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Sample records for neural interface system

  1. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    Science.gov (United States)

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-01-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

  2. High speed digital interfacing for a neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

    Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

  3. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  4. Implantable Neural Interfaces for Sharks

    Science.gov (United States)

    2007-05-01

    technology for recording and stimulating from the auditory and olfactory sensory nervous systems of the awake, swimming nurse shark , G. cirratum (Figures...overlay of the central nervous system of the nurse shark on a horizontal MR image. Implantable Neural Interfaces for Sharks ...Neural Interfaces for Characterizing Population Responses to Odorants and Electrical Stimuli in the Nurse Shark , Ginglymostoma cirratum.” AChemS Abs

  5. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  6. Efficient decoding with steady-state Kalman filter in neural interface systems.

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    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

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    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.

  12. 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.

  13. Analysis of neural activity in human motor cortex -- Towards brain machine interface system

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    Secundo, Lavi

    , the correlation of ECoG activity to kinematic parameters of arm movement is context-dependent, an important constraint to consider in future development of BMI systems. The third chapter delves into a fundamental organizational principle of the primate motor system---cortical control of contralateral limb movements. However, ipsilateral motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in ipsilateral primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. We also recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface.

  14. Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

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    Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro

    2011-08-01

    In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.

  15. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  16. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

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

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

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

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    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.

  18. Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics.

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    Szostak, Katarzyna M; Grand, Laszlo; Constandinou, Timothy G

    2017-01-01

    Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes-microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  19. Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics

    Directory of Open Access Journals (Sweden)

    Katarzyna M. Szostak

    2017-12-01

    Full Text Available Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes—microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  20. A review of organic and inorganic biomaterials for neural interfaces.

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    Fattahi, Pouria; Yang, Guang; Kim, Gloria; Abidian, Mohammad Reza

    2014-03-26

    Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.

  1. Intelligent Tutoring Systems: Formalization as Automata and Interface Design Using Neural Networks

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    Curilem, S. Gloria; Barbosa, Andrea R.; de Azevedo, Fernando M.

    2007-01-01

    This article proposes a mathematical model of Intelligent Tutoring Systems (ITS), based on observations of the behaviour of these systems. One of the most important problems of pedagogical software is to establish a common language between the knowledge areas involved in their development, basically pedagogical, computing and domain areas. A…

  2. Flexible neural interfaces with integrated stiffening shank

    Energy Technology Data Exchange (ETDEWEB)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2017-10-17

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  3. Flexible neural interfaces with integrated stiffening shank

    Science.gov (United States)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2016-07-26

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  4. Time to address the problems at the neural interface

    Science.gov (United States)

    Durand, Dominique M.; Ghovanloo, Maysam; Krames, Elliot

    2014-04-01

    Neural engineers have made significant, if not remarkable, progress in interfacing with the nervous system in the last ten years. In particular, neuromodulation of the brain has generated significant therapeutic benefits [1-5]. EEG electrodes can be used to communicate with patients with locked-in syndrome [6]. In the central nervous system (CNS), electrode arrays placed directly over or within the cortex can record neural signals related to the intent of the subject or patient [7, 8]. A similar technology has allowed paralyzed patients to control an otherwise normal skeletal system with brain signals [9, 10]. This technology has significant potential to restore function in these and other patients with neural disorders such as stroke [11]. Although there are several multichannel arrays described in the literature, the workhorse for these cortical interfaces has been the Utah array [12]. This 100-channel electrode array has been used in most studies on animals and humans since the 1990s and is commercially available. This array and other similar microelectrode arrays can record neural signals with high quality (high signal-to-noise ratio), but these signals fade and disappear after a few months and therefore the current technology is not reliable for extended periods of time. Therefore, despite these major advances in communicating with the brain, clinical translation cannot be implemented. The reasons for this failure are not known but clearly involve the interface between the electrode and the neural tissue. The Defense Advanced Research Project Agency (DARPA) as well as other federal funding agencies such as the National Science Foundation (NSF) and the National Institutes of Health have provided significant financial support to investigate this problem without much success. A recent funding program from DARPA was designed to establish the failure modes in order to generate a reliable neural interface technology and again was unsuccessful at producing a robust

  5. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  6. A 1microW 85nV/ radicalHz pseudo open-loop preamplifier with programmable band-pass filter for neural interface system.

    Science.gov (United States)

    Chang, Sun-Il; Yoon, Euisik

    2009-01-01

    We report an energy efficient pseudo open-loop amplifier with programmable band-pass filter developed for neural interface systems. The proposed amplifier consumes 400nA at 2.5V power supply. The measured thermal noise level is 85nV/ radicalHz and input-referred noise is 1.69microV(rms) from 0.3Hz to 1 kHz. The amplifier has a noise efficiency factor of 2.43, the lowest in the differential topologies reported up to date to our knowledge. By programming the switched-capacitor frequency and bias current, we could control the bandwidth of the preamplifier from 138 mHz to 2.2 kHz to meet various application requirements. The entire preamplifier including band-pass filters has been realized in a small area of 0.043mm(2) using a 0.25microm CMOS technology.

  7. Icinga Monitoring System Interface

    CERN Document Server

    Neculae, Alina Georgiana

    2014-01-01

    The aim of this project is to develop a web interface that would be used by the Icinga monitoring system to manage the CMS online cluster, in the experimental site. The interface would allow users to visualize the information in a compressed and intuitive way, as well as modify the information of each individual object and edit the relationships between classes.

  8. Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

    Science.gov (United States)

    Kim, Tae Gyo

    Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord

  9. The Pursuit of Chronically Reliable Neural Interfaces: A Materials Perspective.

    Science.gov (United States)

    Guo, Liang

    2016-01-01

    Brain-computer interfaces represent one of the most astonishing technologies in our era. However, the grand challenge of chronic instability and limited throughput of the electrode-tissue interface has significantly hindered the further development and ultimate deployment of such exciting technologies. A multidisciplinary research workforce has been called upon to respond to this engineering need. In this paper, I briefly review this multidisciplinary pursuit of chronically reliable neural interfaces from a materials perspective by analyzing the problem, abstracting the engineering principles, and summarizing the corresponding engineering strategies. I further draw my future perspectives by extending the proposed engineering principles.

  10. EDITORIAL: Special issue containing contributions from the 39th Neural Interfaces Conference Special issue containing contributions from the 39th Neural Interfaces Conference

    Science.gov (United States)

    Weiland, James D.

