave QPSK signal in E-band optical wireless link.

    Science.gov (United States)

    Fang, Yuan; Yu, Jianjun; Chi, Nan; Xiao, Jiangnan

    2014-01-27

    We experimentally demonstrated full-duplex bidirectional transmission of 10-Gb/s millimeter-wave (mm-wave) quadrature phase shift keying (QPSK) signal in E-band (71-76 GHz and 81-86 GHz) optical wireless link. Single-mode fibers (SMF) are connected at both sides of the antenna for uplink and downlink which realize 40-km SMF and 2-m wireless link for bidirectional transmission simultaneously. We utilized multi-level modulation format and coherent detection in such E-band optical wireless link for the first time. Mm-wave QPSK signal is generated by photonic technique to increase spectrum efficiency and received signal is coherently detected to improve receiver sensitivity. After the coherent detection, digital signal processing is utilized to compensate impairments of devices and transmission link.

  10. Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles.

    Science.gov (United States)

    Smith, Alec; Lohrenz, Terry; King, Justin; Montague, P Read; Camerer, Colin F

    2014-07-22

    Groups of humans routinely misassign value to complex future events, especially in settings involving the exchange of resources. If properly structured, experimental markets can act as excellent probes of human group-level valuation mechanisms during pathological overvaluations--price bubbles. The connection between the behavioral and neural underpinnings of such phenomena has been absent, in part due to a lack of enabling technology. We used a multisubject functional MRI paradigm to measure neural activity in human subjects participating in experimental asset markets in which endogenous price bubbles formed and crashed. Although many ideas exist about how and why such bubbles may form and how to identify them, our experiment provided a window on the connection between neural responses and behavioral acts (buying and selling) that created the bubbles. We show that aggregate neural activity in the nucleus accumbens (NAcc) tracks the price bubble and that NAcc activity aggregated within a market predicts future price changes and crashes. Furthermore, the lowest-earning subjects express a stronger tendency to buy as a function of measured NAcc activity. Conversely, we report a signal in the anterior insular cortex in the highest earners that precedes the impending price peak, is associated with a higher propensity to sell in high earners, and that may represent a neural early warning signal in these subjects. Such markets could be a model system to understand neural and behavior mechanisms in other settings where emergent group-level activity exhibits mistaken belief or valuation.

  11. Mediator Med23 deficiency enhances neural differentiation of murine embryonic stem cells through modulating BMP signaling.

    Science.gov (United States)

    Zhu, Wanqu; Yao, Xiao; Liang, Yan; Liang, Dan; Song, Lu; Jing, Naihe; Li, Jinsong; Wang, Gang

    2015-02-01

    Unraveling the mechanisms underlying early neural differentiation of embryonic stem cells (ESCs) is crucial to developing cell-based therapies of neurodegenerative diseases. Neural fate acquisition is proposed to be controlled by a 'default' mechanism, for which the molecular regulation is not well understood. In this study, we investigated the functional roles of Mediator Med23 in pluripotency and lineage commitment of murine ESCs. Unexpectedly, we found that, despite the largely unchanged pluripotency and self-renewal of ESCs, Med23 depletion rendered the cells prone to neural differentiation in different differentiation assays. Knockdown of two other Mediator subunits, Med1 and Med15, did not alter the neural differentiation of ESCs. Med15 knockdown selectively inhibited endoderm differentiation, suggesting the specificity of cell fate control by distinctive Mediator subunits. Gene profiling revealed that Med23 depletion attenuated BMP signaling in ESCs. Mechanistically, MED23 modulated Bmp4 expression by controlling the activity of ETS1, which is involved in Bmp4 promoter-enhancer communication. Interestingly, med23 knockdown in zebrafish embryos also enhanced neural development at early embryogenesis, which could be reversed by co-injection of bmp4 mRNA. Taken together, our study reveals an intrinsic, restrictive role of MED23 in early neural development, thus providing new molecular insights for neural fate determination. © 2015. Published by The Company of Biologists Ltd.

  12. Enhancing the top-quark signal at Fermilab Tevatron using neural nets

    International Nuclear Information System (INIS)

    Ametller, L.; Garrido, L.; Talavera, P.

    1994-01-01

    We show, in agreement with previous studies, that neural nets can be useful for top-quark analysis at the Fermilab Tevatron. The main features of t bar t and background events in a mixed sample are projected on a single output, which controls the efficiency, purity, and statistical significance of the t bar t signal. We consider a feed-forward multilayer neural net for the CDF reported top-quark mass, using six kinematical variables as inputs. Our main results are based on the exhaustive comparison of the neural net performances with those obtainable from the standard experimental analysis, by imposing different sets of linear cuts over the same variables, showing how the neural net approach improves the standard analysis results

  13. 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.

  14. Multiple-failure signal validation in nuclear power plants using artificial neural networks

    International Nuclear Information System (INIS)

    Fantoni, P.F.; Mazzola, A.

    1996-01-01

    The possibility of using a neural network to validate process signals during normal and abnormal plant conditions is explored. In boiling water reactor plants, signal validation is used to generate reliable thermal limits calculation and to supply reliable inputs to other computerized systems that support the operator during accident scenarios. The way that autoassociative neural networks can promptly detect faulty process signal measurements and produce a best estimate of the actual process values even in multifailure situations is shown. A method was developed to train the network for multiple sensor-failure detection, based on a random failure simulation algorithm. Noise was artificially added to the input to evaluate the network's ability to respond in a very low signal-to-noise ratio environment. Training and test data sets were simulated by the real-time transient simulator code APROS

  15. An input feature selection method applied to fuzzy neural networks for signal esitmation

    International Nuclear Information System (INIS)

    Na, Man Gyun; Sim, Young Rok

    2001-01-01

    It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

  16. CXXC5 is a novel BMP4-regulated modulator of Wnt signaling in neural stem cells

    Czech Academy of Sciences Publication Activity Database

    Andersson, T.; Södersten, E.; Duckworth, J.K.; Cascante, A.; Fritz, N.; Sacchetti, P.; Červenka, I.; Bryja, Vítězslav; Hermanson, O.

    2008-01-01

    Roč. 284, č. 6 (2008), s. 3672-3681 ISSN 0021-9258 Grant - others:GA AV ČR(CZ) KJB501630801 Institutional research plan: CEZ:AV0Z50040507; CEZ:AV0Z50040702 Keywords : Wnt signaling * CXXC5 * neural stem cells Subject RIV: BO - Biophysics Impact factor: 5.520, year: 2008

  17. Sensor signal analysis by neural networks for surveillance in nuclear reactors

    International Nuclear Information System (INIS)

    Keyvan, S.; Rabelo, L.C.

    1992-01-01

    The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data, and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking.

    Science.gov (United States)

    Zacksenhouse, Miriam; Lebedev, Mikhail A; Nicolelis, Miguel A L

    2014-01-01

    What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA) is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i) reaching experiments with Brain-Machine Interface (BMI), and (ii) locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.

  3. Signal-Independent Timescale Analysis (SITA and its Application for Neural Coding during Reaching and Walking

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2014-08-01

    Full Text Available What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i reaching experiments with Brain-Machine Interface (BMI, and (ii locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.

  4. Damage localization using a power-efficient distributed on-board signal processing algorithm in a wireless sensor network

    International Nuclear Information System (INIS)

    Liu, Lei; Liu, Shuntao; Yuan, Fuh-Gwo

    2012-01-01

    A distributed on-board algorithm that is embedded and executed within a group of wireless sensors to locate structural damages in isotropic plates is presented. The algorithm is based on an energy-decay model of Lamb waves and singular value decomposition (SVD) to determine damage locations. A sensor group consists of a small number of sensors, each of which independently collects wave signals and evaluates wave energy upon an external triggering signal sent from a base station. The energy values, usually a few bytes in length, are then sent to the base station to determine the presence and location of damages. In comparison with traditional centralized approaches in which whole datasets are required to be transmitted, the proposed algorithm yields much less wireless communication traffic, yet with a modest amount of computation required within sensors. Experiments have shown that the algorithm is robust to locate damage for isotropic plate structures and is very power efficient, with more than an order-of-magnitude power saving

  5. The COP9 signalosome converts temporal hormone signaling to spatial restriction on neural competence.

    Directory of Open Access Journals (Sweden)

    Yi-Chun Huang

    2014-11-01

    Full Text Available During development, neural competence is conferred and maintained by integrating spatial and temporal regulations. The Drosophila sensory bristles that detect mechanical and chemical stimulations are arranged in stereotypical positions. The anterior wing margin (AWM is arrayed with neuron-innervated sensory bristles, while posterior wing margin (PWM bristles are non-innervated. We found that the COP9 signalosome (CSN suppresses the neural competence of non-innervated bristles at the PWM. In CSN mutants, PWM bristles are transformed into neuron-innervated, which is attributed to sustained expression of the neural-determining factor Senseless (Sens. The CSN suppresses Sens through repression of the ecdysone signaling target gene broad (br that encodes the BR-Z1 transcription factor to activate sens expression. Strikingly, CSN suppression of BR-Z1 is initiated at the prepupa-to-pupa transition, leading to Sens downregulation, and termination of the neural competence of PWM bristles. The role of ecdysone signaling to repress br after the prepupa-to-pupa transition is distinct from its conventional role in activation, and requires CSN deneddylating activity and multiple cullins, the major substrates of deneddylation. Several CSN subunits physically associate with ecdysone receptors to represses br at the transcriptional level. We propose a model in which nuclear hormone receptors cooperate with the deneddylation machinery to temporally shutdown downstream target gene expression, conferring a spatial restriction on neural competence at the PWM.

  6. The response of early neural genes to FGF signaling or inhibition of BMP indicate the absence of a conserved neural induction module

    Directory of Open Access Journals (Sweden)

    Rogers Crystal D

    2011-12-01

    Full Text Available Abstract Background The molecular mechanism that initiates the formation of the vertebrate central nervous system has long been debated. Studies in Xenopus and mouse demonstrate that inhibition of BMP signaling is sufficient to induce neural tissue in explants or ES cells respectively, whereas studies in chick argue that instructive FGF signaling is also required for the expression of neural genes. Although additional signals may be involved in neural induction and patterning, here we focus on the roles of BMP inhibition and FGF8a. Results To address the question of necessity and sufficiency of BMP inhibition and FGF signaling, we compared the temporal expression of the five earliest genes expressed in the neuroectoderm and determined their requirements for induction at the onset of neural plate formation in Xenopus. Our results demonstrate that the onset and peak of expression of the genes vary and that they have different regulatory requirements and are therefore unlikely to share a conserved neural induction regulatory module. Even though all require inhibition of BMP for expression, some also require FGF signaling; expression of the early-onset pan-neural genes sox2 and foxd5α requires FGF signaling while other early genes, sox3, geminin and zicr1 are induced by BMP inhibition alone. Conclusions We demonstrate that BMP inhibition and FGF signaling induce neural genes independently of each other. Together our data indicate that although the spatiotemporal expression patterns of early neural genes are similar, the mechanisms involved in their expression are distinct and there are different signaling requirements for the expression of each gene.

  7. ADAM13 Induces Cranial Neural Crest by Cleaving Class B Ephrins and Regulating Wnt Signaling

    Science.gov (United States)

    Wei, Shuo; Xu, Guofeng; Bridges, Lance C.; Williams, Phoebe; White, Judith M.; DeSimone, Douglas W.

    2010-01-01

    SUMMARY The cranial neural crest (CNC) are multipotent embryonic cells that contribute to craniofacial structures and other cells and tissues of the vertebrate head. During embryogenesis, CNC is induced at the neural plate boundary through the interplay of several major signaling pathways. Here we report that the metalloproteinase activity of ADAM13 is required for early induction of CNC in Xenopus. In both cultured cells and X. tropicalis embryos, membrane-bound Ephrins (Efns) B1 and B2 were identified as substrates for ADAM13. ADAM13 upregulates canonical Wnt signaling and early expression of the transcription factor snail2, whereas EfnB1 inhibits the canonical Wnt pathway and snail2 expression. We propose that by cleaving class B Efns, ADAM13 promotes canonical Wnt signaling and early CNC induction. PMID:20708595

  8. 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.

  9. Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

    International Nuclear Information System (INIS)

    Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

    2015-01-01

    The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately

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

    International Nuclear Information System (INIS)

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

    2009-01-01

    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.

  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. Explaining neural signals in human visual cortex with an associative learning model.

    Science.gov (United States)

    Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

    2012-08-01

    "Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

  13. Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

    International Nuclear Information System (INIS)

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

    1999-01-01

    A feedforward neural network with two hidden layers is used to forecast major and minor disruptive instabilities in TEXT tokamak discharges. Using the experimental data of soft X ray signals as input data, the neural network is trained with one disruptive plasma discharge, and a different disruptive discharge is used for validation. After being properly trained, the networks, with the same set of weights, are used to forecast disruptions in two other plasma discharges. It is observed that the neural network is able to predict the occurrence of a disruption more than 3 ms in advance. This time interval is almost 3 times longer than the one already obtained previously when a magnetic signal from a Mirnov coil was used to feed the neural networks. Visually no indication of an upcoming disruption is seen from the experimental data this far back from the time of disruption. Finally, by observing the predictive behaviour of the network for the disruptive discharges analysed and comparing the soft X ray data with the corresponding magnetic experimental signal, it is conjectured about where inside the plasma column the disruption first started. (author)

  14. A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

    Science.gov (United States)

    Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

    2009-01-01

    For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

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

    Directory of Open Access Journals (Sweden)

    Daewoo eLee

    2015-08-01

    Full Text Available 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.

  16. 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.

  17. Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise

    Science.gov (United States)

    Keeler, James D.; Pichler, Elgar E.; Ross, John

    1989-03-01

    We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may ``quench'' the neural patterns of activity to enhance the ability to learn details.

  18. 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.

  19. Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance.

    Science.gov (United States)

    Dellett, Margaret; Hu, Wanzhou; Papadaki, Vasiliki; Ohnuma, Shin-ichi

    2012-04-01

    The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

  20. Notch signaling patterns neurogenic ectoderm and regulates the asymmetric division of neural progenitors in sea urchin embryos.

    Science.gov (United States)

    Mellott, Dan O; Thisdelle, Jordan; Burke, Robert D

    2017-10-01

    We have examined regulation of neurogenesis by Delta/Notch signaling in sea urchin embryos. At gastrulation, neural progenitors enter S phase coincident with expression of Sp-SoxC. We used a BAC containing GFP knocked into the Sp-SoxC locus to label neural progenitors. Live imaging and immunolocalizations indicate that Sp-SoxC-expressing cells divide to produce pairs of adjacent cells expressing GFP. Over an interval of about 6 h, one cell fragments, undergoes apoptosis and expresses high levels of activated Caspase3. A Notch reporter indicates that Notch signaling is activated in cells adjacent to cells expressing Sp-SoxC. Inhibition of γ-secretase, injection of Sp-Delta morpholinos or CRISPR/Cas9-induced mutation of Sp-Delta results in supernumerary neural progenitors and neurons. Interfering with Notch signaling increases neural progenitor recruitment and pairs of neural progenitors. Thus, Notch signaling restricts the number of neural progenitors recruited and regulates the fate of progeny of the asymmetric division. We propose a model in which localized signaling converts ectodermal and ciliary band cells to neural progenitors that divide asymmetrically to produce a neural precursor and an apoptotic cell. © 2017. Published by The Company of Biologists Ltd.

  1. 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…

  2. Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

    Science.gov (United States)

    Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

    It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

  3. F-band millimeter-wave signal generation for wireless link data transmission using on-chip photonic integrated dual-wavelength sources

    NARCIS (Netherlands)

    Guzman, Robinson; Carpintero, G.; Gordon Gallegos, Carlos; Lawniczuk, Katarzyna; Leijtens, Xaveer

    2015-01-01

    Millimeter-waves (30-300 GHz) have interest due to the wide bandwidths available for carrying information, enabling broadband wireless communications. Photonics is a key technology for millimeter wave signal generation, recently demonstrating the use of photonic integration to reduce size and cost.

  4. Fiber Wireless Transmission of 8.3 Gb/s/ch QPSK-OFDM Signals in 75-110 GHz Band

    DEFF Research Database (Denmark)

    Deng, Lei; Beltrán Ramírez, Marta; Pang, Xiaodan

    2012-01-01

    In this paper, we present a scalable high speed Wband (75-110 GHz) fiber wireless communication system. By using an optical frequency comb generator, 3-channel 8.3 Gb/s/ch optical orthogonal frequency division multiplexing (OOFDM) baseband signals in a 15 GHz bandwidth are seamlessly translated f...

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

    Directory of Open Access Journals (Sweden)

    S. N. Kale

    2009-01-01

    Full Text Available 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 developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP and Radial Basis Function NN (RBF models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.

  6. Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine

    OpenAIRE

    Patri, A.; Nimaje, D. S.

    2015-01-01

    Wireless sensor networks and wireless communication systems have become indispensable in underground mines. Wireless sensor networks are being used for better real-time data acquisition from ground monitoring devices, gas sensors, and mining equipment, whereas wireless communication systems are needed for locating and communicating with workers. Conventional methods like wireline communication have proved to be ineffective in the event of mine hazards such as roof falls, fires etc. Before imp...

  7. EEG signal classification based on artificial neural networks and amplitude spectra features

    Science.gov (United States)

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  8. 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.

  9. Applications of autoassociative neural networks for signal validation in accident management

    International Nuclear Information System (INIS)

    Fantoni, P.; Mazzola, A.

    1994-01-01

    The OECD Halden Reactor Project has been working for several years with computer based systems for determination on plant status including early fault detection and signal validation. The method here presented explores the possibility to use a neural network approach to validate important process signals during normal and abnormal plant conditions. In BWR plants, signal validation has two important applications: reliable thermal limits calculation and reliable inputs to other computerized systems that support the operator during accident scenarious. This work shows how a properly trained autoassociative neural network can promptly detect faulty process signal measurements and produce a best estimate of the actual process value. Noise has been artificially added to the input to evaluate the network ability to respond in a very low signal to noise ratio environment. Training and test datasets have been simulated by the real time transient simulator code APROS. Future development addresses the validation of the model through the use of real data from the plant. (author). 5 refs, 17 figs

  10. 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

  11. Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks

    Science.gov (United States)

    Choudhury, Madhurima; Datta, Abhirup

    2018-05-01

    Observations of HI 21cm transition line is a promising probe into the Dark Ages and Epoch-of-Reionization. Detection of this redshifted 21cm signal is one of the key science goal for several upcoming low-frequency radio telescopes like HERA, SKA and DARE. Other global signal experiments include EDGES, LEDA, BIGHORNS, SCI-HI, SARAS. One of the major challenges for the detection of this signal is the accuracy of the foreground source removal. Several novel techniques have been explored already to remove bright foregrounds from both interferometric as well as total power experiments. Here, we present preliminary results from our investigation on application of ANN to detect 21cm global signal amidst bright galactic foreground. Following the formalism of representing the global 21cm signal by 'tanh' model, this study finds that the global 21cm signal parameters can be accurately determined even in the presence of bright foregrounds represented by 3rd order log-polynomial or higher.