    2011-07-01

    Implantable neural interfaces provide substantial benefits to individuals with neurological disorders. That was the unequivocal message delivered by speaker after speaker from the podium of the 39th Neural Interfaces Conference (NIC2010) held in Long Beach, California, in June 2010. Giving benefit to patients is the most important measure for any biomedical technology, and myriad presentations at NIC2010 made clear that implantable neurostimulation technology has achieved this goal. Cochlear implants allow deaf people to communicate through speech. Deep brain stimulators give back mobility and dexterity necessary for so many daily tasks that are often taken for granted. Chronic pain can be alleviated through spinal cord stimulation. Motor prosthesis systems have been demonstrated in humans, through both reanimation of paralyzed limbs and neural control of robotic arms. Earlier this year, a retinal prosthesis was approved for sale in Europe, providing some hope for the blind. In sum, current clinical implants have been tremendously beneficial for today's patients and experimental systems that will be translated to the clinic promise to expand the number of people helped through bioelectronic therapies. Yet there are significant opportunities for improvement. For sensory prostheses, patients report an artificial sensation, clearly different from the natural sensation they remember. Neuromodulation systems, such as deep brain stimulation and pain stimulators, often have side effects that are tolerated as long as the side effects are less impactful than the disease. The papers published in the special issue from NIC2010 reflect the maturing and expanding field of neural interfaces. Our field has moved past proof-of-principle demonstrations and is now focusing on proving the longevity required for clinical implementation of new devices, extending existing approaches to new diseases and improving current devices for better outcomes. Closed-loop neuromodulation is a

  11. Integration of active devices on smart polymers for neural interfaces

    Science.gov (United States)

    Avendano-Bolivar, Adrian Emmanuel

    The increasing ability to ever more precisely identify and measure neural interactions and other phenomena in the central and peripheral nervous systems is revolutionizing our understanding of the human body and brain. To facilitate further understanding, more sophisticated neural devices, perhaps using microelectronics processing, must be fabricated. Materials often used in these neural interfaces, while compatible with these fabrication processes, are not optimized for long-term use in the body and are often orders of magnitude stiffer than the tissue with which they interact. Using the smart polymer substrates described in this work, suitability for processing as well as chronic implantation is demonstrated. We explore how to integrate reliable circuitry onto these flexible, biocompatible substrates that can withstand the aggressive environment of the body. To increase the capabilities of these devices beyond individual channel sensing and stimulation, active electronics must also be included onto our systems. In order to add this functionality to these substrates and explore the limits of these devices, we developed a process to fabricate single organic thin film transistors with mobilities up to 0.4 cm2/Vs and threshold voltages close to 0V. A process for fabricating organic light emitting diodes on flexible substrates is also addressed. We have set a foundation and demonstrated initial feasibility for integrating multiple transistors onto thin-film flexible devices to create new applications, such as matrix addressable functionalized electrodes and organic light emitting diodes. A brief description on how to integrate waveguides for their use in optogenetics is addressed. We have built understanding about device constraints on mechanical, electrical and in vivo reliability and how various conditions affect the electronics' lifetime. We use a bi-layer gate dielectric using an inorganic material such as HfO 2 combined with organic Parylene-c. A study of

  12. Incorporating an optical waveguide into a neural interface

    Energy Technology Data Exchange (ETDEWEB)

    Tolosa, Vanessa; Delima, Terri L.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tooker, Angela C.

    2016-11-08

    An optical waveguide integrated into a multielectrode array (MEA) neural interface includes a device body, at least one electrode in the device body, at least one electrically conducting lead coupled to the at least one electrode, at least one optical channel in the device body, and waveguide material in the at least one optical channel. The fabrication of a neural interface device includes the steps of providing a device body, providing at least one electrode in the device body, providing at least one electrically conducting lead coupled to the at least one electrode, providing at least one optical channel in the device body, and providing a waveguide material in the at least one optical channel.

  13. Neural growth into a microchannel network: towards a regenerative neural interface

    NARCIS (Netherlands)

    Wieringa, P.A.; Wiertz, Remy; le Feber, Jakob; Rutten, Wim

    2009-01-01

    We propose and validated a design for a highly selective 'endcap' regenerative neural interface towards a neuroprosthesis. In vitro studies using rat cortical neurons determine if a branching microchannel structure can counter fasciculated growth and cause neurites to separte from one another,

  14. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

    Science.gov (United States)

    Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

  15. 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.

  16. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  17. ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

    Directory of Open Access Journals (Sweden)

    Ulises A Aregueta-Robles

    2014-05-01

    Full Text Available Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes.

  18. Modeling soft interface dominated systems

    NARCIS (Netherlands)

    Lamorgese, A.; Mauri, R.; Sagis, L.M.C.

    2017-01-01

    The two main continuum frameworks used for modeling the dynamics of soft multiphase systems are the Gibbs dividing surface model, and the diffuse interface model. In the former the interface is modeled as a two dimensional surface, and excess properties such as a surface density, or surface energy

  19. 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

  20. Fractal Interfaces for Stimulating and Recording Neural Implants

    Science.gov (United States)

    Watterson, William James

    From investigating movement in an insect to deciphering cognition in a human brain to treating Parkinson's disease, hearing loss, or even blindness, electronic implants are an essential tool for understanding the brain and treating neural diseases. Currently, the stimulating and recording resolution of these implants remains low. For instance, they can record all the neuron activity associated with movement in an insect, but are quite far from recording, at an individual neuron resolution, the large volumes of brain tissue associated with cognition. Likewise, there is remarkable success in the cochlear implant restoring hearing due to the relatively simple anatomy of the auditory nerves, but are failing to restore vision to the blind due to poor signal fidelity and transmission in stimulating the more complex anatomy of the visual nerves. The critically important research needed to improve the resolution of these implants is to optimize the neuron-electrode interface. This thesis explores geometrical and material modifications to both stimulating and recording electrodes which can improve the neuron-electrode interface. First, we introduce a fractal electrode geometry which radically improves the restored visual acuity achieved by retinal implants and leads to safe, long-term operation of the implant. Next, we demonstrate excellent neuron survival and neurite outgrowth on carbon nanotube electrodes, thus providing a safe biomaterial which forms a strong connection between the electrode and neurons. Additional preliminary evidence suggests carbon nanotubes patterned into a fractal geometry will provide further benefits in improving the electrode-neuron interface. Finally, we propose a novel implant based off field effect transistor technology which utilizes an interconnecting fractal network of semiconducting carbon nanotubes to record from thousands of neurons simutaneously at an individual neuron resolution. Taken together, these improvements have the potential to

  1. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  2. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  3. Delphi Interface Maintenance System -

    Data.gov (United States)

    Department of Transportation — DIMS is the primary financial information system for tracking federally funded highway projects. It tracks authorizations, obligations, apportionments, allocations,...