  12. 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.

  13. Lunatic fringe-mediated Notch signaling regulates adult hippocampal neural stem cell maintenance.

    Science.gov (United States)

    Semerci, Fatih; Choi, William Tin-Shing; Bajic, Aleksandar; Thakkar, Aarohi; Encinas, Juan Manuel; Depreux, Frederic; Segil, Neil; Groves, Andrew K; Maletic-Savatic, Mirjana

    2017-07-12

    Hippocampal neural stem cells (NSCs) integrate inputs from multiple sources to balance quiescence and activation. Notch signaling plays a key role during this process. Here, we report that Lunatic fringe ( Lfng), a key modifier of the Notch receptor, is selectively expressed in NSCs. Further, Lfng in NSCs and Notch ligands Delta1 and Jagged1, expressed by their progeny, together influence NSC recruitment, cell cycle duration, and terminal fate. We propose a new model in which Lfng-mediated Notch signaling enables direct communication between a NSC and its descendants, so that progeny can send feedback signals to the 'mother' cell to modify its cell cycle status. Lfng-mediated Notch signaling appears to be a key factor governing NSC quiescence, division, and fate.

  14. Characterisation of eddy current signals using different types of artificial neural networks

    International Nuclear Information System (INIS)

    Shyamsunder, M.T.; Rajagopalan, C.; Jayakumar, T.; Kalyanasundaram, P.; Baldev Raj; Ray, K.K.

    1996-01-01

    Eddy current testing is one of the important techniques in nondestructive testing. Automated characterisation of eddy current signals (ECS), either in the form of lissajous patterns (figure-of-eight) or individual voltage vs. time signals is an area of growing interest. This is particularly relevant in environments where the signal-to-noise ratio (SNR) of ECS are very poor. Intelligent, timely and precise interpretation of resulting data, is the key for improving the efficiency of NDT and E. A comprehensive study has been undertaken by the authors for the characterisation of ECS having poor SNR, using three types of artificial neural networks (ANNs). The types of ANNs used in this study are [a] the error-back propagation model, [b] the binary Hopfield model and [c] the Kohonen's self-organising maps model. Eddy current signals, acquired from different types of defects such as holes and notches on stainless steel type 316 sheets were used in this study. (author)

  15. 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.

  16. Transcriptional response of Hoxb genes to retinoid signalling is regionally restricted along the neural tube rostrocaudal axis.

    Science.gov (United States)

    Carucci, Nicoletta; Cacci, Emanuele; Nisi, Paola S; Licursi, Valerio; Paul, Yu-Lee; Biagioni, Stefano; Negri, Rodolfo; Rugg-Gunn, Peter J; Lupo, Giuseppe

    2017-04-01

    During vertebrate neural development, positional information is largely specified by extracellular morphogens. Their distribution, however, is very dynamic due to the multiple roles played by the same signals in the developing and adult neural tissue. This suggests that neural progenitors are able to modify their competence to respond to morphogen signalling and autonomously maintain positional identities after their initial specification. In this work, we take advantage of in vitro culture systems of mouse neural stem/progenitor cells (NSPCs) to show that NSPCs isolated from rostral or caudal regions of the mouse neural tube are differentially responsive to retinoic acid (RA), a pivotal morphogen for the specification of posterior neural fates. Hoxb genes are among the best known RA direct targets in the neural tissue, yet we found that RA could promote their transcription only in caudal but not in rostral NSPCs. Correlating with these effects, key RA-responsive regulatory regions in the Hoxb cluster displayed opposite enrichment of activating or repressing histone marks in rostral and caudal NSPCs. Finally, RA was able to strengthen Hoxb chromatin activation in caudal NSPCs, but was ineffective on the repressed Hoxb chromatin of rostral NSPCs. These results suggest that the response of NSPCs to morphogen signalling across the rostrocaudal axis of the neural tube may be gated by the epigenetic configuration of target patterning genes, allowing long-term maintenance of intrinsic positional values in spite of continuously changing extrinsic signals.

  17. Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

    Science.gov (United States)

    Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M

    2011-11-01

    Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

  18. Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

    Science.gov (United States)

    Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

    2018-04-04

    The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

  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. Optical Switching for Dynamic Distribution of Wireless-over-Fiber Signals

    DEFF Research Database (Denmark)

    Rodes Lopez, Guillermo Arturo; Vegas Olmos, Juan José; Karinou, Fotini

    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; the rest of the network was designed according to the channel distribution over the optical spectra required by the optical...... switch. An experimental validation was also conducted. The experiment consists of a four wavelength division multiplexed (WDM) channel system operating on a WiMax frequency band, and employing an orthogonal frequency-division multiplexing (OFDM) modulation at 625 Mbit/s per channel, transmission...... of the data over 20 km of optical fiber, and active switching in a one-by-sixteen 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....

  2. 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.

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

    International Nuclear Information System (INIS)

    Li Xiumin; Small, Michael

    2010-01-01

    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.

  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. 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.

  6. Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

    Science.gov (United States)

    Johnson, J L

    1994-09-10

    The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

  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. 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...... the use of digital signal processing (DSP) technology to receive the generated UWB signal at 781.25 Mbit/s. Error-free transmission is achieved....

  9. Development of a wireless system for auditory neuroscience.

    Science.gov (United States)

    Lukes, A J; Lear, A T; Snider, R K

    2001-01-01

    In order to study how the auditory cortex extracts communication sounds in a realistic acoustic environment, a wireless system is being developed that will transmit acoustic as well as neural signals. The miniature transmitter will be capable of transmitting two acoustic signals with 37.5 KHz bandwidths (75 KHz sample rate) and 56 neural signals with bandwidths of 9.375 KHz (18.75 KHz sample rate). These signals will be time-division multiplexed into one high bandwidth signal with a 1.2 MHz sample rate. This high bandwidth signal will then be frequency modulated onto a 2.4 GHz carrier, which resides in the industrial, scientic, and medical (ISM) band that is designed for low-power short-range wireless applications. On the receiver side, the signal will be demodulated from the 2.4 GHz carrier and then digitized by an analog-to-digital (A/D) converter. The acoustic and neural signals will be digitally demultiplexed from the multiplexed signal into their respective channels. Oversampling (20 MHz) will allow the reconstruction of the multiplexing clock by a digital signal processor (DSP) that will perform frame and bit synchronization. A frame is a subset of the signal that contains all the channels and several channels tied high and low will signal the start of a frame. This technological development will bring two benefits to auditory neuroscience. It will allow simultaneous recording of many neurons that will permit studies of population codes. It will also allow neural functions to be determined in higher auditory areas by correlating neural and acoustic signals without apriori knowledge of the necessary stimuli.

  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. 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.

  12. 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.

  13. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  14. Neural Crest-Derived Mesenchymal Cells Require Wnt Signaling for Their Development and Drive Invagination of the Telencephalic Midline

    Science.gov (United States)

    Choe, Youngshik; Zarbalis, Konstantinos S.; Pleasure, Samuel J.

    2014-01-01

    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. PMID:24516524

  15. 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.

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

    Science.gov (United States)

    Vanni, Simo; Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

    2015-08-01

    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. Copyright © 2015 the American Physiological Society.

  17. 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.

  18. An Overview of a Class of Clock Synchronization Algorithms for Wireless Sensor Networks: A Statistical Signal Processing Perspective

    Directory of Open Access Journals (Sweden)

    Xu Wang

    2015-08-01

    Full Text Available Recently, wireless sensor networks (WSNs have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.

  19. 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.

  20. Genomic DISC1 Disruption in hiPSCs Alters Wnt Signaling and Neural Cell Fate

    Directory of Open Access Journals (Sweden)

    Priya Srikanth

    2015-09-01

    Full Text Available Genetic and clinical association studies have identified disrupted in schizophrenia 1 (DISC1 as a candidate risk gene for major mental illness. DISC1 is interrupted by a balanced chr(1;11 translocation in a Scottish family in which the translocation predisposes to psychiatric disorders. We investigate the consequences of DISC1 interruption in human neural cells using TALENs or CRISPR-Cas9 to target the DISC1 locus. We show that disruption of DISC1 near the site of the translocation results in decreased DISC1 protein levels because of nonsense-mediated decay of long splice variants. This results in an increased level of canonical Wnt signaling in neural progenitor cells and altered expression of fate markers such as Foxg1 and Tbr2. These gene expression changes are rescued by antagonizing Wnt signaling in a critical developmental window, supporting the hypothesis that DISC1-dependent suppression of basal Wnt signaling influences the distribution of cell types generated during cortical development.

  1. Radio Frequency Propagation Model and Fading of Wireless Signal at 2.4 GHz in Underground Coal Mine

    OpenAIRE

    Patri, Ashutosh; Nimaje, Devidas S.

    2015-01-01

    Deployment of wireless sensor networks and wireless communication systems have become indispensable for better real-time data acquisition from ground monitoring devices, gas sensors, and equipment used in underground mines as well as in locating the miners, since conventional methods like use of wireline communication are rendered ineffective in the event of mine hazards such as roof-falls, fire hazard etc. Before implementation of any wireless system, the variable path loss indices for diffe...

  2. Digital Tracking Array for FM Signals Based on Off-The-Shelf Wireless Technologies

    National Research Council Canada - National Science Library

    Edmund, Hui K

    2007-01-01

    ... a 2.4 GHz frequency modulation (FM) video signal from an unmanned air vehicle. The tracking is done using a monopulse technique Various numbers of elements were simulated to access the pattern coverage...

  3. Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

    International Nuclear Information System (INIS)

    Handayani, N; Akbar, Y; Khotimah, S N; Haryanto, F; Arif, I; Taruno, W P

    2016-01-01

    This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons. (paper)

  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. 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.

  6. Real-time classification of signals from three-component seismic sensors using neural nets

    Science.gov (United States)

    Bowman, B. C.; Dowla, F.

    1992-05-01

    Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

  7. Neural signal for counteracting pre-action bias in the centromedian thalamic nucleus

    Directory of Open Access Journals (Sweden)

    Takafumi eMinamimoto

    2014-01-01

    Full Text Available Most of our daily actions are selected and executed involuntarily under familiar situations by the guidance of internal drives, such as motivation. The behavioral tendency or biasing towards one over others reflects the action-selection process in advance of action execution (i.e., pre-action bias. Facing unexpected situations, however, pre-action bias should be withdrawn and replaced by an alternative that is suitable for the situation (i.e., counteracting bias. To understand the neural mechanism for the counteracting process, we studied the neural activity of the thalamic centromedian (CM nucleus in monkeys performing GO-NOGO task with asymmetrical or symmetrical reward conditions. The monkeys reacted to GO signal faster in large-reward condition, indicating behavioral bias toward large reward. In contrast, they responded slowly in small-reward condition, suggesting a conflict between internal drive and external demand. We found that neurons in the CM nucleus exhibited phasic burst discharges after GO and NOGO instructions especially when they were associated with small reward. The small-reward preference was positively correlated with the strength of behavioral bias toward large reward. The small-reward preference disappeared when only NOGO action was requested. The timing of activation predicted the timing of action opposed to bias. These results suggest that CM signals the discrepancy between internal pre-action bias and external demand, and mediates the counteracting process — resetting behavioral bias and leading to execution of opposing action.

  8. 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.

  9. 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.

  10. 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 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 the use of digi...

  11. 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

  12. Warming Up to Wireless

    Science.gov (United States)

    Milner, Jacob

    2005-01-01

    In districts big and small across the U.S., students, teachers, and administrators alike have come to appreciate the benefits of wireless technology. Because the technology delivers Internet signals on airborne radio frequencies, wireless networking allows users of all portable devices to move freely on a school's campus and stay connected to the…

  13. Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

    Science.gov (United States)

    Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

    2018-02-01

    Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

  14. miR-342-5p Regulates Neural Stem Cell Proliferation and Differentiation Downstream to Notch Signaling in Mice

    Directory of Open Access Journals (Sweden)

    Fang Gao

    2017-04-01

    Full Text Available Summary: Notch signaling is critically involved in neural development, but the downstream effectors remain incompletely understood. In this study, we cultured neurospheres from Nestin-Cre-mediated conditional Rbp-j knockout (Rbp-j cKO and control embryos and compared their miRNA expression profiles using microarray. Among differentially expressed miRNAs, miR-342-5p showed upregulated expression as Notch signaling was genetically or pharmaceutically interrupted. Consistently, the promoter of the miR-342-5p host gene, the Ena-vasodilator stimulated phosphoprotein-like (Evl, was negatively regulated by Notch signaling, probably through HES5. Transfection of miR-342-5p promoted the differentiation of neural stem cells (NSCs into intermediate neural progenitors (INPs in vitro and reduced the stemness of NSCs in vivo. Furthermore, miR-342-5p inhibited the differentiation of neural stem/intermediate progenitor cells into astrocytes, likely mediated by targeting GFAP directly. Our results indicated that miR-342-5p could function as a downstream effector of Notch signaling to regulate the differentiation of NSCs into INPs and astrocytes commitment. : In this article, Han and colleagues show that miR-342-5p acts as a downstream effector of Notch signaling in the mouse CNS. Notch signal inhibits miR-342-5p expression by regulating its host gene Evl. And with attenuated Notch signal in NSCs, miR-342-5p is upregulated to promote NSCs transition into INPs, and to inhibit astrocyte commitment by targeting GFAP. Keywords: neural stem cells, intermediate neural progenitors, Notch, RBP-J, neuron, glia, miR-342-5p

  15. 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.

  16. Enhanced signaling scheme with admission control in the hybrid optical wireless (HOW) networks

    DEFF Research Database (Denmark)

    Yan, Ying; Yu, Hao; Wessing, Henrik

    2009-01-01

    that it can support stringent Quality of Service (QoS) requirements. In this paper, we describe and evaluate a resource management framework designed for the HOW networks. There are two parts in the resource management framework The first part is the Enhanced MPCP (E-MPCP) scheme aiming at improving signaling...... dropping probability depend on several factors. These factors include the frame duration, the traffic load and the total number of shared users. The results also highlight that our proposed system achieves significant improvements over the traditional approach in terms of user QoS guarantee and network...

  17. Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

    Science.gov (United States)

    Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

    2018-05-01

    In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

  18. 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.

  19. Orphan nuclear receptor TLX activates Wnt/β-catenin signalling to stimulate neural stem cell proliferation and self-renewal

    Science.gov (United States)

    Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T.; Gage, Fred H.; Evans, Ronald M.; Shi, Yanhong

    2010-01-01

    The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/β-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/β-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active β-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a β-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active β-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active β-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active β-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/β-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode. PMID:20010817

  20. Orphan nuclear receptor TLX activates Wnt/beta-catenin signalling to stimulate neural stem cell proliferation and self-renewal.

    Science.gov (United States)

    Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T; Gage, Fred H; Evans, Ronald M; Shi, Yanhong

    2010-01-01

    The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/beta-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/beta-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active beta-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a beta-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active beta-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active beta-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active beta-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/beta-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode.

  1. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

    Science.gov (United States)

    Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

    2008-01-01

    Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

  2. Folic Acid supplementation stimulates notch signaling and cell proliferation in embryonic neural stem cells.

    Science.gov (United States)

    Liu, Huan; Huang, Guo-Wei; Zhang, Xu-Mei; Ren, Da-Lin; X Wilson, John

    2010-09-01

    The present study investigated the effect of folic acid supplementation on the Notch signaling pathway and cell proliferation in rat embryonic neural stem cells (NSCs). The NSCs were isolated from E14-16 rat brain and grown as neurospheres in serum-free suspension culture. Individual cultures were assigned to one of 3 treatment groups that differed according to the concentration of folic acid in the medium: Control (baseline folic acid concentration of 4 mg/l), low folic acid supplementation (4 mg/l above baseline, Folate-L) and high folic acid supplementation (40 mg/l above baseline, Folate-H). NSCs were identified by their expression of immunoreactive nestin and proliferating cells by incorporation of 5'bromo-2'deoxyuridine. Cell proliferation was also assessed by methyl thiazolyl tetrazolium assay. Notch signaling was analyzed by real-time PCR and western blot analyses of the expression of Notch1 and hairy and enhancer of split 5 (Hes5). Supplementation of NSCs with folic acid increased the mRNA and protein expression levels of Notch1 and Hes5. Folic acid supplementation also stimulated NSC proliferation dose-dependently. Embryonic NSCs respond to folic acid supplementation with increased Notch signaling and cell proliferation. This mechanism may mediate the effects of folic acid supplementation on neurogenesis in the embryonic nervous system.

  3. An artificial neural network model of energy expenditure using nonintegrated acceleration signals.

    Science.gov (United States)

    Rothney, Megan P; Neumann, Megan; Béziat, Ashley; Chen, Kong Y

    2007-10-01

    Accelerometers are a promising tool for characterizing physical activity patterns in free living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the oversimplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected biaxial raw (32 Hz) acceleration signals at the hip to develop a relationship between acceleration and minute-to-minute EE in 102 healthy adults using EE data collected for nearly 24 h in a room calorimeter as the reference standard. From each 1 min of acceleration data, we extracted 10 signal characteristics (features) that we felt had the potential to characterize EE intensity. Using these data, we developed a feed-forward/back-propagation artificial neural network (ANN) model with one hidden layer (12 x 20 x 1 nodes). Results of the ANN were compared with estimations using the ActiGraph monitor, a uniaxial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out) were completed, the ANN showed significantly reduced mean absolute errors (0.29 +/- 0.10 kcal/min), mean squared errors (0.23 +/- 0.14 kcal(2)/min(2)), and difference in total EE (21 +/- 115 kcal/day), compared with both the IDEEA (P types under free-living conditions.

  4. Orientation Encoding and Viewpoint Invariance in Face Recognition: Inferring Neural Properties from Large-Scale Signals.