  4. Modality-Specific Axonal Regeneration: Towards selective regenerative neural interfaces

    Directory of Open Access Journals (Sweden)

    Parisa eLotfi

    2011-10-01

    Full Text Available Regenerative peripheral nerve interfaces have been proposed as viable alternatives for the natural control of robotic prosthetic devices. However, sensory and motor axons at the neural interface are of mixed submodality types, which difficult the specific recording from motor axons and the eliciting of precise sensory modalities through selective stimulation. Here we evaluated the possibility of using type-specific neurotrophins to preferentially entice the regeneration of defined axonal populations from transected peripheral nerves into separate compartments. Segregation of mixed sensory fibers from dorsal root ganglion neurons was evaluated in vitro by compartmentalized diffusion delivery of nerve growth factor (NGF and neurotrophin-3 (NT-3, to preferentially entice the growth of TrkA+ nociceptive and TrkC+ proprioceptive subsets of sensory neurons, respectively. The average axon length in the NGF channel increased 2.5 fold compared to that in saline or NT-3, whereas the number of branches increased 3 fold in the NT-3 channels. These results were confirmed using a 3-D Y-shaped in vitro assay showing that the arm containing NGF was able to entice a 5-fold increase in axonal length of unbranched fibers. To address if such segregation can be enticed in vivo, a Y-shaped tubing was used to allow regeneration of the transected adult rat sciatic nerve into separate compartments filled with either NFG or NT-3. A significant increase in the number of CGRP+ pain fibers were attracted towards the sural nerve, while N-52+ large diameter axons were observed in the tibial and NT-3 compartments. This study demonstrates the guided enrichment of sensory axons in specific regenerative chambers, and supports the notion that neurotrophic factors can be used to segregate sensory and perhaps motor axons in separate peripheral interfaces.

  5. NUHOMS transportation system interfaces

    International Nuclear Information System (INIS)

    McConaghy, W.J.; Lehnert, R.A.; Rasmussen, R.W.

    1989-01-01

    The NUHOMS system utilizes a reinforced concrete Horizontal Storage Module (HSM) to store spent nuclear fuel assemblies which are sealed in a Dry Shielded Canister (DSC). The DSC has an internal basket assembly designed to hold 24 PWR or 60 BWR spent fuel assemblies. The HSMs are constructed in interconnected arrays on the utilities reactor site with each HSM holding one DSC. The HSMs and DSCs are the principal components of the Independent Spent Fuel Storage Installation (ISFSI) for which plants are granted a 10CFR72 (1) license by the US Nuclear Regulatory Commission (NRC) for interim dry storage. A complete description of the NUHOMS system for dry storage of spent fuel and its operation is contained in the NRC approved NUHOMS Topical Report and related publications (2, 3, 4, 6)

  6. Control system oriented human interface

    International Nuclear Information System (INIS)

    Barale, P.; Jacobson, V.; Kilgore, R.; Rondeau, D.

    1976-11-01

    The on-line control system interface for magnet beam steering and focusing in the Bevalac is described. An Aydin model 5205B display generator was chosen. This display generator will allow the computer to completely rewrite a monitor screen in less than 50 ms and is also capable of controlling a color monitor

  7. In vitro verification of a 3-D regenerative neural interface design: examination of neurite growth and electrical properties within a bifurcating microchannel structure

    NARCIS (Netherlands)

    Wieringa, P.A.; Wiertz, Remy; de Weerd, Eddy L; Rutten, Wim

    2010-01-01

    Toward the development of neuroprosthesis, we propose a 3-D regenerative neural interface design for connecting with the peripheral nervous system. This approach relies on bifurcating microstructures to achieve defasciculated ingrowth patterns and, consequently, high selectivity. In vitro studies

  8. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  9. Modification of surface/neuron interfaces for neural cell-type specific responses: a review

    International Nuclear Information System (INIS)

    Chen, Cen; Kong, Xiangdong; Lee, In-Seop

    2016-01-01

    Surface/neuron interfaces have played an important role in neural repair including neural prostheses and tissue engineered scaffolds. This comprehensive literature review covers recent studies on the modification of surface/neuron interfaces. These interfaces are identified in cases both where the surfaces of substrates or scaffolds were in direct contact with cells and where the surfaces were modified to facilitate cell adhesion and controlling cell-type specific responses. Different sources of cells for neural repair are described, such as pheochromocytoma neuronal-like cell, neural stem cell (NSC), embryonic stem cell (ESC), mesenchymal stem cell (MSC) and induced pluripotent stem cell (iPS). Commonly modified methods are discussed including patterned surfaces at micro- or nano-scale, surface modification with conducting coatings, and functionalized surfaces with immobilized bioactive molecules. These approaches to control cell-type specific responses have enormous potential implications in neural repair. (paper)

  10. An ovine model of cerebral catheter venography for implantation of an endovascular neural interface.

    Science.gov (United States)

    Oxley, Thomas James; Opie, Nicholas Lachlan; Rind, Gil Simon; Liyanage, Kishan; John, Sam Emmanuel; Ronayne, Stephen; McDonald, Alan James; Dornom, Anthony; Lovell, Timothy John Haynes; Mitchell, Peter John; Bennett, Iwan; Bauquier, Sebastien; Warne, Leon Norris; Steward, Chris; Grayden, David Bruce; Desmond, Patricia; Davis, Stephen M; O'Brien, Terence John; May, Clive N

    2018-04-01

    OBJECTIVE Neural interface technology may enable the development of novel therapies to treat neurological conditions, including motor prostheses for spinal cord injury. Intracranial neural interfaces currently require a craniotomy to achieve implantation and may result in chronic tissue inflammation. Novel approaches are required that achieve less invasive implantation methods while maintaining high spatial resolution. An endovascular stent electrode array avoids direct brain trauma and is able to record electrocorticography in local cortical tissue from within the venous vasculature. The motor area in sheep runs in a parasagittal plane immediately adjacent to the superior sagittal sinus (SSS). The authors aimed to develop a sheep model of cerebral venography that would enable validation of an endovascular neural interface. METHODS Cerebral catheter venography was performed in 39 consecutive sheep. Contrast-enhanced MRI of the brain was performed on 13 animals. Multiple telescoping coaxial catheter systems were assessed to determine the largest wide-bore delivery catheter that could be delivered into the anterior SSS. Measurements of SSS diameter and distance from the motor area were taken. The location of the motor area was determined in relation to lateral and superior projections of digital subtraction venography images and confirmed on MRI. RESULTS The venous pathway from the common jugular vein (7.4 mm) to the anterior SSS (1.2 mm) was technically challenging to selectively catheterize. The SSS coursed immediately adjacent to the motor cortex (SSS. Attempted access with 5-Fr and 6-Fr delivery catheters was associated with longer procedure times and higher complication rates. A 4-Fr catheter (internal lumen diameter 1.1 mm) was successful in accessing the SSS in 100% of cases with no associated complications. Complications included procedure-related venous dissection in two major areas: the torcular herophili, and the anterior formation of the SSS. The

  11. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  12. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  13. EPICS system: system structure and user interface

    International Nuclear Information System (INIS)

    West, R.E.; Bartlett, J.F.; Bobbitt, J.S.; Lahey, T.E.; Kramper, B.J.; MacKinnon, B.A.

    1984-02-01

    This paper present the user's view of and the general organization of the EPICS control system at Fermilab. Various subsystems of the EPICS control system are discussed. These include the user command language, software protection, the device database, remote computer interfaces, and several application utilities. This paper is related to two other papers on EPICS: an overview paper and a detailed implementation paper

  14. UNIVERSAL INTERFACE TO MULTIPLE OPERATIONS SYSTEMS

    DEFF Research Database (Denmark)

    Sonnenwald, Diane H.

    1986-01-01

    Alternative ways to provide access to operations systems that maintain, test, and configure complex telephone networks are being explored. It is suggested that a universal interface that provides simultaneous access to multiple operations systems that execute in different hardware and software...... environments, can be provided by an architecture that is based on the separation of presentation issues from application issues and on a modular interface management system that consists of a virtual user interface, physical user interface, and interface agent. The interface functionality that is needed...

  15. Co-Design Method and Wafer-Level Packaging Technique of Thin-Film Flexible Antenna and Silicon CMOS Rectifier Chips for Wireless-Powered Neural Interface Systems

    Directory of Open Access Journals (Sweden)

    Kenji Okabe

    2015-12-01

    Full Text Available In this paper, a co-design method and a wafer-level packaging technique of a flexible antenna and a CMOS rectifier chip for use in a small-sized implantable system on the brain surface are proposed. The proposed co-design method optimizes the system architecture, and can help avoid the use of external matching components, resulting in the realization of a small-size system. In addition, the technique employed to assemble a silicon large-scale integration (LSI chip on the very thin parylene film (5 μm enables the integration of the rectifier circuits and the flexible antenna (rectenna. In the demonstration of wireless power transmission (WPT, the fabricated flexible rectenna achieved a maximum efficiency of 0.497% with a distance of 3 cm between antennas. In addition, WPT with radio waves allows a misalignment of 185% against antenna size, implying that the misalignment has a less effect on the WPT characteristics compared with electromagnetic induction.

  16. 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.

  17. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  18. Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

    Directory of Open Access Journals (Sweden)

    Yoko Saito

    Full Text Available Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET. We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song, sung lyrics on a single pitch (lyrics, and the sung syllable 'la' on original pitches (melody. The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition.

  19. Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

    Science.gov (United States)

    Saito, Yoko; Ishii, Kenji; Sakuma, Naoko; Kawasaki, Keiichi; Oda, Keiichi; Mizusawa, Hidehiro

    2012-01-01

    Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET). We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song), sung lyrics on a single pitch (lyrics), and the sung syllable 'la' on original pitches (melody). The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control) that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control) showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition.

  20. SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models.

    Science.gov (United States)

    Cope, A J; Richmond, P; James, S S; Gurney, K; Allerton, D J

    2017-01-01

    There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a variety of simulation engines and hardware platforms. Layered XML model specification formats solve many of these problems, however they are difficult to write and visualise without tools. Here we describe a new graphical software tool, SpineCreator, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming. We demonstrate the tool through the reproduction and visualisation of published models and show simulation results using code generation interfaced directly into SpineCreator. As a unique application for the graphical creation of neural networks, SpineCreator represents an important step forward for neuronal modelling.

  1. All-optical bidirectional neural interfacing using hybrid multiphoton holographic optogenetic stimulation.

    Science.gov (United States)

    Paluch-Siegler, Shir; Mayblum, Tom; Dana, Hod; Brosh, Inbar; Gefen, Inna; Shoham, Shy

    2015-07-01

    Our understanding of neural information processing could potentially be advanced by combining flexible three-dimensional (3-D) neuroimaging and stimulation. Recent developments in optogenetics suggest that neurophotonic approaches are in principle highly suited for noncontact stimulation of network activity patterns. In particular, two-photon holographic optical neural stimulation (2P-HONS) has emerged as a leading approach for multisite 3-D excitation, and combining it with temporal focusing (TF) further enables axially confined yet spatially extended light patterns. Here, we study key steps toward bidirectional cell-targeted 3-D interfacing by introducing and testing a hybrid new 2P-TF-HONS stimulation path for accurate parallel optogenetic excitation into a recently developed hybrid multiphoton 3-D imaging system. The system is shown to allow targeted all-optical probing of in vitro cortical networks expressing channelrhodopsin-2 using a regeneratively amplified femtosecond laser source tuned to 905 nm. These developments further advance a prospective new tool for studying and achieving distributed control over 3-D neuronal circuits both in vitro and in vivo.

  2. iSpike: a spiking neural interface for the iCub robot

    International Nuclear Information System (INIS)

    Gamez, D; Fidjeland, A K; Lazdins, E

    2012-01-01

    This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

  3. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  4. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    Directory of Open Access Journals (Sweden)

    A. Novellino

    2007-01-01

    Full Text Available One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA, to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

  5. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    Science.gov (United States)

    Novellino, A.; D'Angelo, P.; Cozzi, L.; Chiappalone, M.; Sanguineti, V.; Martinoia, S.

    2007-01-01

    One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses. PMID:18350128

  6. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  7. Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

    Science.gov (United States)

    Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng

    2018-05-01

    This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.

  8. Boron-doped nanocrystalline diamond electrodes for neural interfaces: in vivo biocompatibility evaluation

    Czech Academy of Sciences Publication Activity Database

    Alcaide, M.; Taylor, Andrew; Fjorback, M.; Zachar, V.; Pennisi, C.P.

    2016-01-01

    Roč. 10, Mar (2016), 1-9, č. článku 87. ISSN 1662-453X Institutional support: RVO:68378271 Keywords : nanocrystalline diamond * neuroprosthetic interfaces * neural electrodes * boron-doped diamond * titanium nitride * foreign body reaction Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 3.566, year: 2016

  9. 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...

  10. Human-system Interfaces for Automatic Systems

    Energy Technology Data Exchange (ETDEWEB)

    OHara, J.M.; Higgins,J. (BNL); Fleger, S.; Barnes V. (NRC)

    2010-11-07

    Automation is ubiquitous in modern complex systems, and commercial nuclear- power plants are no exception. Automation is applied to a wide range of functions including monitoring and detection, situation assessment, response planning, and response implementation. Automation has become a 'team player' supporting personnel in nearly all aspects of system operation. In light of its increasing use and importance in new- and future-plants, guidance is needed to conduct safety reviews of the operator's interface with automation. The objective of this research was to develop such guidance. We first characterized the important HFE aspects of automation, including six dimensions: levels, functions, processes, modes, flexibility, and reliability. Next, we reviewed literature on the effects of all of these aspects of automation on human performance, and on the design of human-system interfaces (HSIs). Then, we used this technical basis established from the literature to identify general principles for human-automation interaction and to develop review guidelines. The guidelines consist of the following seven topics: automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration. In addition, our study identified several topics for additional research.