    Science.gov (United States)

    Ramírez, Fernando M

    2018-05-01

    Viewpoint-invariant face recognition is thought to be subserved by a distributed network of occipitotemporal face-selective areas that, except for the human anterior temporal lobe, have been shown to also contain face-orientation information. This review begins by highlighting the importance of bilateral symmetry for viewpoint-invariant recognition and face-orientation perception. Then, monkey electrophysiological evidence is surveyed describing key tuning properties of face-selective neurons-including neurons bimodally tuned to mirror-symmetric face-views-followed by studies combining functional magnetic resonance imaging (fMRI) and multivariate pattern analyses to probe the representation of face-orientation and identity information in humans. Altogether, neuroimaging studies suggest that face-identity is gradually disentangled from face-orientation information along the ventral visual processing stream. The evidence seems to diverge, however, regarding the prevalent form of tuning of neural populations in human face-selective areas. In this context, caveats possibly leading to erroneous inferences regarding mirror-symmetric coding are exposed, including the need to distinguish angular from Euclidean distances when interpreting multivariate pattern analyses. On this basis, this review argues that evidence from the fusiform face area is best explained by a view-sensitive code reflecting head angular disparity, consistent with a role of this area in face-orientation perception. Finally, the importance is stressed of explicit models relating neural properties to large-scale signals.

  5. A role for chemokine signaling in neural crest cell migration and craniofacial development

    Science.gov (United States)

    Killian, Eugenia C. Olesnicky; Birkholz, Denise A.; Artinger, Kristin Bruk

    2009-01-01

    Neural crest cells (NCCs) are a unique population of multipotent cells that migrate along defined pathways throughout the embryo and give rise to many diverse cell types including pigment cells, craniofacial cartilage and the peripheral nervous system (PNS). Aberrant migration of NCCs results in a wide variety of congenital birth defects including craniofacial abnormalities. The chemokine Sdf1 and its receptors, Cxcr4 and Cxcr7, have been identified as key components in the regulation of cell migration in a variety of tissues. Here we describe a novel role for the zebrafish chemokine receptor Cxcr4a in the development and migration of cranial NCCs (CNCCs). We find that loss of Cxcr4a, but not Cxcr7b results in aberrant CNCC migration, defects in the neurocranium, as well as cranial ganglia dismorphogenesis. Moreover, overexpression of either Sdf1b or Cxcr4a causes aberrant CNCC migration and results in ectopic craniofacial cartilages. We propose a model in which Sdf1b signaling from the pharyngeal arch endoderm and optic stalk to Cxcr4a expressing CNCCs is important for both the proper condensation of the CNCCs into pharyngeal arches and the subsequent patterning and morphogenesis of the neural crest derived tissues. PMID:19576198

  6. 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.

  7. 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.

  8. 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...

  9. 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; Nyholm Jørgensen, Rasmus; 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...

  10. 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.

  11. 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.

  12. Mapping and signaling of neural pathways involved in the regulation of hydromineral homeostasis

    Directory of Open Access Journals (Sweden)

    J. Antunes-Rodrigues

    2013-04-01

    Full Text Available Several forebrain and brainstem neurochemical circuitries interact with peripheral neural and humoral signals to collaboratively maintain both the volume and osmolality of extracellular fluids. Although much progress has been made over the past decades in the understanding of complex mechanisms underlying neuroendocrine control of hydromineral homeostasis, several issues still remain to be clarified. The use of techniques such as molecular biology, neuronal tracing, electrophysiology, immunohistochemistry, and microinfusions has significantly improved our ability to identify neuronal phenotypes and their signals, including those related to neuron-glia interactions. Accordingly, neurons have been shown to produce and release a large number of chemical mediators (neurotransmitters, neurohormones and neuromodulators into the interstitial space, which include not only classic neurotransmitters, such as acetylcholine, amines (noradrenaline, serotonin and amino acids (glutamate, GABA, but also gaseous (nitric oxide, carbon monoxide and hydrogen sulfide and lipid-derived (endocannabinoids mediators. This efferent response, initiated within the neuronal environment, recruits several peripheral effectors, such as hormones (glucocorticoids, angiotensin II, estrogen, which in turn modulate central nervous system responsiveness to systemic challenges. Therefore, in this review, we shall evaluate in an integrated manner the physiological control of body fluid homeostasis from the molecular aspects to the systemic and integrated responses.

  13. 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.

  14. 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.

  15. Andrographolide Promotes Neural Differentiation of Rat Adipose Tissue-Derived Stromal Cells through Wnt/β-Catenin Signaling Pathway

    Directory of Open Access Journals (Sweden)

    Yan Liang

    2017-01-01

    Full Text Available Adipose tissue-derived stromal cells (ADSCs are a high-yield source of pluripotent stem cells for use in cell-based therapies. We explored the effect of andrographolide (ANDRO, one of the ingredients of the medicinal herb extract on the neural differentiation of rat ADSCs and associated molecular mechanisms. We observed that rat ADSCs were small and spindle-shaped and expressed multiple stem cell markers including nestin. They were multipotent as evidenced by adipogenic, osteogenic, chondrogenic, and neural differentiation under appropriate conditions. The proportion of cells exhibiting neural-like morphology was higher, and neurites developed faster in the ANDRO group than in the control group in the same neural differentiation medium. Expression levels of the neural lineage markers MAP2, tau, GFAP, and β-tubulin III were higher in the ANDRO group. ANDRO induced a concentration-dependent increase in Wnt/β-catenin signaling as evidenced by the enhanced expression of nuclear β-catenin and the inhibited form of GSK-3β (pSer9. Thus, this study shows for the first time how by enhancing the neural differentiation of ADSCs we expect that ANDRO pretreatment may increase the efficacy of adult stem cell transplantation in nervous system diseases, but more exploration is needed.

  16. 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...... latest findings and experimental results on the W-band, specifically on its 81–86GHz sub-band. These include photonic generation of millimeter-wave carriers and transmission performance of broadband signals on different types of fibers and span lengths....

  17. REVIEW OF WIRELESS MIMO CHANNEL MODELS

    African Journals Online (AJOL)

    user

    MIMO wireless system, the transmitted signal interacts ... delay spread information, power delay profile, angle of arrival and ... With the advent of the MIMO wireless systems, there arose a ..... associated with channel transmission and reception.

  18. Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

    International Nuclear Information System (INIS)

    Kim, Kyungmin; Lee, Hyun Kyu; Harry, Ian W; Hodge, Kari A; Kim, Young-Min; Lee, Chang-Hwan; Oh, John J; Oh, Sang Hoon; Son, Edwin J

    2015-01-01

    We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%–14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs. (paper)

  19. 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

    2018-01-15

    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 110:63-71, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  20. 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.

  1. Kin Rejection: Social Signals, Neural Response and Perceived Distress During Social Exclusion

    Science.gov (United States)

    Sreekrishnan, Anirudh; Herrera, Tania A.; Wu, Jia; Borelli, Jessica L.; White, Lars O.; Rutherford, Helena J. V.; Mayes, Linda C.; Crowley, Michael J.

    2014-01-01

    Across species, kin bond together to promote survival. We sought to understand the dyadic effect of exclusion by kin (as opposed to non-kin strangers) on brain activity of the mother and her child and their subjective distress. To this end, we probed mother-child relationships with a computerized ball-toss game Cyberball. When excluded by one another, rather than by a stranger, both mothers and children exhibited a significantly pronounced frontal P2. Moreover, upon kin-rejection versus stranger-rejection, both mothers and children showed incremented left frontal positive slow waves for rejection events. Children reported more distress upon exclusion than their own mothers. Similar to past work, relatively augmented negative frontal slow wave activity predicted greater self-reported ostracism distress. This effect, generalized to the P2, was limited to mother or child- rejection by kin, with comparable magnitude of effect across kin identity (mothers vs. children). For both mothers and children, the frontal P2 peak was significantly pronounced for kin-rejection versus stranger rejection. Taken together, our results document the rapid categorization of social signals as kin-relevant and the specificity of early and late neural markers for predicting felt ostracism. PMID:24909389

  2. 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.

  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. 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.

  5. 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.

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

    International Nuclear Information System (INIS)

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

    2006-01-01

    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

  7. Ectopic cross-talk between thyroid and retinoic acid signaling: A possible etiology for spinal neural tube defects.

    Science.gov (United States)

    Li, Huili; Bai, Baoling; Zhang, Qin; Bao, Yihua; Guo, Jin; Chen, Shuyuan; Miao, Chunyue; Liu, Xiaozhen; Zhang, Ting

    2015-12-01

    Previous studies have highlighted the connections between neural tube defects (NTDs) and both thyroid hormones (TH) and vitamin A. However, whether the two hormonal signaling pathways interact in NTDs has remained unclear. We measured the expression levels of TH signaling genes in human fetuses with spinal NTDs associated with maternal hyperthyroidism as well as levels of retinoic acid (RA) signaling genes in mouse fetuses exposed to an overdose of RA using NanoString or real-time PCR on spinal cord tissues. Interactions between the two signaling pathways were detected by ChIP assays. The data revealed attenuated DIO2/DIO3 switching in fetuses with NTDs born to hyperthyroid mothers. The promoters of the RA signaling genes CRABP1 and RARB were ectopically occupied by increased RXRG and RXRB but displayed decreased levels of inhibitory histone modifications, suggesting that elevated TH signaling abnormally stimulates RA signaling genes. Conversely, in the mouse model, the observed decrease in Dio3 expression could be explained by increased levels of inhibitory histone modifications in the Dio3 promoter region, suggesting that overactive RA signaling may ectopically derepress TH signaling. This study thus raises in vivo a possible abnormal cross-promotion between two different hormonal signals through their common RXRs and the subsequent recruitment of histone modifications, prompting further investigation into their involvement in the etiology of spinal NTDs. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. 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.

  9. 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

  10. Development of a signal-analysis algorithm for the ZEUS transition-radiation detector under application of a neural network

    International Nuclear Information System (INIS)

    Wollschlaeger, U.

    1992-07-01

    The aim of this thesis consisted in the development of a procedure for the analysis of the data of the transition-radiation detector at ZEUS. For this a neural network was applied and first studied, which results concerning the separation power between electron an pions can be reached by this procedure. It was shown that neural nets yield within the error limits as well results as standard algorithms (total charge, cluster analysis). At an electron efficiency of 90% pion contaminations in the range 1%-2% were reached. Furthermore it could be confirmed that neural networks can be considered for the here present application field as robust in relatively insensitive against external perturbations. For the application in the experiment beside the separation power also the time-behaviour is of importance. The requirement to keep dead-times small didn't allow the application of standard method. By a simulation the time availabel for the signal analysis was estimated. For the testing of the processing time in a neural network subsequently the corresponding algorithm was implemented into an assembler code for the digital signal processor DSP56001. (orig./HSI) [de

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

    International Nuclear Information System (INIS)

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

    1998-02-01

    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

  12. Space-division-multiplexed transmission of 3x3 multiple-input multiple-output wireless signals over conventional graded-index multimode fiber.

    Science.gov (United States)

    Lei, Yi; Li, Jianqiang; Fan, Yuting; Yu, Dawei; Fu, Songnian; Yin, Feifei; Dai, Yitang; Xu, Kun

    2016-12-12

    In this paper, we experimentally demonstrate space-division-multiplexed (SDM) transmission of IEEE 802.11ac-compliant 3-spatial-stream WLAN signals over 3 spatial modes of conventional 50um graded-index (GI) multimode fiber (MMF) employing non-mode-selective 3D-waveguide photonic lantern. Two kinds of scenarios, including fiber-only transmission and fiber-wireless hybrid transmission, were investigated by measuring error vector magnitude (EVM) performance for each stream and condition number (CN) of the channel matrix. The experimental results show that, SDM-based MMF link could offer a CNwireless MIMO signals over existing in-building commercially-available MMFs with enormous cost-saving.

  13. 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 R.

    2018-04-01

    Objective. 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 Cramér-Rao Bound on evoked calcium traces recorded

  14. Beta1 integrins activate a MAPK signalling pathway in neural stem cells that contributes to their maintenance

    DEFF Research Database (Denmark)

    Campos, Lia S; Leone, Dino P; Relvas, Joao B

    2004-01-01

    , signalling is required for neural stem cell maintenance, as assessed by neurosphere formation, and inhibition or genetic ablation of beta1 integrin using cre/lox technology reduces the level of MAPK activity. We conclude that integrins are therefore an important part of the signalling mechanisms that control......The emerging evidence that stem cells develop in specialised niches highlights the potential role of environmental factors in their regulation. Here we examine the role of beta1 integrin/extracellular matrix interactions in neural stem cells. We find high levels of beta1 integrin expression...... in the stem-cell containing regions of the embryonic CNS, with associated expression of the laminin alpha2 chain. Expression levels of laminin alpha2 are reduced in the postnatal CNS, but a population of cells expressing high levels of beta1 remains. Using neurospheres - aggregate cultures, derived from...

  15. Electroacupuncture in the repair of spinal cord injury: inhibiting the Notch signaling pathway and promoting neural stem cell proliferation

    Directory of Open Access Journals (Sweden)

    Xin Geng

    2015-01-01

    Full Text Available Electroacupuncture for the treatment of spinal cord injury has a good clinical curative effect, but the underlying mechanism is unclear. In our experiments, the spinal cord of adult Sprague-Dawley rats was clamped for 60 seconds. Dazhui (GV14 and Mingmen (GV4 acupoints of rats were subjected to electroacupuncture. Enzyme-linked immunosorbent assay revealed that the expression of serum inflammatory factors was apparently downregulated in rat models of spinal cord injury after electroacupuncture. Hematoxylin-eosin staining and immunohistochemistry results demonstrated that electroacupuncture contributed to the proliferation of neural stem cells in rat injured spinal cord, and suppressed their differentiation into astrocytes. Real-time quantitative PCR and western blot assays showed that electroacupuncture inhibited activation of the Notch signaling pathway induced by spinal cord injury. These findings indicate that electroacupuncture repaired the injured spinal cord by suppressing the Notch signaling pathway and promoting the proliferation of endogenous neural stem cells.

  16. Activin/Nodal Signaling Supports Retinal Progenitor Specification in a Narrow Time Window during Pluripotent Stem Cell Neuralization

    Directory of Open Access Journals (Sweden)

    Michele Bertacchi

    2015-10-01

    Full Text Available Retinal progenitors are initially found in the anterior neural plate region known as the eye field, whereas neighboring areas undertake telencephalic or hypothalamic development. Eye field cells become specified by switching on a network of eye field transcription factors, but the extracellular cues activating this network remain unclear. In this study, we used chemically defined media to induce in vitro differentiation of mouse embryonic stem cells (ESCs toward eye field fates. Inhibition of Wnt/β-catenin signaling was sufficient to drive ESCs to telencephalic, but not retinal, fates. Instead, retinal progenitors could be generated from competent differentiating mouse ESCs by activation of Activin/Nodal signaling within a narrow temporal window corresponding to the emergence of primitive anterior neural progenitors. Activin also promoted eye field gene expression in differentiating human ESCs. Our results reveal insights into the mechanisms of eye field specification and open new avenues toward the generation of retinal progenitors for translational medicine.

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

    Directory of Open Access Journals (Sweden)

    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

  18. Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

    Science.gov (United States)

    Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

    2008-09-01

    In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

  19. Towards Perpetual Energy Operation in Wireless Communication Systems

    KAUST Repository

    Benkhelifa, Fatma

    2017-01-01

    energy operation of wireless communication systems, energy harvesting (EH) from the radio frequency (RF) signals is one promising solution to make the wireless communication systems self-sustaining. Since RF signals are known to transmit information

  20. 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

  1. 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

  2. Neural induction in Xenopus: requirement for ectodermal and endomesodermal signals via Chordin, Noggin, beta-Catenin, and Cerberus.

    Directory of Open Access Journals (Sweden)

    Hiroki Kuroda

    2004-05-01

    Full Text Available The origin of the signals that induce the differentiation of the central nervous system (CNS is a long-standing question in vertebrate embryology. Here we show that Xenopus neural induction starts earlier than previously thought, at the blastula stage, and requires the combined activity of two distinct signaling centers. One is the well-known Nieuwkoop center, located in dorsal-vegetal cells, which expresses Nodal-related endomesodermal inducers. The other is a blastula Chordin- and Noggin-expressing (BCNE center located in dorsal animal cells that contains both prospective neuroectoderm and Spemann organizer precursor cells. Both centers are downstream of the early beta-Catenin signal. Molecular analyses demonstrated that the BCNE center was distinct from the Nieuwkoop center, and that the Nieuwkoop center expressed the secreted protein Cerberus (Cer. We found that explanted blastula dorsal animal cap cells that have not yet contacted a mesodermal substratum can, when cultured in saline solution, express definitive neural markers and differentiate histologically into CNS tissue. Transplantation experiments showed that the BCNE region was required for brain formation, even though it lacked CNS-inducing activity when transplanted ventrally. Cell-lineage studies demonstrated that BCNE cells give rise to a large part of the brain and retina and, in more posterior regions of the embryo, to floor plate and notochord. Loss-of-function experiments with antisense morpholino oligos (MO showed that the CNS that forms in mesoderm-less Xenopus embryos (generated by injection with Cerberus-Short [CerS] mRNA required Chordin (Chd, Noggin (Nog, and their upstream regulator beta-Catenin. When mesoderm involution was prevented in dorsal marginal-zone explants, the anterior neural tissue formed in ectoderm was derived from BCNE cells and had a complete requirement for Chd. By injecting Chd morpholino oligos (Chd-MO into prospective neuroectoderm and Cerberus

  3. Phenothiourea sensitizes zebrafish cranial neural crest and extraocular muscle development to changes in retinoic acid and IGF signaling.

    Directory of Open Access Journals (Sweden)

    Brenda L Bohnsack

    Full Text Available 1-Phenyl 2-thiourea (PTU is a tyrosinase inhibitor commonly used to block pigmentation and aid visualization of zebrafish development. At the standard concentration of 0.003% (200 µM, PTU inhibits melanogenesis and reportedly has minimal other effects on zebrafish embryogenesis. We found that 0.003% PTU altered retinoic acid and insulin-like growth factor (IGF regulation of neural crest and mesodermal components of craniofacial development. Reduction of retinoic acid synthesis by the pan-aldehyde dehydrogenase inhibitor diethylbenzaldehyde, only when combined with 0.003% PTU, resulted in extraocular muscle disorganization. PTU also decreased retinoic acid-induced teratogenic effects on pharyngeal arch and jaw cartilage despite morphologically normal appearing PTU-treated controls. Furthermore, 0.003% PTU in combination with inhibition of IGF signaling through either morpholino knockdown or pharmacologic inhibition of tyrosine kinase receptor phosphorylation, disrupted jaw development and extraocular muscle organization. PTU in and of itself inhibited neural crest development at higher concentrations (0.03% and had the greatest inhibitory effect when added prior to 22 hours post fertilization (hpf. Addition of 0.003% PTU between 4 and 20 hpf decreased thyroxine (T4 in thyroid follicles in the nasopharynx of 96 hpf embryos. Treatment with exogenous triiodothyronine (T3 and T4 improved, but did not completely rescue, PTU-induced neural crest defects. Thus, PTU should be used with caution when studying zebrafish embryogenesis as it alters the threshold of different signaling pathways important during craniofacial development. The effects of PTU on neural crest development are partially caused by thyroid hormone signaling.