  11. Design and manufacturing challenges of optogenetic neural interfaces: a review

    Science.gov (United States)

    Goncalves, S. B.; Ribeiro, J. F.; Silva, A. F.; Costa, R. M.; Correia, J. H.

    2017-08-01

    Optogenetics is a relatively new technology to achieve cell-type specific neuromodulation with millisecond-scale temporal precision. Optogenetic tools are being developed to address neuroscience challenges, and to improve the knowledge about brain networks, with the ultimate aim of catalyzing new treatments for brain disorders and diseases. To reach this ambitious goal the implementation of mature and reliable engineered tools is required. The success of optogenetics relies on optical tools that can deliver light into the neural tissue. Objective/Approach: Here, the design and manufacturing approaches available to the scientific community are reviewed, and current challenges to accomplish appropriate scalable, multimodal and wireless optical devices are discussed. Significance: Overall, this review aims at presenting a helpful guidance to the engineering and design of optical microsystems for optogenetic applications.

  12. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

    Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

  13. Concept of software interface for BCI systems

    Science.gov (United States)

    Svejda, Jaromir; Zak, Roman; Jasek, Roman

    2016-06-01

    Brain Computer Interface (BCI) technology is intended to control external system by brain activity. One of main part of such system is software interface, which carries about clear communication between brain and either computer or additional devices connected to computer. This paper is organized as follows. Firstly, current knowledge about human brain is briefly summarized to points out its complexity. Secondly, there is described a concept of BCI system, which is then used to build an architecture of proposed software interface. Finally, there are mentioned disadvantages of sensing technology discovered during sensing part of our research.

  14. A Low Noise Amplifier for Neural Spike Recording Interfaces

    Directory of Open Access Journals (Sweden)

    Jesus Ruiz-Amaya

    2015-09-01

    Full Text Available This paper presents a Low Noise Amplifier (LNA for neural spike recording applications. The proposed topology, based on a capacitive feedback network using a two-stage OTA, efficiently solves the triple trade-off between power, area and noise. Additionally, this work introduces a novel transistor-level synthesis methodology for LNAs tailored for the minimization of their noise efficiency factor under area and noise constraints. The proposed LNA has been implemented in a 130 nm CMOS technology and occupies 0.053 mm-sq. Experimental results show that the LNA offers a noise efficiency factor of 2.16 and an input referred noise of 3.8 μVrms for 1.2 V power supply. It provides a gain of 46 dB over a nominal bandwidth of 192 Hz–7.4 kHz and consumes 1.92 μW. The performance of the proposed LNA has been validated through in vivo experiments with animal models.

  15. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  16. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

    1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.

  17. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2015-05-01

    Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  18. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

    Science.gov (United States)

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

    Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  19. Communications interface for plant monitoring system

    International Nuclear Information System (INIS)

    Lee, K.L.; Morgan, F.A.

    1988-01-01

    This paper presents the communications interface for an intelligent color graphic system which PSE and G developed as part of a plant monitoring system. The intelligent graphic system is designed to off-load traditional host functions such as dynamic graphic updates, keyboard handling and alarm display. The distributed system's data and synchronization problems and their solutions are discussed

  20. Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Hossein Bashashati

    2017-07-01

    Full Text Available Classification of EEG signals in self-paced Brain Computer Interfaces (BCI is an extremely challenging task. The main difficulty stems from the fact that start time of a control task is not defined. Therefore it is imperative to exploit the characteristics of the EEG data to the extent possible. In sensory motor self-paced BCIs, while performing the mental task, the user’s brain goes through several well-defined internal state changes. Applying appropriate classifiers that can capture these state changes and exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an ensemble learning approach for self-paced BCIs. We use Bayesian optimization to train several different classifiers on different parts of the BCI hyper- parameter space. We call each of these classifiers Neural Network Conditional Random Field (NNCRF. NNCRF is a combination of a neural network and conditional random field (CRF. As in the standard CRF, NNCRF is able to model the correlation between adjacent EEG samples. However, NNCRF can also model the nonlinear dependencies between the input and the output, which makes it more powerful than the standard CRF. We compare the performance of our algorithm to those of three popular sequence labeling algorithms (Hidden Markov Models, Hidden Markov Support Vector Machines and CRF, and to two classical classifiers (Logistic Regression and Support Vector Machines. The classifiers are compared for the two cases: when the ensemble learning approach is not used and when it is. The data used in our studies are those from the BCI competition IV and the SM2 dataset. We show that our algorithm is considerably superior to the other approaches in terms of the Area Under the Curve (AUC of the BCI system.

  1. Tracking Single Units in Chronic, Large Scale, Neural Recordings for Brain Machine Interface Applications

    Directory of Open Access Journals (Sweden)

    Ahmed eEleryan

    2014-07-01

    Full Text Available In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs. Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 seconds processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions.

  2. 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.

  3. Development of Baby-EBM Interface System

    International Nuclear Information System (INIS)

    Mukhlis Mokhtar; Abu Bakar Ghazali; Muhammad Zahidee Taat

    2010-01-01

    This paper explains the works being done to develop an interface system for Baby-Electron Beam Machine (EBM). The function of the system is for the safety, controlling and monitoring the Baby-EBM. The integration for the system is using data acquisition (DAQ) hardware and LabVIEW to develop the software. (author)

  4. Development of Baby-EBM Interface System

    Energy Technology Data Exchange (ETDEWEB)

    Mokhtar, Mukhlis; Ghazali, Abu Bakar; Taat, Muhammad Zahidee [Accelerator Development Center, Malaysian Nuclear Agency, Bangi, Kajang, Selangor (Malaysia), Technical Support Div.

    2010-07-01

    This paper explains the works being done to develop an interface system for Baby-Electron Beam Machine (EBM). The function of the system is for the safety, controlling and monitoring the Baby-EBM. The integration for the system is using data acquisition (DAQ) hardware and LabVIEW to develop the software. (author)

  5. Microchannel neural interface manufacture by stacking silicone and metal foil laminae

    Science.gov (United States)

    Lancashire, Henry T.; Vanhoestenberghe, Anne; Pendegrass, Catherine J.; Ajam, Yazan Al; Magee, Elliot; Donaldson, Nick; Blunn, Gordon W.