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

    Directory of Open Access Journals (Sweden)

    Kalcheim Chaya

    2008-10-01

    Full Text Available Abstract 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.

  5. 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.

  6. 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.

  7. An evaluation of neural networks for identification of system parameters in reactor noise signals

    International Nuclear Information System (INIS)

    Miller, L.F.

    1991-01-01

    Several backpropagation neural networks for identifying fundamental mode eigenvalues were evaluated. The networks were trained and tested on analytical data and on results from other numerical methods. They were then used to predict first mode break frequencies for noise data from several sources. These predictions were, in turn, compared with analytical values and with results from alternative methods. Comparisons of results for some data sets suggest that the accuracy of predictions from neural networks are essentially equivalent to results from conventional methods while other evaluations indicate that either method may be superior. Experience gained from these numerical experiments provide insight for improving the performance of neural networks relative to other methods for identifying parameters associated with experimental data. Neural networks may also be used in support of conventional algorithms by providing starting points for nonlinear minimization algorithms

  8. 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.

  9. Dual small-molecule targeting of SMAD signaling stimulates human induced pluripotent stem cells toward neural lineages.

    Directory of Open Access Journals (Sweden)

    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.

  10. CONCEPTION OF USE VIBROACOUSTIC SIGNALS AND NEURAL NETWORKS FOR DIAGNOSING OF CHOSEN ELEMENTS OF INTERNAL COMBUSTION ENGINES IN CAR VEHICLES

    Directory of Open Access Journals (Sweden)

    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.

  11. OPTICAL WIRELESS COMMUNICATION SYSTEM

    Directory of Open Access Journals (Sweden)

    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.

  12. Wireless Power Transfer Strategies for Implantable Bioelectronics.

    Science.gov (United States)

    Agarwal, Kush; Jegadeesan, Rangarajan; Guo, Yong-Xin; Thakor, Nitish V

    2017-01-01

    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.

  13. Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Yan, W.

    1993-11-01

    The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods

  14. 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

  15. Electronic bypass of spinal lesions: activation of lower motor neurons directly driven by cortical neural signals.

    Science.gov (United States)

    Li, Yan; Alam, Monzurul; Guo, Shanshan; Ting, K H; He, Jufang

    2014-07-03

    Lower motor neurons in the spinal cord lose supraspinal inputs after complete spinal cord injury, leading to a loss of volitional control below the injury site. Extensive locomotor training with spinal cord stimulation can restore locomotion function after spinal cord injury in humans and animals. However, this locomotion is non-voluntary, meaning that subjects cannot control stimulation via their natural "intent". A recent study demonstrated an advanced system that triggers a stimulator using forelimb stepping electromyographic patterns to restore quadrupedal walking in rats with spinal cord transection. However, this indirect source of "intent" may mean that other non-stepping forelimb activities may false-trigger the spinal stimulator and thus produce unwanted hindlimb movements. We hypothesized that there are distinguishable neural activities in the primary motor cortex during treadmill walking, even after low-thoracic spinal transection in adult guinea pigs. We developed an electronic spinal bridge, called "Motolink", which detects these neural patterns and triggers a "spinal" stimulator for hindlimb movement. This hardware can be head-mounted or carried in a backpack. Neural data were processed in real-time and transmitted to a computer for analysis by an embedded processor. Off-line neural spike analysis was conducted to calculate and preset the spike threshold for "Motolink" hardware. We identified correlated activities of primary motor cortex neurons during treadmill walking of guinea pigs with spinal cord transection. These neural activities were used to predict the kinematic states of the animals. The appropriate selection of spike threshold value enabled the "Motolink" system to detect the neural "intent" of walking, which triggered electrical stimulation of the spinal cord and induced stepping-like hindlimb movements. We present a direct cortical "intent"-driven electronic spinal bridge to restore hindlimb locomotion after complete spinal cord injury.

  16. 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...

  17. Tcf3 represses Wnt-β-catenin signaling and maintains neural stem cell population during neocortical development.

    Directory of Open Access Journals (Sweden)

    Atsushi Kuwahara

    Full Text Available During mouse neocortical development, the Wnt-β-catenin signaling pathway plays essential roles in various phenomena including neuronal differentiation and proliferation of neural precursor cells (NPCs. Production of the appropriate number of neurons without depletion of the NPC population requires precise regulation of the balance between differentiation and maintenance of NPCs. However, the mechanism that suppresses Wnt signaling to prevent premature neuronal differentiation of NPCs is poorly understood. We now show that the HMG box transcription factor Tcf3 (also known as Tcf7l1 contributes to this mechanism. Tcf3 is highly expressed in undifferentiated NPCs in the mouse neocortex, and its expression is reduced in intermediate neuronal progenitors (INPs committed to the neuronal fate. We found Tcf3 to be a repressor of Wnt signaling in neocortical NPCs in a reporter gene assay. Tcf3 bound to the promoter of the proneural bHLH gene Neurogenin1 (Neurog1 and repressed its expression. Consistent with this, Tcf3 repressed neuronal differentiation and increased the self-renewal activity of NPCs. We also found that Wnt signal stimulation reduces the level of Tcf3, and increases those of Tcf1 (also known as Tcf7 and Lef1, positive mediators of Wnt signaling, in NPCs. Together, these results suggest that Tcf3 antagonizes Wnt signaling in NPCs, thereby maintaining their undifferentiated state in the neocortex and that Wnt signaling promotes the transition from Tcf3-mediated repression to Tcf1/Lef1-mediated enhancement of Wnt signaling, constituting a positive feedback loop that facilitates neuronal differentiation.

  18. 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.

  19. Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals

    NARCIS (Netherlands)

    Fonseca, Andre; Boboeva, Vezha; Brederoo, Sanne; Baggio, Giosue

    2015-01-01

    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,

  20. Dissecting Repulsive Guidance Molecule/Neogenin function and signaling during neural development

    NARCIS (Netherlands)

    van den Heuvel, D.M.A.

    2013-01-01

    During neural development a series of precisely ordered cellular processes acts to establish a functional brain comprising millions of neurons and many more neuronal connections. Neogenin and its repulsive guidance molecule (RGM) ligands contribute to neuronal network formation by inducing axon

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

    International Nuclear Information System (INIS)

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

    2001-01-01

    A feed-forward neural network is used to forecast major and minor disruptions in TEXT tokamak discharges. Using the experimental data of soft X-ray signals as input data, the neural net is trained with one disruptive plasma discharge, while a different disruptive discharge is used for validation. After proper training, the net works with the same set of weights, it is then used to forecast disruptions in two other different plasma discharges. It is observed that the neural net is capable of predicting the onset of a disruption up to 3.12 ms in advance. From what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism is associated with an increase in the m=2 magnetic island, that disturbs the central part of the plasma column afterwards, or the initial perturbation has first occurred in the central part of the plasma column and then the m=2 MHD mode is destabilized. (author)

  2. 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.

  3. 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.

  4. 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

  5. Miniaturized wireless sensor network

    OpenAIRE

    Lecointre , Aubin; Dragomirescu , Daniela; Dubuc , David; Grenier , Katia; Pons , Patrick; Aubert , Hervé; Müller , A.; Berthou , Pascal; Gayraud , Thierry; Plana , Robert

    2006-01-01

    This paper addresses an overview of the wireless sensor networks. It is shown that MEMS/NEMS technologies and SIP concept are well suited for advanced architectures. It is also shown analog architectures have to be compatible with digital signal techniques to develop smart network of microsystem.

  6. Wireless networks; Traadloese nettverk

    Energy Technology Data Exchange (ETDEWEB)

    2008-07-01

    Wireless Local Area Networks - WLAN, is being installed in homes, offices, schools and city areas with an increasing speed. Computers communicate with each other through networks by using radio signals. Base stations make sure there is sufficient radio coverage in the current areas. The effects on human and if it is dangerous is discussed

  7. Expression of p53/HGF/c-met/STAT3 signal in fetuses with neural tube defects.

    Science.gov (United States)

    Trovato, Maria; D'Armiento, Maria; Lavra, Luca; Ulivieri, Alessandra; Dominici, Roberto; Vitarelli, Enrica; Grosso, Maddalena; Vecchione, Raffaella; Barresi, Gaetano; Sciacchitano, Salvatore

    2007-02-01

    Neural tube defects (NTD) are morphogenetic alterations due to a defective closure of neural tube. Hepatocyte growth factor (HGF)/c-met system plays a role in morphogenesis of nervous system, lung, and kidney. HGF/c-met morphogenetic effects are mediated by signal transducers and activators of transcription (STAT)3 and both HGF and c-met genes are regulated from p53. The aim of our study was to analyze mRNA and protein expressions of p53, HGF, c-met, and STAT3 in fetuses with NTD. By reverse transcriptase-polymerase chain reaction and immunohistochemistry, we analyzed neural tissues from four NTD fetuses and the corresponding non-malformed lungs, kidneys and placentas. We found a reduced mRNA expression of HGF/c-met/STAT3 pathway, in the malformed nervous systems and placentas. The reduced expression of this pathway correlated with the absence of p53 in all these samples. On the contrary, detectable expression levels of p53, HGF, c-met, and STAT3 were observed in non-malformed lungs and kidneys obtained from the same fetuses. Comparable results were obtained by immunohistochemistry, with the exception of p53, which was undetected in all fetal tissues. In conclusion, in NTD fetuses, both the defective neural tube tissue and the placenta have a reduction in all components of the p53/HGF/c-met/STAT3 cascade. This raises the possibility of using the suppression of these genes for early diagnosis of NTD especially on chorionic villus sampling.

  8. 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.

  9. 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

  10. Overexpression of miR‑21 promotes neural stem cell proliferation and neural differentiation via the Wnt/β‑catenin signaling pathway in vitro.

    Science.gov (United States)

    Zhang, Wei-Min; Zhang, Zhi-Ren; Yang, Xi-Tao; Zhang, Yong-Gang; Gao, Yan-Sheng

    2018-01-01

    The primary aim of the present study was to examine the effects of microRNA‑21 (miR‑21) on the proliferation and differentiation of rat primary neural stem cells (NSCs) in vitro. miR‑21 was overexpressed in NSCs by transfection with a miR‑21 mimic. The effects of miR‑21 overexpression on NSC proliferation were revealed by Cell Counting kit 8 and 5‑ethynyl‑2'‑deoxyuridine incorporation assay, and miR‑21 overexpression was revealed to increase NSC proliferation. miR‑21 overexpression was confirmed using reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). mRNA and protein expression levels of key molecules (β‑catenin, cyclin D1, p21 and miR‑21) in the Wnt/β‑catenin signaling pathway were studied by RT‑qPCR and western blot analysis. RT‑qPCR and western blot analyses revealed that miR‑21 overexpression increased β‑catenin and cyclin D1 expression, and decreased p21 expression. These results suggested that miR‑21‑induced increase in proliferation was mediated by activation of the Wnt/β‑catenin signaling pathway, since overexpression of miR‑21 increased β‑catenin and cyclin D1 expression and reduced p21 expression. Furthermore, inhibition of the Wnt/β‑catenin pathway with FH535 attenuated the influence of miR‑21 overexpression on NSC proliferation, indicating that the factors activated by miR‑21 overexpression were inhibited by FH535 treatment. Furthermore, overexpression of miR‑21 enhanced the differentiation of NSCs into neurons and inhibited their differentiation into astrocytes. The present study indicated that in primary rat NSCs, overexpression of miR‑21 may promote proliferation and differentiation into neurons via the Wnt/β‑catenin signaling pathway in vitro.

  11. 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.

  12. Neural network for recognizing signal-shape of nuclear detector output

    International Nuclear Information System (INIS)

    Mardiyanto M Panitra

    2006-01-01

    The use of artificial intelligent technique in the engineering field has been familiar especially in the field of pattern recognition. By using this technique, either simple routine works or complicated routine works can be done by the help of a digital camera and a personal computer. One of the complicated works that can not be solved easily is how to separate two kinds of nuclear radiation types which are mixed in the same field. The separation of the two kinds of radiation become is very important for the radiation dosimetry purposes. For doing this we have carried out a preliminary research in applying a neural network technique for recognizing C and T letters with right, left, up, and down positions. We arranged a three-layer neural network i.e. input layer (9 neurons with/without bias neuron), hidden layer (11 neurons), and output layer (1 neuron). From this preliminary study the use of a bias neuron gave faster learning process compared with the one without the bias neuron. The neural network could work successfully in determining the letter S and T without any mistake. (author)

  13. Augmented Indian hedgehog signaling in cranial neural crest cells leads to craniofacial abnormalities and dysplastic temporomandibular joint in mice.

    Science.gov (United States)

    Yang, Ling; Gu, Shuping; Ye, Wenduo; Song, Yingnan; Chen, YiPing

    2016-04-01

    Extensive studies have pinpointed the crucial role of Indian hedgehog (Ihh) signaling in the development of the appendicular skeleton and the essential function of Ihh in the formation of the temporomandibular joint (TMJ). In this study, we have investigated the effect of augmented Ihh signaling in TMJ development. We took a transgenic gain-of-function approach by overexpressing Ihh in the cranial neural crest (CNC) cells using a conditional Ihh transgenic allele and the Wnt1-Cre allele. We found that Wnt1-Cre-mediated tissue-specific overexpression of Ihh in the CNC lineage caused severe craniofacial abnormalities, including cleft lip/palate, encephalocele, anophthalmos, micrognathia, and defective TMJ development. In the mutant TMJ, the glenoid fossa was completely absent, whereas the condyle and the articular disc appeared relatively normal with slightly delayed chondrocyte differentiation. Our findings thus demonstrate that augmented Ihh signaling is detrimental to craniofacial development, and that finely tuned Ihh signaling is critical for TMJ formation. Our results also provide additional evidence that the development of the condyle and articular disc is independent of the glenoid fossa.

  14. 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

  15. Curcumin increased the differentiation rate of neurons in neural stem cells via wnt signaling in vitro study.

    Science.gov (United States)

    Chen, Fei; Wang, Haoxiang; Xiang, Xin; Yuan, Jichao; Chu, Weihua; Xue, Xingsen; Zhu, Haitao; Ge, Hongfei; Zou, Mingming; Feng, Hua; Lin, Jiangkai

    2014-12-01

    The objective of the present study was to clarify the relationship between the neuroprotective effects of curcumin and the classical wnt signaling pathway. Using Sprague-Dawley rats at a gestational age of 14.5 d, we isolated neural stem cells from the anterior two-thirds of the fetal rat brain. The neural stem cells were passaged three times using the half media replacement method and identified using cellular immunofluorescence. After passaging for three generations, we cultured cells in media without basic fibroblast growth factor and epidermal growth factor. Then we treated cells in five different ways, including a blank control group, a group treated with IWR1 (10 μmol/L), a group treated with curcumin (500 nmol/L), a group treated with IWR1 + curcumin, and a group treated with dimethyl sulfoxide (10 μmol/L). We then measured the protein and RNA expression levels for wnt3a and β-catenin using Western blotting and Reverse transcription-polymerase chain reaction (RT-PCR). Western-blotting: after the third generation of cells had been treated for 72 h, we observed that wnt3a and β-catenin expression was significantly increased in the group receiving 500 nmol/L curcumin but not in the other groups. Furthermore, cells in the IWR1-treated group showed decreased wnt3a and β-catenin expression, and wnt3a and β-catenin was also decreased in the IWR1 + 500 nmol/L curcumin group. No obvious change was observed in the dimethyl sulfoxide group. RT-PCR showed similar changes to those observed with the Western blotting experiments. Our study suggests that curcumin can activate the wnt signaling pathway, which provides evidence that curcumin exhibits a neuroprotective effect through the classical wnt signaling pathway. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. A passive wireless ultrasound pitch–catch system

    International Nuclear Information System (INIS)

    Zahedi, F; Yao, J; Huang, H

    2015-01-01

    This paper exploits amplitude modulation and demodulation to achieve a passive wireless ultrasound pitch–catch system consisting of a wireless interrogator and a combination of a wireless actuator and a sensor mounted on a structure. The wireless interrogator operates in two modes, i.e. the generation and sensing modes. At the generation mode, the interrogator transmits two microwave signals; one is amplitude modulated with the ultrasound excitation signal while the other is a continuous-wave carrier signal. Once received by the wireless actuator, the amplitude modulated signal is demodulated using the carrier signal to recover the ultrasound excitation signal, which is then supplied to a piezoelectric wafer actuator for ultrasound generation. Subsequently, the interrogator is switched to the sensing mode by transmitting a carrier signal with a different frequency. Once received by the wireless sensor, this carrier signal is modulated with the ultrasound sensing signal acquired by the piezoelectric wafer sensor to produce an amplitude modulated microwave signal, which can then be wirelessly transmitted and demodulated by the interrogator to recover the original ultrasound sensing signal. The principle and implementation of the wireless ultrasound pitch–catch system as well as the data processing of the wirelessly received sensing signal are described. Experiment results validating wireless ultrasound generation and sensing from a distance of 0.5 m are presented. (paper)

  17. 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

  18. Ras-dva1 small GTPase regulates telencephalon development in Xenopus laevis embryos by controlling Fgf8 and Agr signaling at the anterior border of the neural plate

    Directory of Open Access Journals (Sweden)

    Maria B. Tereshina

    2014-07-01

    Full Text Available We previously found that the small GTPase Ras-dva1 is essential for the telencephalic development in Xenopus laevis because Ras-dva1 controls the Fgf8-mediated induction of FoxG1 expression, a key telencephalic regulator. In this report, we show, however, that Ras-dva1 and FoxG1 are expressed in different groups of cells; whereas Ras-dva1 is expressed in the outer layer of the anterior neural fold, FoxG1 and Fgf8 are activated in the inner layer from which the telencephalon is derived. We resolve this paradox by demonstrating that Ras-dva1 is involved in the transduction of Fgf8 signal received by cells in the outer layer, which in turn send a feedback signal that stimulates FoxG1 expression in the inner layer. We show that this feedback signal is transmitted by secreted Agr proteins, the expression of which is activated in the outer layer by mediation of Ras-dva1 and the homeodomain transcription factor Otx2. In turn, Agrs are essential for maintaining Fgf8 and FoxG1 expression in cells at the anterior neural plate border. Our finding reveals a novel feedback loop mechanism based on the exchange of Fgf8 and Agr signaling between neural and non-neural compartments at the anterior margin of the neural plate and demonstrates a key role of Ras-dva1 in this mechanism.