    2016-06-01

    Objective. Microchannel neural interfaces (MNIs) overcome problems with recording from peripheral nerves by amplifying signals independent of node of Ranvier position. Selective recording and stimulation using an MNI requires good insulation between microchannels and a high electrode density. We propose that stacking microchannel laminae will improve selectivity over single layer MNI designs due to the increase in electrode number and an improvement in microchannel sealing. Approach. This paper describes a manufacturing method for creating MNIs which overcomes limitations on electrode connectivity and microchannel sealing. Laser cut silicone—metal foil laminae were stacked using plasma bonding to create an array of microchannels containing tripolar electrodes. Electrodes were DC etched and electrode impedance and cyclic voltammetry were tested. Main results. MNIs with 100 μm and 200 μm diameter microchannels were manufactured. High electrode density MNIs are achievable with electrodes present in every microchannel. Electrode impedances of 27.2 ± 19.8 kΩ at 1 kHz were achieved. Following two months of implantation in Lewis rat sciatic nerve, micro-fascicles were observed regenerating through the MNI microchannels. Significance. Selective MNIs with the peripheral nervous system may allow upper limb amputees to control prostheses intuitively.

  6. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

    For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...

  7. 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.

  8. Evaluation of Explanation Interfaces in Recommender Systems

    Directory of Open Access Journals (Sweden)

    Sergio Cleger-Tamayo

    2017-05-01

    Full Text Available Explaining interfaces become a useful tool in systems that have a lot of content to evaluate by users. The different interfaces represent a help for the undecided users or those who consider systems as boxed black smart. These systems present recommendations to users based on different learning models. In this paper, we present the different objectives of the explanation interfaces and some of the criteria that you can evaluate, as well as a proposal of metrics to obtain results in the experiments. Finally, we showed the main results of a study with real users and their interaction with e-commerce systems. Among the main results, highlight the positive impact in relation to the time of interaction with the applications and acceptance of the recommendations received.

  9. Spectrometer user interface to computer systems

    International Nuclear Information System (INIS)

    Salmon, L.; Davies, M.; Fry, F.A.; Venn, J.B.

    1979-01-01

    A computer system for use in radiation spectrometry should be designed around the needs and comprehension of the user and his operating environment. To this end, the functions of the system should be built in a modular and independent fashion such that they can be joined to the back end of an appropriate user interface. The point that this interface should be designed rather than just allowed to evolve is illustrated by reference to four related computer systems of differing complexity and function. The physical user interfaces in all cases are keyboard terminals, and the virtues and otherwise of these devices are discussed and compared with others. The language interface needs to satisfy a number of requirements, often conflicting. Among these, simplicity and speed of operation compete with flexibility and scope. Both experienced and novice users need to be considered, and any individual's needs may vary from naive to complex. To be efficient and resilient, the implementation must use an operating system, but the user needs to be protected from its complex and unfamiliar syntax. At the same time the interface must allow the user access to all services appropriate to his needs. The user must also receive an image of privacy in a multi-user system. The interface itself must be stable and exhibit continuity between implementations. Some of these conflicting needs have been overcome by the SABRE interface with languages operating at several levels. The foundation is a simple semimnemonic command language that activates indididual and independent functions. The commands can be used with positional parameters or in an interactive dialogue the precise nature of which depends upon the operating environment and the user's experience. A command procedure or macrolanguage allows combinations of commands with conditional branching and arithmetic features. Thus complex but repetitive operations are easily performed

  10. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

    The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

  11. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease.

    Science.gov (United States)

    Swann, Nicole C; de Hemptinne, Coralie; Miocinovic, Svjetlana; Qasim, Salman; Ostrem, Jill L; Galifianakis, Nicholas B; Luciano, Marta San; Wang, Sarah S; Ziman, Nathan; Taylor, Robin; Starr, Philip A

    2018-02-01

    OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation

  12. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    Science.gov (United States)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  13. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.

    Science.gov (United States)

    Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

    2014-12-01

    To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like 'Face in a Crowd' task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the 'Crowd') using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a 'Crowd Off' condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.

  14. Flexible microelectrode array for interfacing with the surface of neural ganglia

    Science.gov (United States)

    Sperry, Zachariah J.; Na, Kyounghwan; Parizi, Saman S.; Chiel, Hillel J.; Seymour, John; Yoon, Euisik; Bruns, Tim M.

    2018-06-01

    Objective. The dorsal root ganglia (DRG) are promising nerve structures for sensory neural interfaces because they provide centralized access to primary afferent cell bodies and spinal reflex circuitry. In order to harness this potential, new electrode technologies are needed which take advantage of the unique properties of DRG, specifically the high density of neural cell bodies at the dorsal surface. Here we report initial in vivo results from the development of a flexible non-penetrating polyimide electrode array interfacing with the surface of ganglia. Approach. Multiple layouts of a 64-channel iridium electrode (420 µm2) array were tested, with pitch as small as 25 µm. The buccal ganglia of invertebrate sea slug Aplysia californica were used to develop handling and recording techniques with ganglionic surface electrode arrays (GSEAs). We also demonstrated the GSEA’s capability to record single- and multi-unit activity from feline lumbosacral DRG related to a variety of sensory inputs, including cutaneous brushing, joint flexion, and bladder pressure. Main results. We recorded action potentials from a variety of Aplysia neurons activated by nerve stimulation, and units were observed firing simultaneously on closely spaced electrode sites. We also recorded single- and multi-unit activity associated with sensory inputs from feline DRG. We utilized spatial oversampling of action potentials on closely-spaced electrode sites to estimate the location of neural sources at between 25 µm and 107 µm below the DRG surface. We also used the high spatial sampling to demonstrate a possible spatial sensory map of one feline’s DRG. We obtained activation of sensory fibers with low-amplitude stimulation through individual or groups of GSEA electrode sites. Significance. Overall, the GSEA has been shown to provide a variety of information types from ganglia neurons and to have significant potential as a tool for neural mapping and interfacing.

  15. Development and Evaluation of Micro-Electrocorticography Arrays for Neural Interfacing Applications

    Science.gov (United States)

    Schendel, Amelia Ann

    Neural interfaces have great promise for both electrophysiological research and therapeutic applications. Whether for the study of neural circuitry or for neural prosthetic or other therapeutic applications, micro-electrocorticography (micro-ECoG) arrays have proven extremely useful as neural interfacing devices. These devices strike a balance between invasiveness and signal resolution, an important step towards eventual human application. The objective of this research was to make design improvements to micro-ECoG devices to enhance both biocompatibility and device functionality. To best evaluate the effectiveness of these improvements, a cranial window imaging method for in vivo monitoring of the longitudinal tissue response post device implant was developed. Employment of this method provided valuable insight into the way tissue grows around micro-ECoG arrays after epidural implantation, spurring a study of the effects of substrate geometry on the meningeal tissue response. The results of the substrate footprint comparison suggest that a more open substrate geometry provides an easy path for the tissue to grow around to the top side of the device, whereas a solid device substrate encourages the tissue to thicken beneath the device, between the electrode sites and the brain. The formation of thick scar tissue between the recording electrode sites and the neural tissue is disadvantageous for long-term recorded signal quality, and thus future micro-ECoG device designs should incorporate open-architecture substrates for enhanced longitudinal in vivo function. In addition to investigating improvements for long-term device reliability, it was also desired to enhance the functionality of micro-ECoG devices for neural electrophysiology research applications. To achieve this goal, a completely transparent graphene-based device was fabricated for use with the cranial window imaging method and optogenetic techniques. The use of graphene as the conductive material provided