  19. Differential signaling spread-spectrum modulation of the LED visible light wireless communications using a mobile-phone camera

    Science.gov (United States)

    Chen, Shih-Hao; Chow, Chi-Wai

    2015-02-01

    Visible light communication (VLC) using spread spectrum modulation (SSM) and differential signaling (DS), detected by a mobile-phone camera is proposed and demonstrated for the first time to provide high immunity to background ambient light interference. The SSM signal provides the coding gain while the DS scheme enhances the clock recovery particular under high background ambient light. Experiment results confirm the feasibility of the proposed scheme, showing that the proposed system has 6-dB gain comparing with the traditional on-off keying (OOK) modulation under background ambient light of 3000 lux. The direct incident ambient light to the mobile-phone camera is 520 lux.

  20. From early wireless to Everest.

    Science.gov (United States)

    Allen, A

    1998-01-01

    Medical information has been transmitted using wireless technologies for almost 80 years. A "wired wireless" electronic stethoscope was developed by the U.S. Army Signal Corps in the early 1920's, for potential use in ship-to-shore transmission of cardiac sounds. [Winters SR. Diagnosis by wireless. Scientific American June 11, 1921, p. 465] Today, wireless is used in a wide range of medical applications and at sites from transoceanic air flights to offshore oil platforms to Mt. Everest. 'Wireless LANs' are often used in medical environments. Typically, nurses and physicians in a hospital or clinic use hand-held "wireless thin client" pen computers that exchange patient information and images with the hospital server. Numerous companies, such as Fujitsu (article below) and Cruise Technologies (www.cruisetech.com) manufacture handheld pen-entry computers. One company, LXE, integrates radio-frequency (RF) enhanced hand-held computers specifically designed for production use within a wireless LAN (www.lxe.com). Other companies (Proxim, Symbol, and others) supply the wireless RF LAN infrastructure for the enterprise. Unfortunately, there have been problems with widespread deployment of wireless LANs. Perhaps the biggest impediment has been the lack of standards. Although an international standard (IEEE 802.11) was adopted in 1997, most wireless LAN products still are not compatible with the equipment of competing companies. A problem with the current standard for LAN adapters is that throughput is limited to 3 Mbps--compared to at least 10 Mbps, and often 100 Mbps, in a hard-wired Ethernet LAN. An II Mbps standard is due out in the next year or so, but it will be at least 2 years before standards-compliant products are available. This story profiles some of the ways that wireless is being used to overcome gaps in terrestrial and within-enterprise communication.

  1. Tumor necrosis factor α triggers proliferation of adult neural stem cells via IKK/NF-κB signaling

    Directory of Open Access Journals (Sweden)

    Kaltschmidt Christian

    2006-09-01

    Full Text Available Abstract Background Brain inflammation has been recognized as a complex phenomenon with numerous related aspects. In addition to the very well-described neurodegenerative effect of inflammation, several studies suggest that inflammatory signals exert a potentially positive influence on neural stem cell proliferation, migration and differentiation. Tumor necrosis factor alpha (TNF-α is one of the best-characterized mediators of inflammation. To date, conclusions about the action of TNF on neural stem or progenitor cells (NSCs, NPCs have been conflicting. TNF seems to activate NSC proliferation and to inhibit their differentiation into NPCs. The purpose of the present study was to analyze the molecular signal transduction mechanisms induced by TNF and resulting in NSC proliferation. Results Here we describe for the first time the TNF-mediated signal transduction cascade in neural stem cells (NSCs that results in increased proliferation. Moreover, we demonstrate IKK-α/β-dependent proliferation and markedly up-regulated cyclin D1 expression after TNF treatment. The significant increase in proliferation in TNF-treated cells was indicated by increased neurosphere volume, increased bromodeoxyuridin (BrdU incorporation and a higher total cell number. Furthermore, TNF strongly activated nuclear factor-kappa B (NF-κB as measured by reporter gene assays and by an activity-specific antibody. Proliferation of control and TNF-treated NSCs was strongly inhibited by expression of the NF-κB super-repressor IκB-AA1. Pharmacological blockade of IκB ubiquitin ligase activity led to comparable decreases in NF-κB activity and proliferation. In addition, IKK-β gene product knock-down via siRNA led to diminished NF-κB activity, attenuated cyclin D1 expression and finally decreased proliferation. In contrast, TGFβ-activated kinase 1 (TAK-1 is partially dispensable for TNF-mediated and endogenous proliferation. Understanding stem cell proliferation is crucial

  2. Transcriptional Profiling of Hypoxic Neural Stem Cells Identifies Calcineurin-NFATc4 Signaling as a Major Regulator of Neural Stem Cell Biology

    Science.gov (United States)

    Moreno, Marta; Fernández, Virginia; Monllau, Josep M.; Borrell, Víctor; Lerin, Carles; de la Iglesia, Núria

    2015-01-01

    Summary Neural stem cells (NSCs) reside in a hypoxic microenvironment within the brain. However, the crucial transcription factors (TFs) that regulate NSC biology under physiologic hypoxia are poorly understood. Here we have performed gene set enrichment analysis (GSEA) of microarray datasets from hypoxic versus normoxic NSCs with the aim of identifying pathways and TFs that are activated under oxygen concentrations mimicking normal brain tissue microenvironment. Integration of TF target (TFT) and pathway enrichment analysis identified the calcium-regulated TF NFATc4 as a major candidate to regulate hypoxic NSC functions. Nfatc4 expression was coordinately upregulated by top hypoxia-activated TFs, while NFATc4 target genes were enriched in hypoxic NSCs. Loss-of-function analyses further revealed that the calcineurin-NFATc4 signaling axis acts as a major regulator of NSC self-renewal and proliferation in vitro and in vivo by promoting the expression of TFs, including Id2, that contribute to the maintenance of the NSC state. PMID:26235896

  3. 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

  4. Neural stem cells in the adult ciliary epithelium express GFAP and are regulated by Wnt signaling

    International Nuclear Information System (INIS)

    Das, Ani V.; Zhao Xing; James, Jackson; Kim, Min; Cowan, Kenneth H.; Ahmad, Iqbal

    2006-01-01

    The identification of neural stem cells with retinal potential in the ciliary epithelium (CE) of the adult mammals is of considerable interest because of their potential for replacing or rescuing degenerating retinal neurons in disease or injury. The evaluation of such a potential requires characterization of these cells with regard to their phenotypic properties, potential, and regulatory mechanisms. Here, we demonstrate that rat CE stem cells/progenitors in neurosphere culture display astrocytic nature in terms of expressing glial intermediate neurofilament protein, GFAP. The GFAP-expressing CE stem cells/progenitors form neurospheres in proliferating conditions and generate neurons when shifted to differentiating conditions. These cells express components of the canonical Wnt pathway and its activation promotes their proliferation. Furthermore, we demonstrate that the activation of the canonical Wnt pathway influences neuronal differentiation of CE stem cells/progenitors in a context dependent manner. Our observations suggest that CE stem cells/progenitors share phenotypic properties and regulatory mechanism(s) with neural stem cells elsewhere in the adult CNS

  5. 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.

  6. 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.

  7. 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

  8. Detection of uranium with a wireless sensing method by using salophen as receptor and magnetic nanoparticles as signal-amplifying tags

    International Nuclear Information System (INIS)

    Miao Yang; Lifu Liao; Guangliang Zhang; Bo He; Xilin Xiao; Yingwu Lin; Changming Nie

    2013-01-01

    A new wireless sensing method for the detection of uranium in water samples has been reported in this paper. The method is based on a sandwich-type detection strategy. Salophen, a tetradentate ligand of uranyl ion, was immobilized on the surface of the polyurethane-protected magnetoelastic sensor as receptor for the capture of uranyl ion. The phosphorylated polyvinyl alcohol coated magnetic Fe 3 O 4 nanoparticles were used as signal-amplifying tags of uranyl ion. In a procedure of determining uranium, firstly uranyl ion in sample solution was captured on the sensor surface. Then the captured uranyl bound the nanoparticle through its coordination with the phosphate group. The amount of uranium was detected through the measure of the resonance frequency shift caused by the enhanced mass loading on the sensor surface. A linear range was found to be 0.2-20.0 μg/L under optimal conditions with a detection limit of 0.11 μg/L. The method has been applied to determine uranium in environmental water samples with the relative standard deviations of 2.1-3.6 % and the recoveries of 98.0-101.5 %. The present technique is one of the most suitable techniques for assay of uranium at trace level in environmental water samples collected from different sources. (author)

  9. 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.

  10. Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

    Science.gov (United States)

    Avci, Onur; Abdeljaber, Osama; Kiranyaz, Serkan; Hussein, Mohammed; Inman, Daniel J.

    2018-06-01

    Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

  11. Optimization of a neural network model for signal-to-background prediction in gamma-ray spectrometry

    International Nuclear Information System (INIS)

    Dragovic, S.; Onjia, A. . E-mail address of corresponding author: sdragovic@inep.co.yu; Dragovic, S.)

    2005-01-01

    The artificial neural network (ANN) model was optimized for the prediction of signal-to-background (SBR) ratio as a function of the measurement time in gamma-ray spectrometry. The network parameters: learning rate (α), momentum (μ), number of epochs (E) and number of nodes in hidden layer (N) were optimized simultaneously employing variable-size simplex method. The most accurate model with the root mean square (RMS) error of 0.073 was obtained using ANN with online backpropagation randomized (OBPR) algorithm with α = 0.27, μ 0.36, E = 14800 and N = 9. Most of the predicted and experimental SBR values for the eight radionuclides ( 226 Ra, 214 Bi, 235 U, 40 K, 232 Th, 134 Cs, 137 Cs and 7 Be), studied in this work, reasonably agreed to within 15 %, which was satisfactory accuracy. (author)

  12. A reliability index for assessment of crack profile reconstructed from ECT signals using a neural-network approach

    International Nuclear Information System (INIS)

    Yusa, Noritaka; Chen, Zhenmao; Miya, Kenzo; Cheng, Weiying

    2002-01-01

    This paper proposes a reliability parameter to enhance an version scheme developed by authors. The scheme is based upon an artificial neural network that simulates mapping between eddy current signals and crack profiles. One of the biggest advantages of the scheme is that it can deal with conductive cracks, which is necessary to reconstruct natural cracks. However, it has one significant disadvantage: the reliability of reconstructed profiles was unknown. The parameter provides an index for assessment of the crack profile and overcomes this disadvantage. After the parameter is validated by reconstruction of simulated cracks, it is applied to reconstruction of natural cracks that occurred in steam generator tubes of a pressurized water reactor. It is revealed that the parameter is applicable to not only simulated cracks but also natural ones. (author)

  13. 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.

  14. The signal extraction of fetal heart rate based on wavelet transform and BP neural network

    Science.gov (United States)

    Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

    2005-04-01

    This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.

  15. Self-organized neural network for the quality control of 12-lead ECG signals

    International Nuclear Information System (INIS)

    Chen, Yun; Yang, Hui

    2012-01-01

    Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

  16. Introduction to Ultra Wideband for Wireless Communications

    DEFF Research Database (Denmark)

    Nikookar, Homayoun; Prasad, Ramjee

    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......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...... 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...

  17. Monitoring Activity for Recognition of Illness in Experimentally Infected Weaned Piglets Using Received Signal Strength Indication ZigBee-based Wireless Acceleration Sensor

    Directory of Open Access Journals (Sweden)

    Sonia Tabasum Ahmed

    2016-01-01

    Full Text Available In this experiment, we proposed and implemented a disease forecasting system using a received signal strength indication ZigBee-based wireless network with a 3-axis acceleration sensor to detect illness at an early stage by monitoring movement of experimentally infected weaned piglets. Twenty seven piglets were divided into control, Salmonella enteritidis (SE infection, and Escherichia coli (EC infection group, and their movements were monitored for five days using wireless sensor nodes on their backs. Data generated showed the 3-axis movement of piglets (X-axis: left and right direction, Y-axis: anteroposterior direction, and Z-axis: up and down direction at five different time periods. Piglets in both infected groups had lower weight gain and feed intake, as well as higher feed conversion ratios than the control group (p<0.05. Infection with SE and EC resulted in reduced body temperature of the piglets at day 2, 4, and 5 (p<0.05. The early morning X-axis movement did not differ between groups; however, the Y-axis movement was higher in the EC group (day 1 and 2, and the Z-axis movement was higher in the EC (day 1 and SE group (day 4 during different experimental periods (p<0.05. The morning X and Y-axis movement did not differ between treatment groups. However, the Z-axis movement was higher in both infected groups at day 1 and lower at day 4 compared to the control (p<0.05. The midday X-axis movement was significantly lower in both infected groups (day 4 and 5 compared to the control (p<0.05, whereas the Y-axis movement did not differ. The Z-axis movement was highest in the SE group at day 1 and 2 and lower at day 4 and 5 (p<0.05. Evening X-axis movement was highest in the control group throughout the experimental period. During day 1 and 2, the Z-axis movement was higher in both of the infected groups; whereas it was lower in the SE group during day 3 and 4 (p<0.05. During day 1 and 2, the night X-axis movement was lower and the Z

  18. 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.

  19. 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.

  20. Wireless data link for FBTR

    International Nuclear Information System (INIS)

    Sundararajan, M.K.; Prabhakara Rao, G.; Ilango Sambasivan, S.; Swaminathan, P.; Ramakrishna, P.V.

    2004-01-01

    This paper deals with the design and development of a wireless data link for transmission of block pile signals at the Fast Breeder Test Reactor (FBTR) of Indira Gandhi Center for Atomic Research (IGCAR). This link is to establish wireless connectivity, typically at RS232C rates, over distances of the order of 50 m, and is expected to operate under electrically hostile conditions. (author)

  1. 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.

  2. Transcriptomic Analysis Of Purified Embryonic Neural Stem Cells From Zebrafish Embryos Reveals Signalling Pathways Involved In Glycine-dependent Neurogenesis

    Directory of Open Access Journals (Sweden)

    Eric eSAMARUT

    2016-03-01

    Full Text Available How is the initial set of neurons correctly established during the development of the vertebrate central nervous system? In the embryo, glycine and GABA are depolarizing due the immature chloride gradient, which is only reversed to become hyperpolarizing later in post-natal development. We previously showed that glycine regulates neurogenesis via paracrine signalling that promotes calcium transients in neural stem cells (NSCs and their differentiation into interneurons within the spinal cord of the zebrafish embryo. However, the subjacent molecular mechanisms are not yet understood. Our previous work suggests that early neuronal progenitors were not differentiating correctly in the developing spinal cord. As a result, we aimed at identifying the downstream molecular mechanisms involved specifically in NSCs during glycine-dependent embryonic neurogenesis. Using a gfap:GFP transgenic line, we successfully purified NSCs by fluorescence-activated cell sorting (FACS from whole zebrafish embryos and in embryos in which the glycine receptor was knocked down. The strength of this approach is that it focused on the NSC population while tackling the biological issue in an in vivo context in whole zebrafish embryos. After sequencing the transcriptome by RNA-sequencing, we analyzed the genes whose expression was changed upon disruption of glycine signalling and we confirmed the differential expression by independent RTqPCR assay. While over a thousand genes showed altered expression levels, through pathway analysis we identified 14 top candidate genes belonging to five different canonical signalling pathways (signalling by calcium, TGF-beta, sonic hedgehog, Wnt and p53-related apoptosis that are likely to mediate the promotion of neurogenesis by glycine.

  3. 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...

  4. Mitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networks

    OpenAIRE

    de Paz Santana, Juan F.; Tapia Martínez, Dante I.; Alonso Rincón, Ricardo S.; Pinzón, Cristian; Bajo Pérez, Javier; Corchado Rodríguez, Juan M.

    2017-01-01

    Wireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the ...

  5. Wireless Cybersecurity

    Science.gov (United States)

    2013-04-01

    completely change the entire landscape. For example, under the quantum computing regime, factoring prime numbers requires only polynomial time (i.e., Shor’s...AFRL-OSR-VA-TR-2013-0206 Wireless Cybersecurity Biao Chen Syracuse University April 2013 Final Report DISTRIBUTION A...19a. NAME OF RESPONSIBLE PERSON 19b. TELEPHONE NUMBER (Include area code) 21-02-2013 FINAL REPORT 01-04-2009 TO 30-11-2012 Wireless Cybersecurity

  6. Physiology of CA2+ signaling in human embryonic stem cell-derived neural precursors

    Czech Academy of Sciences Publication Activity Database

    Forostyak, Oksana; Romanyuk, Nataliya; Syková, Eva; Dayanithi, Govindan

    2011-01-01

    Roč. 59, S1 (2011), S119-S119 ISSN 0894-1491. [European meeting on Glia l Cells in Health and Disease /10./. 13.09.2011-17.09.2011, Prague] R&D Projects: GA ČR(CZ) GAP303/11/0192 Institutional research plan: CEZ:AV0Z50390703 Keywords : calcium signaling * ATP * calcium channels Subject RIV: FH - Neurology

  7. Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan.

    Science.gov (United States)

    Nomi, Jason S; Bolt, Taylor S; Ezie, C E Chiemeka; Uddin, Lucina Q; Heller, Aaron S

    2017-05-31

    Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation. SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan. Copyright © 2017 the authors 0270-6474/17/375539-10$15.00/0.

  8. Excess thyroid hormone inhibits embryonic neural stem/progenitor cells proliferation and maintenance through STAT3 signalling pathway.

    Science.gov (United States)

    Chen, Chunhai; Zhou, Zhou; Zhong, Min; Li, Maoquan; Yang, Xuesen; Zhang, Yanwen; Wang, Yuan; Wei, Aimin; Qu, Mingyue; Zhang, Lei; Xu, Shangcheng; Chen, Shude; Yu, Zhengping

    2011-07-01

    Hyperthyroidism is prevalent during pregnancy, but little is known about the effects of excess thyroid hormone on the development of embryonic neural stem/progenitor cells (NSCs), and the mechanisms underlying these effects. Previous studies indicate that STAT3 plays a crucial role in determining NSC fate during neurodevelopment. In this study, we investigated the effects of a supraphysiological dose of 3,5,3'-L-triiodothyronine (T3) on the proliferation and maintenance of NSCs derived from embryonic day 13.5 mouse neocortex, and the involvement of STAT3 in this process. Our results suggest that excess T3 treatment inhibits NSC proliferation and maintenance. T3 decreased tyrosine phosphorylation of JAK1, JAK2 and STAT3, and subsequently inhibited STAT3-DNA binding activity. Furthermore, proliferation and maintenance of NSCs were decreased by inhibitors of JAKs and STAT3, indicating that the STAT3 signalling pathway is involved in the process of NSC proliferation and maintenance. Taken together, these results suggest that the STAT3 signalling pathway is involved in the process of T3-induced inhibition of embryonic NSC proliferation and maintenance. These findings provide data for understanding the effects of hyperthyroidism during pregnancy on fetal brain development, and the mechanisms underlying these effects.

  9. Wireless multi-channel single unit recording in freely moving and vocalizing primates.

    Science.gov (United States)

    Roy, Sabyasachi; Wang, Xiaoqin

    2012-01-15

    The ability to record well-isolated action potentials from individual neurons in naturally behaving animals is crucial for understanding neural mechanisms underlying natural behaviors. Traditional neurophysiology techniques, however, require the animal to be restrained which often restricts natural behavior. An example is the common marmoset (Callithrix jacchus), a highly vocal New World primate species, used in our laboratory to study the neural correlates of vocal production and sensory feedback. When restrained by traditional neurophysiological techniques marmoset vocal behavior is severely inhibited. Tethered recording systems, while proven effective in rodents pose limitations in arboreal animals such as the marmoset that typically roam in a three-dimensional environment. To overcome these obstacles, we have developed a wireless neural recording technique that is capable of collecting single-unit data from chronically implanted multi-electrodes in freely moving marmosets. A lightweight, low power and low noise wireless transmitter (headstage) is attached to a multi-electrode array placed in the premotor cortex of the marmoset. The wireless headstage is capable of transmitting 15 channels of neural data with signal-to-noise ratio (SNR) comparable to a tethered system. To minimize radio-frequency (RF) and electro-magnetic interference (EMI), the experiments were conducted within a custom designed RF/EMI and acoustically shielded chamber. The individual electrodes of the multi-electrode array were periodically advanced to densely sample the cortical layers. We recorded single-unit data over a period of several months from the frontal cortex of two marmosets. These recordings demonstrate the feasibility of using our wireless recording method to study single neuron activity in freely roaming primates. Copyright © 2011 Elsevier B.V. All rights reserved.

  10. Xanomeline suppresses excessive pro-inflammatory cytokine responses through neural signal-mediated pathways and improves survival in lethal inflammation

    Science.gov (United States)

    Rosas-Ballina, Mauricio; Ferrer, Sergio Valdés; Dancho, Meghan; Ochani, Mahendar; Katz, David; Cheng, Kai Fan; Olofsson, Peder S.; Chavan, Sangeeta S.; Al-Abed, Yousef; Tracey, Kevin J.; Pavlov, Valentin A.

    2014-01-01

    Inflammatory conditions characterized by excessive immune cell activation and cytokine release, are associated with bidirectional immune system-brain communication, underlying sickness behavior and other physiological responses. The vagus nerve has an important role in this communication by conveying sensory information to the brain, and brain-derived immunoregulatory signals that suppress peripheral cytokine levels and inflammation. Brain muscarinic acetylcholine receptor (mAChR)-mediated cholinergic signaling has been implicated in this regulation. However, the possibility of controlling inflammation by peripheral administration of centrally-acting mAChR agonists is unexplored. To provide insight we used the centrally-acting M1 mAChR agonist xanomeline, previously developed in the context of Alzheimer’s disease and schizophrenia. Intraperitoneal administration of xanomeline significantly suppressed serum and splenic TNF levels, alleviated sickness behavior, and increased survival during lethal murine endotoxemia. The anti-inflammatory effects of xanomeline were brain mAChR-mediated and required intact vagus nerve and splenic nerve signaling. The anti-inflammatory efficacy of xanomeline was retained for at least 20h, associated with alterations in splenic lymphocyte, and dendritic cell proportions, and decreased splenocyte responsiveness to endotoxin. These results highlight an important role of the M1 mAChR in a neural circuitry to spleen in which brain cholinergic activation lowers peripheral pro-inflammatory cytokines to levels favoring survival. The therapeutic efficacy of xanomeline was also manifested by significantly improved survival in preclinical settings of severe sepsis. These findings are of interest for strategizing novel therapeutic approaches in inflammatory diseases. PMID:25063706

  11. 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.

  12. Phase dynamics of complex-valued neural networks and its application to traffic signal control.

    Science.gov (United States)

    Nishikawa, Ikuko; Iritani, Takeshi; Sakakibara, Kazutoshi; Kuroe, Yasuaki

    2005-01-01

    Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.

  13. Differentiation potential of neonatal neural stem/progenitor cells is affected by WNT signaling

    Czech Academy of Sciences Publication Activity Database

    Kriška, Ján; Honsa, Pavel; Džamba, Dávid; Butenko, Olena; Tůmová, L.; Kořínek, Vladimír; Anděrová, Miroslava

    2015-01-01

    Roč. 134, SI (2015), s. 181-181 ISSN 0022-3042. [25th Biennial Meeting of the International-Society-for-Neurochemistry Jointly with the 13th Meeting of the Asian-Pacific-Society-for-Neurochemistry in Conjuction with the 35th Meeting of the Australasian-Neuroscience-Society. 23.08.2015-27.08.2015, Cairns] R&D Projects: GA ČR(CZ) GAP303/12/0855 Institutional support: RVO:68378041 ; RVO:68378050 Keywords : Wnt signaling * beta-catenin * neurogenesis Subject RIV: EB - Genetics ; Molecular Biology

  14. Neurotrophin-3 promotes proliferation and cholinergic neuronal differentiation of bone marrow- derived neural stem cells via notch signaling pathway.

    Science.gov (United States)

    Yan, Yu-Hui; Li, Shao-Heng; Gao, Zhong; Zou, Sa-Feng; Li, Hong-Yan; Tao, Zhen-Yu; Song, Jie; Yang, Jing-Xian

    2016-12-01

    Recently, the potential for neural stem cells (NSCs) to be used in the treatment of Alzheimer's disease (AD) has been reported; however, the therapeutic effects are modest by virtue of the low neural differentiation rate. In our study, we transfected bone marrow-derived NSCs (BM-NSCs) with Neurotrophin-3 (NT-3), a superactive neurotrophic factor that promotes neuronal survival, differentiation, and migration of neuronal cells, to investigate the effects of NT-3 gene overexpression on the proliferation and differentiation into cholinergic neuron of BM-NSCs in vitro and its possible molecular mechanism. BM-NSCs were generated from BM mesenchymal cells of adult C57BL/6 mice and cultured in vitro. After transfected with NT-3 gene, immunofluorescence and RT-PCR method were used to determine the ability of BM-NSCs on proliferation and differentiation into cholinergic neuron; Acetylcholine Assay Kit was used for acetylcholine (Ach). RT-PCR and WB analysis were used to characterize mRNA and protein level related to the Notch signaling pathway. We found that NT-3 can promote the proliferation and differentiation of BM-NSCs into cholinergic neurons and elevate the levels of acetylcholine (ACh) in the supernatant. Furthermore, NT-3 gene overexpression increase the expression of Hes1, decreased the expression of Mash1 and Ngn1 during proliferation of BM-NSCs. Whereas, the expression of Hes1 was down-regulated, and Mash1 and Ngn1 expression were up-regulated during differentiation of BM-NSCs. Our findings support the prospect of using NT-3-transduced BM-NSCs in developing therapies for AD due to their equivalent therapeutic potential as subventricular zone-derived NSCs (SVZ-NSCs), greater accessibility, and autogenous attributes. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. A wireless multi-channel recording system for freely behaving mice and rats.

    Science.gov (United States)

    Fan, David; Rich, Dylan; Holtzman, Tahl; Ruther, Patrick; Dalley, Jeffrey W; Lopez, Alberto; Rossi, Mark A; Barter, Joseph W; Salas-Meza, Daniel; Herwik, Stanislav; Holzhammer, Tobias; Morizio, James; Yin, Henry H

    2011-01-01

    To understand the neural basis of behavior, it is necessary to record brain activity in freely moving animals. Advances in implantable multi-electrode array technology have enabled researchers to record the activity of neuronal ensembles from multiple brain regions. The full potential of this approach is currently limited by reliance on cable tethers, with bundles of wires connecting the implanted electrodes to the data acquisition system while impeding the natural behavior of the animal. To overcome these limitations, here we introduce a multi-channel wireless headstage system designed for small animals such as rats and mice. A variety of single unit and local field potential signals were recorded from the dorsal striatum and substantia nigra in mice and the ventral striatum and prefrontal cortex simultaneously in rats. This wireless system could be interfaced with commercially available data acquisition systems, and the signals obtained were comparable in quality to those acquired using cable tethers. On account of its small size, light weight, and rechargeable battery, this wireless headstage system is suitable for studying the neural basis of natural behavior, eliminating the need for wires, commutators, and other limitations associated with traditional tethered recording systems.

  20. A wireless multi-channel recording system for freely behaving mice and rats.

    Directory of Open Access Journals (Sweden)

    David Fan

    Full Text Available To understand the neural basis of behavior, it is necessary to record brain activity in freely moving animals. Advances in implantable multi-electrode array technology have enabled researchers to record the activity of neuronal ensembles from multiple brain regions. The full potential of this approach is currently limited by reliance on cable tethers, with bundles of wires connecting the implanted electrodes to the data acquisition system while impeding the natural behavior of the animal. To overcome these limitations, here we introduce a multi-channel wireless headstage system designed for small animals such as rats and mice. A variety of single unit and local field potential signals were recorded from the dorsal striatum and substantia nigra in mice and the ventral striatum and prefrontal cortex simultaneously in rats. This wireless system could be interfaced with commercially available data acquisition systems, and the signals obtained were comparable in quality to those acquired using cable tethers. On account of its small size, light weight, and rechargeable battery, this wireless headstage system is suitable for studying the neural basis of natural behavior, eliminating the need for wires, commutators, and other limitations associated with traditional tethered recording systems.

  1. Cooperative Diversity in Wireless Networks

    Directory of Open Access Journals (Sweden)

    A. Mahmood

    2010-01-01

    Full Text Available Transmit Diversity is an effective methodology for improving the quality and reliability of a wireless network by reducingthe effects of fading. As majority of the wireless devices (i.e. mobile handsets, etc are limited to only one antenna, especiallydue to hardware constraints, size and cost factors; cooperative communication can be utilized in order to generatetransmit diversity [1]. This enables single antenna wireless devices to share their antennas during transmission in such amanner that creates a virtual MIMO (multiple-input and multiple-output system [2] [3]. In this paper, we will analyze therecent developments and trends in this promising area of wireless Ad hoc networks. The article will also discuss variousmain cooperative signaling methods and will also observe their performance.

  2. Multiphasic on/off pheromone signalling in moths as neural correlates of a search strategy.

    Directory of Open Access Journals (Sweden)

    Dominique Martinez

    Full Text Available Insects and robots searching for odour sources in turbulent plumes face the same problem: the random nature of mixing causes fluctuations and intermittency in perception. Pheromone-tracking male moths appear to deal with discontinuous flows of information by surging upwind, upon sensing a pheromone patch, and casting crosswind, upon losing the plume. Using a combination of neurophysiological recordings, computational modelling and experiments with a cyborg, we propose a neuronal mechanism that promotes a behavioural switch between surge and casting. We show how multiphasic On/Off pheromone-sensitive neurons may guide action selection based on signalling presence or loss of the pheromone. A Hodgkin-Huxley-type neuron model with a small-conductance calcium-activated potassium (SK channel reproduces physiological On/Off responses. Using this model as a command neuron and the antennae of tethered moths as pheromone sensors, we demonstrate the efficiency of multiphasic patterning in driving a robotic searcher toward the source. Taken together, our results suggest that multiphasic On/Off responses may mediate olfactory navigation and that SK channels may account for these responses.

  3. Multiphasic on/off pheromone signalling in moths as neural correlates of a search strategy.

    Science.gov (United States)

    Martinez, Dominique; Chaffiol, Antoine; Voges, Nicole; Gu, Yuqiao; Anton, Sylvia; Rospars, Jean-Pierre; Lucas, Philippe

    2013-01-01

    Insects and robots searching for odour sources in turbulent plumes face the same problem: the random nature of mixing causes fluctuations and intermittency in perception. Pheromone-tracking male moths appear to deal with discontinuous flows of information by surging upwind, upon sensing a pheromone patch, and casting crosswind, upon losing the plume. Using a combination of neurophysiological recordings, computational modelling and experiments with a cyborg, we propose a neuronal mechanism that promotes a behavioural switch between surge and casting. We show how multiphasic On/Off pheromone-sensitive neurons may guide action selection based on signalling presence or loss of the pheromone. A Hodgkin-Huxley-type neuron model with a small-conductance calcium-activated potassium (SK) channel reproduces physiological On/Off responses. Using this model as a command neuron and the antennae of tethered moths as pheromone sensors, we demonstrate the efficiency of multiphasic patterning in driving a robotic searcher toward the source. Taken together, our results suggest that multiphasic On/Off responses may mediate olfactory navigation and that SK channels may account for these responses.

  4. 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.

  5. Introductory survey for wireless infrared communications

    Directory of Open Access Journals (Sweden)

    Munsif Ali Jatoi

    2014-08-01

    Full Text Available Wireless infrared communications can be defined as the propagation of light waves in free space using infrared radiation whose range is 400–700 nm. This range corresponds to frequencies of hundreds of terahertz, which is high for higher data rate applications. Wireless infrared is applied for higher data rates applications such as wireless computing, wireless video and wireless multimedia communication applications. Introduced by Gfeller, this field has grown with different link configurations, improved transmitter efficiency, increased receiver responsivity and various multiple access techniques for improved quality. Errors are caused because of background light, which causes degradation overall system performance. Error correction techniques are used to remove the errors caused during transmission. This study provides a brief account on field theory used for error correction in wireless infrared systems. The results are produced in terms of bit error rate and signal-to-noise ratio for various bit lengths to show the ability of encoding and decoding algorithms.

  6. 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.

  7. 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.

  8. MicroRNA let-7b regulates neural stem cell proliferation and differentiation by targeting nuclear receptor TLX signaling.

    Science.gov (United States)

    Zhao, Chunnian; Sun, GuoQiang; Li, Shengxiu; Lang, Ming-Fei; Yang, Su; Li, Wendong; Shi, Yanhong

    2010-02-02

    Neural stem cell self-renewal and differentiation is orchestrated by precise control of gene expression involving nuclear receptor TLX. Let-7b, a member of the let-7 microRNA family, is expressed in mammalian brains and exhibits increased expression during neural differentiation. However, the role of let-7b in neural stem cell proliferation and differentiation remains unknown. Here we show that let-7b regulates neural stem cell proliferation and differentiation by targeting the stem cell regulator TLX and the cell cycle regulator cyclin D1. Overexpression of let-7b led to reduced neural stem cell proliferation and increased neural differentiation, whereas antisense knockdown of let-7b resulted in enhanced proliferation of neural stem cells. Moreover, in utero electroporation of let-7b to embryonic mouse brains led to reduced cell cycle progression in neural stem cells. Introducing an expression vector of Tlx or cyclin D1 that lacks the let-7b recognition site rescued let-7b-induced proliferation deficiency, suggesting that both TLX and cyclin D1 are important targets for let-7b-mediated regulation of neural stem cell proliferation. Let-7b, by targeting TLX and cyclin D1, establishes an efficient strategy to control neural stem cell proliferation and differentiation.

  9. BDNF Overexpression Exhibited Bilateral Effect on Neural Behavior in SCT Mice Associated with AKT Signal Pathway.

    Science.gov (United States)

    Chen, Mei-Rong; Dai, Ping; Wang, Shu-Fen; Song, Shu-Hua; Wang, Hang-Ping; Zhao, Ya; Wang, Ting-Hua; Liu, Jia

    2016-10-01

    Spinal cord injury (SCI), a severe health problem in worldwide, was commonly associated with functional disability and reduced quality of life. As the expression of brain-derived neurotrophic factor (BDNF) was substantial event in injured spinal cord, we hypothesized whether BDNF-overexpression could be in favor of the recovery of both sensory function and hindlimb function after SCI. By using BDNF-overexpression transgene mice [CMV-BDNF 26 (CB26) mice] we assessed the role of BDNF on the recovery of neurological behavior in spinal cord transection (SCT) model. BMS score and tail-flick test was performed to evaluate locomotor function and sensory function, respectively. Immunohistochemistry was employed to detect the location and the expression of BDNF, NeuN, 5-HT, GAP-43, GFAP as well as CGRP, and the level of p-AKT and AKT were examined through western blot analysis. BDNF overexpressing resulted in significant locomotor functional recovery from 21 to 28 days after SCT, compared with wild type (WT)+SCT group. Meanwhile, the NeuN, 5-HT and GAP-43 positive cells were markedly increased in ventral horn in BDNF overexpression animals, compared with WT mice with SCT. Moreover, the crucial molecular signal, p-AKT/AKT has been largely up-regulated, which is consistent with the improvement of locomotor function. However, in this study, thermal hyperpathia encountered in sham (CB26) group and WT+SCT mice and further aggravated in CB26 mice after SCT. Also, following SCT, the significant augment of positive-GFAP astrocytes and CGRP fibers were found in WT+SCT mice, and further increase was seen in BDNF over-expression transgene mice. BDNF-overexpression may not only facilitate the recovery of locomotor function via AKT pathway, but also contributed simultaneously to thermal hyperalgesia after SCT.