  16. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  17. Poly(3,4-ethylene dioxythiophene (PEDOT as a micro-neural interface material for electrostimulation

    Directory of Open Access Journals (Sweden)

    Seth J Wilks

    2009-06-01

    Full Text Available Chronic microstimulation-based devices are being investigated to treat conditions such as blindness, deafness, pain, paralysis and epilepsy. Small area electrodes are desired to achieve high selectivity. However, a major trade-off with electrode miniaturization is an increase in impedance and charge density requirements. Thus, the development of novel materials with lower interfacial impedance and enhanced charge storage capacity is essential for the development of micro-neural interface-based neuroprostheses. In this report, we study the use of conducting polymer poly(3,4-ethylene dioxythiophene (PEDOT as a neural interface material for microstimulation of small area iridium electrodes on silicon-substrate arrays. Characterized by electrochemical impedance spectroscopy, electrodeposition of PEDOT results in lower interfacial impedance at physiologically-relevant frequencies, with the 1kHz impedance magnitude being 23.3 ± 0.7 kΩ compared to 113.6 ± 3.5 kΩ for iridium oxide (IrOx on 177μm2 sites. Further, PEDOT exhibits enhanced charge storage capacity at 75.6 ± 5.4 mC/cm2 compared to 28.8 ± 0.3 mC/cm2 for IrOx, characterized by cyclic voltammetry (50 mV/s. These improvements at the electrode interface were corroborated by observation of the voltage excursions that result from constant current pulsing. The PEDOT coatings provide both a lower amplitude voltage and a more ohmic representation of the applied current compared to IrOx. During repetitive pulsing, PEDOT-coated electrodes show stable performance and little change in electrical properties, even at relatively high current densities which cause IrOx instability. These findings support the potential of PEDOT coatings as a micro-neural interface material for electrostimulation.

  18. Control Strategies for the DAB Based PV Interface System.

    Directory of Open Access Journals (Sweden)

    Hadi M El-Helw

    Full Text Available This paper presents an interface system based on the Dual Active Bridge (DAB converter for Photovoltaic (PV arrays. Two control strategies are proposed for the DAB converter to harvest the maximum power from the PV array. The first strategy is based on a simple PI controller to regulate the terminal PV voltage through the phase shift angle of the DAB converter. The Perturb and Observe (P&O Maximum Power Point Tracking (MPPT technique is utilized to set the reference of the PV terminal voltage. The second strategy presented in this paper employs the Artificial Neural Network (ANN to directly set the phase shift angle of the DAB converter that results in harvesting maximum power. This feed-forward strategy overcomes the stability issues of the feedback strategy. The proposed PV interface systems are modeled and simulated using MATLAB/SIMULINK and the EMTDC/PSCAD software packages. The simulation results reveal accurate and fast response of the proposed systems. The dynamic performance of the proposed feed-forward strategy outdoes that of the feedback strategy in terms of accuracy and response time. Moreover, an experimental prototype is built to test and validate the proposed PV interface system.

  19. ITER diagnostic system: Vacuum interface

    International Nuclear Information System (INIS)

    Patel, K.M.; Udintsev, V.S.; Hughes, S.; Walker, C.I.; Andrew, P.; Barnsley, R.; Bertalot, L.; Drevon, J.M.; Encheva, A.; Kashchuk, Y.; Maquet, Ph.; Pearce, R.; Taylor, N.; Vayakis, G.; Walsh, M.J.

    2013-01-01

    Diagnostics play an essential role for the successful operation of the ITER tokamak. They provide the means to observe control and to measure plasma during the operation of ITER tokamak. The components of the diagnostic system in the ITER tokamak will be installed in the vacuum vessel, in the cryostat, in the upper, equatorial and divertor ports, in the divertor cassettes and racks, as well as in various buildings. Diagnostic components that are placed in a high radiation environment are expected to operate for the life of ITER. There are approx. 45 diagnostic systems located on ITER. Some diagnostics incorporate direct or independently pumped extensions to maintain their necessary vacuum conditions. They require a base pressure less than 10 −7 Pa, irrespective of plasma operation, and a leak rate of less than 10 −10 Pa m 3 s −1 . In all the cases it is essential to maintain the ITER closed fuel cycle. These directly coupled diagnostic systems are an integral part of the ITER vacuum containment and are therefore subject to the same design requirements for tritium and active gas confinement, for all normal and accidental conditions. All the diagnostics, whether or not pumped, incorporate penetration of the vacuum boundary (i.e. window assembly, vacuum feedthrough etc.) and demountable joints. Monitored guard volumes are provided for all elements of the vacuum boundary that are judged to be vulnerable by virtue of their construction, material, load specification etc. Standard arrangements are made for their construction and for the monitoring, evacuating and leak testing of these volumes. Diagnostic systems are incorporated at more than 20 ports on ITER. This paper will describe typical and particular arrangements of pumped diagnostic and monitored guard volume. The status of the diagnostic vacuum systems, which are at the start of their detailed design, will be outlined and the specific features of the vacuum systems in ports and extensions will be described

  20. ITER diagnostic system: Vacuum interface

    Energy Technology Data Exchange (ETDEWEB)

    Patel, K.M., E-mail: Kaushal.Patel@iter.org [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France); Udintsev, V.S.; Hughes, S.; Walker, C.I.; Andrew, P.; Barnsley, R.; Bertalot, L. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France); Drevon, J.M. [Bertin Technologies, BP 22, 13762 Aix-en Provence cedex 3 (France); Encheva, A. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France); Kashchuk, Y. [Institution “PROJECT CENTER ITER”, 1, Akademika Kurchatova pl., Moscow (Russian Federation); Maquet, Ph. [Bertin Technologies, BP 22, 13762 Aix-en Provence cedex 3 (France); Pearce, R.; Taylor, N.; Vayakis, G.; Walsh, M.J. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France)