  10. Classification Technique for Ultrasonic Weld Inspection Signals using a Neural Network based on 2-dimensional fourier Transform and Principle Component Analysis

    International Nuclear Information System (INIS)

    Kim, Jae Joon

    2004-01-01

    Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented

  11. Spatio-temporal Model of Endogenous ROS and Raft-Dependent WNT/Beta-Catenin Signaling Driving Cell Fate Commitment in Human Neural Progenitor Cells

    Science.gov (United States)

    Haack, Fiete; Lemcke, Heiko; Ewald, Roland; Rharass, Tareck; Uhrmacher, Adelinde M.

    2015-01-01

    Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model’s predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the

  12. Predictive power control in wireless sensor networks

    NARCIS (Netherlands)

    Chincoli, M.; Syed, Aly; Mocanu, D.C.; Liotta, A.

    2016-01-01

    Communications in Wireless Sensor Networks (WSNs) are affected by dynamic environments, variable signal fluctuations and interference. Thus, prompt actions are necessary to achieve dependable communications and meet quality of service requirements. To this end, the reactive algorithms used in

  13. Wireless Tots

    Science.gov (United States)

    Scott, Lee-Allison

    2003-01-01

    The first wireless technology program for preschoolers was implemented in January at the Primrose School at Bentwater in Atlanta, Georgia, a new corporate school operated by Primrose School Franchising Co. The new school serves as a testing and training facility for groundbreaking educational approaches, including emerging innovations in…

  14. Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

    Directory of Open Access Journals (Sweden)

    Irina Popova

    2013-08-01

    Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.

  15. Patterns of cortical oscillations organize neural activity into whole-brain functional networks evident in the fMRI BOLD signal

    Directory of Open Access Journals (Sweden)

    Jennifer C Whitman

    2013-03-01

    Full Text Available Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG / MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks.

  16. An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

    NARCIS (Netherlands)

    Aiello, F.; Bellifemine, F.L.; Fortino, G.; Galzarano, S.; Gravina, R.

    2011-01-01

    Nowadays wireless body sensor networks (WBSNs) have great potential to enable a broad variety of assisted living applications such as human biophysical/biochemical control and activity monitoring for health care, e-fitness, emergency detection, emotional recognition for social networking, security,

  17. Signaling through three chemokine receptors triggers the migration of transplanted neural precursor cells in a model of multiple sclerosis.

    Science.gov (United States)

    Cohen, Mikhal E; Fainstein, Nina; Lavon, Iris; Ben-Hur, Tamir

    2014-09-01

    Multiple sclerosis (MS) is a multifocal disease, and precursor cells need to migrate into the multiple lesions in order to exert their therapeutic effects. Therefore, cell migration is a crucial element in regenerative processes in MS, dictating the route of delivery, when cell transplantation is considered. We have previously shown that inflammation triggers migration of multi-potential neural precursor cells (NPCs) into the white matter of experimental autoimmune encephalomyelitis (EAE) rodents, a widely used model of MS. Here we investigated the molecular basis of this attraction. NPCs were grown from E13 embryonic mouse brains and transplanted into the lateral cerebral ventricles of EAE mice. Transplanted NPC migration was directed by three tissue-derived chemokines. Stromal cell-derived factor-1α, monocyte chemo-attractant protein-1 and hepatocyte growth factor were expressed in the EAE brain and specifically in microglia and astrocytes. Their cognate receptors, CXCR4, CCR2 or c-Met were constitutively expressed on NPCs. Selective blockage of CXCR4, CCR2 or c-Met partially inhibited NPC migration in EAE brains. Blocking all three receptors had an additive effect and resulted in profound inhibition of NPC migration, as compared to extensive migration of control NPCs. The inflammation-triggered NPC migration into white matter tracts was dependent on a motile NPC phenotype. Specifically, depriving NPCs from epidermal growth factor (EGF) prevented the induction of glial commitment and a motile phenotype (as indicated by an in vitro motility assay), hampering their response to neuroinflammation. In conclusion, signaling via three chemokine systems accounts for most of the inflammation-induced, tissue-derived attraction of transplanted NPCs into white matter tracts during EAE. Copyright © 2014. Published by Elsevier B.V.

  18. A novel framework for intelligent signal detection via artificial neural networks for cyclic voltammetry in pyroprocessing technology

    International Nuclear Information System (INIS)

    Rakhshan Pouri, Samaneh; Manic, Milos; Phongikaroon, Supathorn

    2018-01-01

    Highlights: •First time ANN implementation toward pyroprocessing safeguards. •Real time monitoring in terms of intelligent materials detection and accountability. •CV simulation via ANN showing a high accuracy of prediction for the unseen situation. •Elimination of trial and error approach to avoid overfitting in learning. -- Abstract: Electrorefiner (ER) is the heart of pyroprocessing technology which contains different fission, rare-earth, and transuranic chloride compositions during the operation. This is still a developing technology that needs to be advanced for the commercial reprocessing design of used nuclear fuel (UNF) in terms of intelligent materials detection and accountability towards safeguards. A novel signal detection, artificial neural network (ANN), has been proposed in this study to apply on massive ER systemic parameters to simulate cyclic voltammetry (CV) graphs for the unseen situation. ANN could be trained to mimic the system by driving the data sets interrelation between variables to provide current and potential simulated data sets with a high accuracy of prediction. For this purpose, over 230,000 experimental data points reported in literature have been explored—0.5–5 wt% of zirconium chloride (ZrCl 4 ) in LiCl-KCl molten salt with different scan rates at 773 K. This study has illustrated a new framework of ANN implementation to eliminate trial and error approach by comparing the average error of one to three hidden layers with different number of neurons. In addition, this framework results in finding a preferable balance between underfitting and overfitting in deep learning. Furthermore, simulated CV graphs were compared with the experimental data and illustrated a reasonable prediction. The results reveal two structures with three hidden layers providing a good prediction with a low average error. The outcomes indicate that ANN has a strong potential in applying toward safeguards for pyroprocessing technology.

  19. Efficient universal computing architectures for decoding neural activity.

    Directory of Open Access Journals (Sweden)

    Benjamin I Rapoport

    Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion

  20. 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....

  1. 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.

  2. 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.

  3. System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

    Science.gov (United States)

    Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

    2017-04-01

    This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

  4. 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

  5. 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.

  6. Deployable wireless Fresnel lens

    Science.gov (United States)

    Kennedy, Timothy F. (Inventor); Fink, Patrick W. (Inventor); Chu, Andrew W. (Inventor); Lin, Gregory Y. (Inventor)

    2013-01-01

    Apparatus and methods for enhancing the gain of a wireless signal are provided. In at least one specific embodiment, the apparatus can include a screen comprised of one or more electrically conductive regions for reflecting electromagnetic radiation and one or more non-conductive regions for permitting electromagnetic radiation therethrough. The one or more electrically conductive regions can be disposed adjacent to at least one of the one or more non-conductive regions. The apparatus can also include a support member disposed about at least a portion of the screen. The screen can be capable of collapsing by twisting the support member in opposite screw senses to form interleaved concentric sections.

  7. Wireless installation standard

    International Nuclear Information System (INIS)

    Lim, Hwang Bin

    2007-12-01

    This is divided six parts which are radio regulation law on securing of radio resource, use of radio resource, protection of radio resource, radio regulation enforcement ordinance with securing, distribution and assignment of radio regulation, radio regulation enforcement regulation on utility of radio resource and technical qualification examination, a wireless installation regulation of technique standard and safety facility standard, radio regulation such as certification regulation of information communicative machines and regulation of radio station on compliance of signal security, radio equipment in radio station, standard frequency station and emergency communication.

  8. 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.

  9. Conscious wireless electroretinogram and visual evoked potentials in rats.

    Directory of Open Access Journals (Sweden)

    Jason Charng

    Full Text Available The electroretinogram (ERG, retina and visual evoked potential (VEP, brain are widely used in vivo tools assaying the integrity of the visual pathway. Current recordings in preclinical models are conducted under anesthesia, which alters neural physiology and contaminates responses. We describe a conscious wireless ERG and VEP recording platform in rats. Using a novel surgical technique to chronically implant electrodes subconjunctivally on the eye and epidurally over the visual cortex, we are able to record stable and repeatable conscious ERG and VEP signals over at least 1 month. We show that the use of anaesthetics, necessary for conventional ERG and VEP measurements, alters electrophysiology recordings. Conscious visual electrophysiology improves the viability of longitudinal studies by eliminating complications associated with repeated anaesthesia. It will also enable uncontaminated assessment of drug effects, allowing the eye to be used as an effective biomarker of the central nervous system.

  10. Brain substrates of implicit and explicit memory: the importance of concurrently acquired neural signals of both memory types.

    Science.gov (United States)

    Voss, Joel L; Paller, Ken A

    2008-11-01

    A comprehensive understanding of human memory requires cognitive and neural descriptions of memory processes along with a conception of how memory processing drives behavioral responses and subjective experiences. One serious challenge to this endeavor is that an individual memory process is typically operative within a mix of other contemporaneous memory processes. This challenge is particularly disquieting in the context of implicit memory, which, unlike explicit memory, transpires without the subject necessarily being aware of memory retrieval. Neural correlates of implicit memory and neural correlates of explicit memory are often investigated in different experiments using very different memory tests and procedures. This strategy poses difficulties for elucidating the interactions between the two types of memory process that may result in explicit remembering, and for determining the extent to which certain neural processing events uniquely contribute to only one type of memory. We review recent studies that have succeeded in separately assessing neural correlates of both implicit memory and explicit memory within the same paradigm using event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI), with an emphasis on studies from our laboratory. The strategies we describe provide a methodological framework for achieving valid assessments of memory processing, and the findings support an emerging conceptualization of the distinct neurocognitive events responsible for implicit and explicit memory.

  11. Study on Magneto-Hydro-Dynamics Disturbance Signal Feature Classification Using Improved S-Transform Algorithm and Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Nan YU

    2014-09-01

    Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.

  12. 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....

  13. 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.

  14. 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.

  15. Brain substrates of implicit and explicit memory: The importance of concurrently acquired neural signals of both memory types

    OpenAIRE

    Voss, Joel L.; Paller, Ken A.

    2008-01-01

    A comprehensive understanding of human memory requires cognitive and neural descriptions of memory processes along with a conception of how memory processing drives behavioral responses and subjective experiences. One serious challenge to this endeavor is that an individual memory process is typically operative within a mix of other contemporaneous memory processes. This challenge is particularly disquieting in the context of implicit memory, which, unlike explicit memory, transpires without ...

  16. 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.

  17. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  18. A wireless sensor enabled by wireless power.

    Science.gov (United States)

    Lee, Da-Sheng; Liu, Yu-Hong; Lin, Chii-Ruey

    2012-11-22

    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.

  19. FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro

    Science.gov (United States)

    Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

    2015-01-01

    The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

  20. Differential roles of AVP and VIP signaling in the postnatal changes of neural networks for coherent circadian rhythms in the SCN

    Science.gov (United States)

    Ono, Daisuke; Honma, Sato; Honma, Ken-ichi

    2016-01-01

    The suprachiasmatic nucleus (SCN) is the site of the master circadian clock in mammals. The SCN neural network plays a critical role in expressing the tissue-level circadian rhythm. Previously, we demonstrated postnatal changes in the SCN network in mice, in which the clock gene products CRYPTOCHROMES (CRYs) are involved. Here, we show that vasoactive intestinal polypeptide (VIP) signaling is essential for the tissue-level circadian PER2::LUC rhythm in the neonatal SCN of CRY double-deficient mice (Cry1,2−/−). VIP and arginine vasopressin (AVP) signaling showed redundancy in expressing the tissue-level circadian rhythm in the SCN. AVP synthesis was significantly attenuated in the Cry1,2−/− SCN, which contributes to aperiodicity in the adult mice together with an attenuation of VIP signaling as a natural process of ontogeny. The SCN network consists of multiple clusters of cellular circadian rhythms that are differentially integrated by AVP and VIP signaling, depending on the postnatal period. PMID:27626074

  1. Simulation of Wireless Digital Communication Systems

    Directory of Open Access Journals (Sweden)

    A. Mohammed

    2004-12-01

    Full Text Available Due to the explosive demands for high speed wireless services, suchas wireless Internet, email and cellular video conferencing, digitalwireless communications has become one of the most exciting researchtopics in electrical and electronic engineering field. The never-endingdemand for such personal and multimedia services, however, demandstechnologies operating at higher data rates and broader bandwidths. Inaddition, the complexity of wireless communication and signalprocessing systems has grown considerably during the past decade.Therefore, powerful computer­aided techniques are required for theprocess of modeling, designing, analyzing and evaluating theperformance of digital wireless communication systems. In this paper wediscuss the basic propagation mechanisms affecting the performance ofwireless communication systems, and present a simple, powerful andefficient way to simulate digital wireless communication systems usingMatlab. The simulated results are compared with the theoreticalanalysis to validate the simulator. The simulator is useful inevaluating the performance of wireless multimedia services and theassociated signal processing structures and algorithms for current andnext generation wireless mobile communication systems.

  2. Experimental validation of wireless communication with chaos

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Hai-Peng; Bai, Chao; Liu, Jian [Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xian University of Technology, Xian 710048 (China); Baptista, Murilo S.; Grebogi, Celso [Institute for Complex System and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen AB24 3UE (United Kingdom)

    2016-08-15

    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.

  3. Experimental validation of wireless communication with chaos

    International Nuclear Information System (INIS)

    Ren, Hai-Peng; Bai, Chao; Liu, Jian; Baptista, Murilo S.; Grebogi, Celso

    2016-01-01

    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.

  4. 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.

  5. Listening to Brain Microcircuits for Interfacing With External World-Progress in Wireless Implantable Microelectronic Neuroengineering Devices: Experimental systems are described for electrical recording in the brain using multiple microelectrodes and short range implantable or wearable broadcasting units.

    Science.gov (United States)

    Nurmikko, Arto V; Donoghue, John P; Hochberg, Leigh R; Patterson, William R; Song, Yoon-Kyu; Bull, Christopher W; Borton, David A; Laiwalla, Farah; Park, Sunmee; Ming, Yin; Aceros, Juan

    2010-01-01

    Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature's amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic "brain-interfaces" within the body, a point of special emphasis of this paper.

  6. 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.

  7. A wireless acoustic emission sensor remotely powered by light

    International Nuclear Information System (INIS)

    Zahedi, F; Huang, H

    2014-01-01

    In this paper, wireless sensing of acoustic emission (AE) signals using a battery-free sensor node remotely powered by light is presented. The wireless sensor consists of a piezoelectric wafer active sensor (PWAS) for AE signal acquisition and a wireless transponder that performs signal conditioning, frequency conversion, and wireless transmission. For signal conditioning, a voltage follower that consumes less than 2 mW was introduced to buffer the high impedance of the PWAS from the low impedance of the wireless transponder. A photocell-based energy harvester with a stable voltage output was developed to power the voltage follower so that the wireless AE sensor can operate without an external power source. The principle of operation of the battery-free wireless AE sensor node and the sensor interrogation system is described, followed by a detailed description of the hardware implementation. The voltage follower and the wireless channel were characterized by ultrasound pitch–catch and pencil lead break experiments. (paper)

  8. Energy Efficient Error-Correcting Coding for Wireless Systems

    NARCIS (Netherlands)

    Shao, X.

    2010-01-01

    The wireless channel is a hostile environment. The transmitted signal does not only suffers multi-path fading but also noise and interference from other users of the wireless channel. That causes unreliable communications. To achieve high-quality communications, error correcting coding is required

  9. Invisible Bridges: Wireless Technology Links Minds over Space and Time

    Science.gov (United States)

    Lambert, Lori

    2004-01-01

    Eight years after Chief Sitting Bull, prophetic chief of the Great Sioux Nation, was assassinated in 1890, Guglielmo Marconi transmitted the first wireless telegraph signals across the Atlantic to England. Although these two events seem unrelated, the names of these two men of vision are linked together today by Marconi's wireless invention. Data,…

  10. Wireless digital information transfer : Modelling, prediction and assessment

    NARCIS (Netherlands)

    Lager, I.E.; De Hoop, A.T.; Kikkawa, T.

    2013-01-01

    The loop-to-loop pulsed electromagnetic field wireless signal transfer is investigated with a view on its application in wireless digital information transfer. Closed-form expressions are derived for the emitted magnetic field and for the open-circuit voltage of the receiving loop in dependence on

  11. 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...

  12. 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...

  13. 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...

  14. Wireless communication technology NFC

    OpenAIRE

    MÁROVÁ, Kateřina

    2014-01-01

    Aim of this bachelor thesis is to handle the issue of new wireless communication technology NFC (Near Field Communication) including a comparison of advantages and disadvantages of NFC with other wireless technologies (Bluetooth, Wi-Fi, etc.). NFC is a technology for wireless communications between different electronic devices, one of which is typically a mobile phone. Near Field Communication allows wireless communication at very short distance by approaching or enclosing two devices and can...

  15. Wireless steganography

    Science.gov (United States)

    Agaian, Sos S.; Akopian, David; D'Souza, Sunil

    2006-02-01

    Modern mobile devices are some of the most technologically advanced devices that people use on a daily basis and the current trends in mobile phone technology indicate that tasks achievable by mobile devices will soon exceed our imagination. This paper undertakes a case study of the development and implementation of one of the first known steganography (data hiding) applications on a mobile device. Steganography is traditionally accomplished using the high processing speeds of desktop or notebook computers. With the introduction of mobile platform operating systems, there arises an opportunity for the users to develop and embed their own applications. We take advantage of this opportunity with the introduction of wireless steganographic algorithms. Thus we demonstrates that custom applications, popular with security establishments, can be developed also on mobile systems independent of both the mobile device manufacturer and mobile service provider. For example, this might be a very important feature if the communication is to be controlled exclusively by authorized personnel. The paper begins by reviewing the technological capabilities of modern mobile devices. Then we address a suitable development platform which is based on Symbian TM/Series60 TM architecture. Finally, two data hiding applications developed for Symbian TM/Series60 TM mobile phones are presented.

  16. The Prospects of Ultra-Broadband THz Wireless Communications

    DEFF Research Database (Denmark)

    Yu, Xianbin; Chen, Ying; Galili, Michael

    2014-01-01

    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...... 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......, in terms of ultrafast THz generation/THz detection and link power budget....

  17. Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models

    Science.gov (United States)

    Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals...

  18. Wireless Communication Technologies

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Wireless Communication Technologies. Since 1999, the wireless LAN has experienced a tremendous growth. Reasons: Adoption of industry standards. Interoperability testing. The progress of wireless equipments to higher data rates. Rapid decrease in product ...

  19. 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.

  20. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

    Directory of Open Access Journals (Sweden)

    Jiann-Shing Shieh

    2013-08-01

    Full Text Available EEG (Electroencephalography signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module. Multivariate empirical mode decomposition (MEMD algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3 is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC curve (AUC of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

  1. Conceptual Considerations for Reducing the Computational Complexity in Software Defined Radio using Cooperative Wireless Networks

    DEFF Research Database (Denmark)

    Kristensen, Jesper Michael; Fitzek, Frank H. P.; Koch, Peter

    2005-01-01

    the expected increase in complexity leading to a decrease in energy efficiency, cooperative wireless networks are introduced. Cooperative wireless networks enables the concept of resource sharing. Resource sharing is interpreted as collaborative signal processing. This interpretation leads to the concept...... of a distributed signal processor. OFDM and the principle of FFT is described as an example of collaborative signal processing....

  2. The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component

    International Nuclear Information System (INIS)

    Marseguerra, M.; Zoia, A.

    2005-01-01

    In this paper, Robust AutoAssociative Neural Networks (RAANN) are applied to a series of signals produced by the Halden simulator of the 1200MWe BWR Forsmark-3 plant in Sweden. The applications concern: - correction of drifts and gross errors in sensors, for diagnostic and control purposes, - cluster analysis, to individuate a failed component and the intensity of the failure, - forecasting system signals, for safety or economic purposes, - reconstruction of unmeasured signals (virtual sensors). In the attainment of the above results, the geometric interpretation of the mapping performed by the network, propounded in Part I of this work, has provided a reasoned choice of the most critical free parameter, i.e., the number f of nodes of the bottleneck layer, thus allowing a deep understanding of the network functioning and also avoiding the traditional and troubling procedure of selection by trial-and-error. The theoretical basis of this analysis, discussed in details in the companion paper, is founded on the idea of dimension and in particular of fractal dimension, which has been used as a numerical estimator of f

  3. A sub-nJ CMOS ECG classifier for wireless smart sensor.

    Science.gov (United States)

    Chollet, Paul; Pallas, Remi; Lahuec, Cyril; Arzel, Matthieu; Seguin, Fabrice

    2017-07-01

    Body area sensor networks hold the promise of more efficient and cheaper medical care services through the constant monitoring of physiological markers such as heart beats. Continuously transmitting the electrocardiogram (ECG) signal requires most of the wireless ECG sensor energy budget. This paper presents the analog implantation of a classifier for ECG signals that can be embedded onto a sensor. The classifier is a sparse neural associative memory. It is implemented using the ST 65 nm CMOS technology and requires only 234 pJ per classification while achieving a 93.6% classification accuracy. The energy requirement is 6 orders of magnitude lower than a digital accelerator that performs a similar task. The lifespan of the resulting sensor is 191 times as large as that of a sensor sending all the data.

  4. Wireless Sensor Network for Medical Applications

    OpenAIRE

    Hanady S.Ahmed; Abduladhem A. Ali

    2015-01-01

    This work presents a healthcare monitoring system that can be used in an intensive care room. Biological information represented by ECG signals is achieved by ECG acquisition part . AD620 Instrumentation Amplifier selected due to its low current noise. The ECG signals of patients in the intensive care room are measured through wireless nodes. A base node is connected to the nursing room computer via a USB port , and is programmed with a specific firmware. The ECG signals are trans...

  5. Statistical mechanics of stochastic neural networks: Relationship between the self-consistent signal-to-noise analysis, Thouless-Anderson-Palmer equation, and replica symmetric calculation approaches

    International Nuclear Information System (INIS)

    Shiino, Masatoshi; Yamana, Michiko

    2004-01-01

    We study the statistical mechanical aspects of stochastic analog neural network models for associative memory with correlation type learning. We take three approaches to derive the set of the order parameter equations for investigating statistical properties of retrieval states: the self-consistent signal-to-noise analysis (SCSNA), the Thouless-Anderson-Palmer (TAP) equation, and the replica symmetric calculation. On the basis of the cavity method the SCSNA can be generalized to deal with stochastic networks. We establish the close connection between the TAP equation and the SCSNA to elucidate the relationship between the Onsager reaction term of the TAP equation and the output proportional term of the SCSNA that appear in the expressions for the local fields

  6. 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

  7. The effect of pulsed electric fields on the electrotactic migration of human neural progenitor cells through the involvement of intracellular calcium signaling.

    Science.gov (United States)

    Hayashi, Hisamitsu; Edin, Fredrik; Li, Hao; Liu, Wei; Rask-Andersen, Helge

    2016-12-01

    Endogenous electric fields (EFs) are required for the physiological control of the central nervous system development. Application of the direct current EFs to neural stem cells has been studied for the possibility of stem cell transplantation as one of the therapies for brain injury. EFs generated within the nervous system are often associated with action potentials and synaptic activity, apparently resulting in a pulsed current in nature. The aim of this study is to investigate the effect of pulsed EF, which can reduce the cytotoxicity, on the migration of human neural progenitor cells (hNPCs). We applied the mono-directional pulsed EF with a strength of 250mV/mm to hNPCs for 6h. The migration distance of the hNPCs exposed to pulsed EF was significantly greater compared with the control not exposed to the EF. Pulsed EFs, however, had less of an effect on the migration of the differentiated hNPCs. There was no significant change in the survival of hNPCs after exposure to the pulsed EF. To investigate the role of Ca 2+ signaling in electrotactic migration of hNPCs, pharmacological inhibition of Ca 2+ channels in the EF-exposed cells revealed that the electrotactic migration of hNPCs exposed to Ca 2+ channel blockers was significantly lower compared to the control group. The findings suggest that the pulsed EF induced migration of hNPCs is partly influenced by intracellular Ca 2+ signaling. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Investigation of interference in multiple-input multiple-output wireless transmission at W band for an optical wireless integration system.

    Science.gov (United States)

    Li, Xinying; Yu, Jianjun; Dong, Ze; Zhang, Junwen; Chi, Nan; Yu, Jianguo

    2013-03-01

    We experimentally investigate the interference in multiple-input multiple-output (MIMO) wireless transmission by adjusting the relative locations of horn antennas (HAs) in a 100 GHz optical wireless integration system, which can deliver a 50 Gb/s polarization-division-multiplexing quadrature-phase-shift-keying signal over 80 km single-mode fiber-28 and a 2×2 MIMO wireless link. For the parallel 2×2 MIMO wireless link, each receiver HA can only get wireless power from the corresponding transmitter HA, while for the crossover ones, the receiver HA can get wireless power from two transmitter HAs. At the wireless receiver, polarization demultiplexing is realized by the constant modulus algorithm (CMA) in the digital-signal-processing part. Compared to the parallel case, wireless interference causes about 2 dB optical signal-to-noise ratio penalty at a bit-error ratio (BER) of 3.8×10(-3) for the crossover cases if similar CMA taps are employed. The increase in CMA tap length can reduce wireless interference and improve BER performance. Furthermore, more CMA taps should be adopted to overcome the severe wireless interference when two pairs of transmitter and receiver HAs have different wireless distances.

  9. Self-consistent signal-to-noise analysis of the statistical behavior of analog neural networks and enhancement of the storage capacity

    Science.gov (United States)

    Shiino, Masatoshi; Fukai, Tomoki

    1993-08-01

    Based on the self-consistent signal-to-noise analysis (SCSNA) capable of dealing with analog neural networks with a wide class of transfer functions, enhancement of the storage capacity of associative memory and the related statistical properties of neural networks are studied for random memory patterns. Two types of transfer functions with the threshold parameter θ are considered, which are derived from the sigmoidal one to represent the output of three-state neurons. Neural networks having a monotonically increasing transfer function FM, FM(u)=sgnu (||u||>θ), FM(u)=0 (||u||memory patterns), implying the reduction of the number of spurious states. The behavior of the storage capacity with changing θ is qualitatively the same as that of the Ising spin neural networks with varying temperature. On the other hand, the nonmonotonic transfer function FNM, FNM(u)=sgnu (||u||=θ) gives rise to remarkable features in several respects. First, it yields a large enhancement of the storage capacity compared with the Amit-Gutfreund-Sompolinsky (AGS) value: with decreasing θ from θ=∞, the storage capacity αc of such a network is increased from the AGS value (~=0.14) to attain its maximum value of ~=0.42 at θ~=0.7 and afterwards is decreased to vanish at θ=0. Whereas for θ>~1 the storage capacity αc coincides with the value αc~ determined by the SCSNA as the upper bound of α ensuring the existence of retrieval solutions, for θr≠0 (i.e., finite width of the local field distribution), which is implied by the order-parameter equations of the SCSNA, disappears at a certain critical loading rate α0, and for αr=0+). As a consequence, memory retrieval without errors becomes possible even in the saturation limit α≠0. Results of the computer simulations on the statistical properties of the novel phase with αstorage capacity is also analyzed for the two types of networks. It is conspicuous for the networks with FNM, where the self-couplings increase the stability of

  10. 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.

  11. Biomedical Wireless Ambulatory Crew Monitor

    Science.gov (United States)

    Chmiel, Alan; Humphreys, Brad

    2009-01-01

    A compact, ambulatory biometric data acquisition system has been developed for space and commercial terrestrial use. BioWATCH (Bio medical Wireless and Ambulatory Telemetry for Crew Health) acquires signals from biomedical sensors using acquisition modules attached to a common data and power bus. Several slots allow the user to configure the unit by inserting sensor-specific modules. The data are then sent real-time from the unit over any commercially implemented wireless network including 802.11b/g, WCDMA, 3G. This system has a distributed computing hierarchy and has a common data controller on each sensor module. This allows for the modularity of the device along with the tailored ability to control the cards using a relatively small master processor. The distributed nature of this system affords the modularity, size, and power consumption that betters the current state of the art in medical ambulatory data acquisition. A new company was created to market this technology.

  12. Manipulating Wnt signaling at different subcellular levels affects the fate of neonatal neural stem/progenitor cells

    Czech Academy of Sciences Publication Activity Database

    Kriška, Ján; Honsa, Pavel; Džamba, Dávid; Butenko, Olena; Koleničová, Denisa; Janečková, Lucie; Nahácka, Z.; Anděra, L.; Kozmík, V.; Taketo, M.M.; Kořínek, Vladimír; Anděrová, Miroslava

    2016-01-01

    Roč. 1641, nov. (2016), s. 73-87 ISSN 1872-6240 R&D Projects: GA ČR(CZ) GBP304/12/G069 Institutional support: RVO:68378041 Keywords : beta-catenin signaling * neonatal mouse * neurogenesis * gliogenesis Subject RIV: ED - Physiology

  13. Manipulating Wnt signaling at different subcellular levels affects the fate of neonatal neural stem/progenitor cells

    Czech Academy of Sciences Publication Activity Database

    Kriska, J.; Honsa, P.; Dzamba, D.; Butenko, O.; Kolenicova, D.; Janečková, Lucie; Nahácka, Zuzana; Anděra, Ladislav; Kozmik, Zbyněk; Taketo, M.M.; Kořínek, Vladimír; Anderova, M.

    2016-01-01

    Roč. 1651, podzim (2016), s. 73-87 ISSN 0006-8993 R&D Projects: GA ČR(CZ) GBP304/12/G069 Institutional support: RVO:68378050 Keywords : beta-catenin signaling * neonatal mouse * neurogenesis * gliogenesis * patch-clamp technique * lon channel Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.746, year: 2016

  14. Applying Fuzzy Artificial Neural Network OSPF to develop Smart ...

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Fuzzy Artificial Neural Network to create Smart Routing. Protocol Algorithm. ... manufactured mental aptitude strategy. The capacity to study .... Based Energy Efficiency in Wireless Sensor Networks: A Survey",. International ...

  15. Multigigabit W-Band (75–110 GHz) Bidirectional Hybrid Fiber-Wireless Systems in Access Networks

    DEFF Research Database (Denmark)

    Pang, Xiaodan; Lebedev, Alexander; Vegas Olmos, Juan José

    2014-01-01

    compare the transmission performances in terms of achievable wireless distances with and without using a high-frequency electrical power amplifier at the wireless transmitter. A downlink 16-Gbit/s QPSK signal and an uplink 1.25-Gbit/s ASK signal transmission over the two implementations are experimentally......We experimentally demonstrate multigigabit capacity bidirectional hybrid fiber-wireless systems with RF carrier frequencies at the W-band (75-110 GHz) that enables the seamless convergence between wireless and fiber-optic data transmission systems in access networks. In this study, we evaluate...... the transmission performances in two scenarios: a fiber-wireless access link that directly provide high-speed connections to wireless end users, and a fiber-wireless-fiber signal relay where a high capacity wireless link can be used to bridge two access fiber spans over physical obstacles. In both scenarios, we...

  16. Toward multi-area distributed network of implanted neural interrogators

    Science.gov (United States)

    Powell, Marc P.; Hou, Xiaoxiao; Galligan, Craig; Ashe, Jeffrey; Borton, David A.

    2017-08-01

    As we aim to improve our understanding of the brain, it is critical that researchers have simultaneous multi-area, large-scale access to the brain. Information processing in the brain occurs through close and distant coupling of functional sub-domains, as opposed to within isolated single neurons. However, commercially available neural interfaces capable of sensing electrophysiology of single neurons, currently allow access to only a small, mm3 volume of cortical cells, are not scalable to recording from orders of magnitude more neurons, and leverage bulky, skull mounted hardware and cabling sensitive to relative movements of the skull and brain. In this work, we propose a system capable of recording from many individual distributed neural interrogator nodes, untethered from any external electronics. Using an array of epidural inductive coils to wirelessly power the implanted electronics, the system is intended to be agnostic to the surgical placement of any individual node. Here, we demonstrate the ability to transmit nearly 15mW of power with greater than 50% power transfer efficiency, benchtop testing of individual subcircuit system components showing successful digitization of neural signals, and wireless transmission currently supporting a data rate of 3.84Mbps. We leverage a software defined radio based RF receiver to demodulate the data which can be stored in memory for later retrieval. Finally, we introduce a packaging technology capable of isolating active electronics from the surrounding tissue while providing capability for electrical feed-through assemblies for external neural interfacing. We expect, based on the presented preliminary findings, that the system can be integrated into a platform technology for the study of the intricate interactions between cortical domains.

  17. Space-division-multiplexed transmission of 3x3 multiple-input multiple-output wireless signals over conventional graded-index multimode fiber

    NARCIS (Netherlands)

    Lei, Yi; Li, Jianqiang; Fan, Yuting; Yu, Dawei; Fu, Songnian; Yin, Feifei; Dai, Yitang; Xu, Kun

    2016-01-01

    In this paper, we experimentally demonstrate space-division-multiplexed (SDM) transmission of IEEE 802.11ac-compliant 3-spatial-stream WLAN signals over 3 spatial modes of conventional 50um graded-index (GI) multimode fiber (MMF) employing non-mode-selective 3D-waveguide photonic lantern. Two kinds

  18. Wireless mesh networks.

    Science.gov (United States)

    Wang, Xinheng

    2008-01-01

    Wireless telemedicine using GSM and GPRS technologies can only provide low bandwidth connections, which makes it difficult to transmit images and video. Satellite or 3G wireless transmission provides greater bandwidth, but the running costs are high. Wireless networks (WLANs) appear promising, since they can supply high bandwidth at low cost. However, the WLAN technology has limitations, such as coverage. A new wireless networking technology named the wireless mesh network (WMN) overcomes some of the limitations of the WLAN. A WMN combines the characteristics of both a WLAN and ad hoc networks, thus forming an intelligent, large scale and broadband wireless network. These features are attractive for telemedicine and telecare because of the ability to provide data, voice and video communications over a large area. One successful wireless telemedicine project which uses wireless mesh technology is the Emergency Room Link (ER-LINK) in Tucson, Arizona, USA. There are three key characteristics of a WMN: self-organization, including self-management and self-healing; dynamic changes in network topology; and scalability. What we may now see is a shift from mobile communication and satellite systems for wireless telemedicine to the use of wireless networks based on mesh technology, since the latter are very attractive in terms of cost, reliability and speed.

  19. Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task.

    Science.gov (United States)

    Goto, Nobuhiko; Mushtaq, Faisal; Shee, Dexter; Lim, Xue Li; Mortazavi, Matin; Watabe, Motoki; Schaefer, Alexandre

    2017-09-01

    We investigated whether well-known neural markers of selective attention to motivationally-relevant stimuli were modulated by variations in subjective preference towards consumer goods in a virtual shopping task. Specifically, participants viewed and rated pictures of various goods on the extent to which they wanted each item, which they could potentially purchase afterwards. Using the event-related potentials (ERP) method, we found that variations in subjective preferences for consumer goods strongly modulated positive slow waves (PSW) from 800 to 3000 milliseconds after stimulus onset. We also found that subjective preferences modulated the N200 and the late positive potential (LPP). In addition, we found that both PSW and LPP were modulated by subsequent buying decisions. Overall, these findings show that well-known brain event-related potentials reflecting selective attention processes can reliably index preferences to consumer goods in a shopping environment. Based on a large body of previous research, we suggest that early ERPs (e.g. the N200) to consumer goods could be indicative of preferences driven by unconditional and automatic processes, whereas later ERPs such as the LPP and the PSW could reflect preferences built upon more elaborative and conscious cognitive processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Wireless LAN security management with location detection capability in hospitals.

    Science.gov (United States)

    Tanaka, K; Atarashi, H; Yamaguchi, I; Watanabe, H; Yamamoto, R; Ohe, K

    2012-01-01

    In medical institutions, unauthorized access points and terminals obstruct the stable operation of a large-scale wireless local area network (LAN) system. By establishing a real-time monitoring method to detect such unauthorized wireless devices, we can improve the efficiency of security management. We detected unauthorized wireless devices by using a centralized wireless LAN system and a location detection system at 370 access points at the University of Tokyo Hospital. By storing the detected radio signal strength and location information in a database, we evaluated the risk level from the detection history. We also evaluated the location detection performance in our hospital ward using Wi-Fi tags. The presence of electric waves outside the hospital and those emitted from portable game machines with wireless communication capability was confirmed from the detection result. The location detection performance showed an error margin of approximately 4 m in detection accuracy and approximately 5% in false detection. Therefore, it was effective to consider the radio signal strength as both an index of likelihood at the detection location and an index for the level of risk. We determined the location of wireless devices with high accuracy by filtering the detection results on the basis of radio signal strength and detection history. Results of this study showed that it would be effective to use the developed location database containing radio signal strength and detection history for security management of wireless LAN systems and more general-purpose location detection applications.