    2013-10-15

    Diagnostics play an essential role for the successful operation of the ITER tokamak. They provide the means to observe control and to measure plasma during the operation of ITER tokamak. The components of the diagnostic system in the ITER tokamak will be installed in the vacuum vessel, in the cryostat, in the upper, equatorial and divertor ports, in the divertor cassettes and racks, as well as in various buildings. Diagnostic components that are placed in a high radiation environment are expected to operate for the life of ITER. There are approx. 45 diagnostic systems located on ITER. Some diagnostics incorporate direct or independently pumped extensions to maintain their necessary vacuum conditions. They require a base pressure less than 10{sup −7} Pa, irrespective of plasma operation, and a leak rate of less than 10{sup −10} Pa m{sup 3} s{sup −1}. In all the cases it is essential to maintain the ITER closed fuel cycle. These directly coupled diagnostic systems are an integral part of the ITER vacuum containment and are therefore subject to the same design requirements for tritium and active gas confinement, for all normal and accidental conditions. All the diagnostics, whether or not pumped, incorporate penetration of the vacuum boundary (i.e. window assembly, vacuum feedthrough etc.) and demountable joints. Monitored guard volumes are provided for all elements of the vacuum boundary that are judged to be vulnerable by virtue of their construction, material, load specification etc. Standard arrangements are made for their construction and for the monitoring, evacuating and leak testing of these volumes. Diagnostic systems are incorporated at more than 20 ports on ITER. This paper will describe typical and particular arrangements of pumped diagnostic and monitored guard volume. The status of the diagnostic vacuum systems, which are at the start of their detailed design, will be outlined and the specific features of the vacuum systems in ports and extensions

  1. The 128-channel fully differential digital integrated neural recording and stimulation interface.

    Science.gov (United States)

    Shahrokhi, Farzaneh; Abdelhalim, Karim; Serletis, Demitre; Carlen, Peter L; Genov, Roman

    2010-06-01

    We present a fully differential 128-channel integrated neural interface. It consists of an array of 8 X 16 low-power low-noise signal-recording and generation circuits for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with and a fully differential 8-b column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The total measured power consumption of each recording channel, including the SAR ADC, is 15.5 ¿W. The measured input-referred noise is 6.08 ¿ Vrms over a 5-kHz bandwidth, resulting in a noise efficiency factor of 5.6. The stimulation channel performs monophasic or biphasic voltage-mode stimulation, with a maximum stimulation current of 5 mA and a quiescent power dissipation of 51.5 ¿W. The design is implemented in 0.35-¿m complementary metal-oxide semiconductor technology with the channel pitch of 200 ¿m for a total die size of 3.4 mm × 2.5 mm and a total power consumption of 9.33 mW. The neural interface was validated in in vitro recording of a low-Mg(2+)/high-K(+) epileptic seizure model in an intact hippocampus of a mouse.

  2. Augmenting intracortical brain-machine interface with neurally driven error detectors

    Science.gov (United States)

    Even-Chen, Nir; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2017-12-01

    Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

  3. Programmable neural processing on a smartdust for brain-computer interfaces.

    Science.gov (United States)

    Yuwen Sun; Shimeng Huang; Oresko, Joseph J; Cheng, Allen C

    2010-10-01

    Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.

  4. CMOS On-Chip Optoelectronic Neural Interface Device with Integrated Light Source for Optogenetics

    International Nuclear Information System (INIS)

    Sawadsaringkarn, Y; Kimura, H; Maezawa, Y; Nakajima, A; Kobayashi, T; Sasagawa, K; Noda, T; Tokuda, T; Ohta, J

    2012-01-01

    A novel optoelectronic neural interface device is proposed for target applications in optogenetics for neural science. The device consists of a light emitting diode (LED) array implemented on a CMOS image sensor for on-chip local light stimulation. In this study, we designed a suitable CMOS image sensor equipped with on-chip electrodes to drive the LEDs, and developed a device structure and packaging process for LED integration. The prototype device produced an illumination intensity of approximately 1 mW with a driving current of 2.0 mA, which is expected to be sufficient to activate channelrhodopsin (ChR2). We also demonstrated the functions of light stimulation and on-chip imaging using a brain slice from a mouse as a target sample.

  5. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Integrated low noise low power interface for neural bio-potentials recording and conditioning

    Science.gov (United States)

    Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

    2005-06-01

    The recent progress in both neurobiology and microelectronics suggests the creation of new, powerful tools to investigate the basic mechanisms of brain functionality. In particular, a lot of efforts are spent by scientific community to define new frameworks devoted to the analysis of in-vitro cultured neurons. One possible approach is recording their spiking activity to monitor the coordinated cellular behaviour and get insights about neural plasticity. Due to the nature of neurons action-potentials, when considering the design of an integrated microelectronic-based recording system, a number of problems arise. First, one would desire to have a high number of recording sites (i.e. several hundreds): this poses constraints on silicon area and power consumption. In this regard, our aim is to integrate-through on-chip post-processing techniques-hundreds of bio-compatible microsensors together with CMOS standard-process low-power (i.e. some tenths of uW per channel) conditioning electronics. Each recording channel is provided with sampling electronics to insure synchronous recording so that, for example, cross-correlation between signals coming from different sites can be performed. Extra-cellular potentials are in the range of [50-150] uV, so a comparison in terms of noise-efficiency was carried out among different architectures and very low-noise pre-amplification electronics (i.e. less than 5 uVrms) was designed. As spikes measurements are made with respect to the voltage of a reference electrode, we opted for an AC-coupled differential-input preamplifier provided with band-pass filtering capability. To achieve this, we implemented large time-constant (up to seconds) integrated components in the preamp feedback path. Thus, we got rid also of random slow-drifting DC-offsets and common mode signals. The paper will present our achievements in the design and implementation of a fully integrated bio-abio interface to record neural spiking activity. In particular

  7. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  8. The desktop interface in intelligent tutoring systems

    Science.gov (United States)

    Baudendistel, Stephen; Hua, Grace

    1987-01-01

    The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine.

  9. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  10. Virtual reality interface devices in the reorganization of neural networks in the brain of patients with neurological diseases

    Science.gov (United States)

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-01-01

    Two key characteristics of all virtual reality applications are interaction and immersion. Systemic interaction is achieved through a variety of multisensory channels (hearing, sight, touch, and smell), permitting the user to interact with the virtual world in real time. Immersion is the degree to which a person can feel wrapped in the virtual world through a defined interface. Virtual reality interface devices such as the Nintendo® Wii and its peripheral nunchuks-balance board, head mounted displays and joystick allow interaction and immersion in unreal environments created from computer software. Virtual environments are highly interactive, generating great activation of visual, vestibular and proprioceptive systems during the execution of a video game. In addition, they are entertaining and safe for the user. Recently, incorporating therapeutic purposes in virtual reality interface devices has allowed them to be used for the rehabilitation of neurological patients, e.g., balance training in older adults and dynamic stability in healthy participants. The improvements observed in neurological diseases (chronic stroke and cerebral palsy) have been shown by changes in the reorganization of neural networks in patients’ brain, along with better hand function and other skills, contributing to their quality of life. The data generated by such studies could substantially contribute to physical rehabilitation strategies. PMID:25206907

  11. Virtual reality interface devices in the reorganization of neural networks in the brain of patients with neurological diseases.

    Science.gov (United States)

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-04-15

    Two key characteristics of all virtual reality applications are interaction and immersion. Systemic interaction is achieved through a variety of multisensory channels (hearing, sight, touch, and smell), permitting the user to interact with the virtual world in real time. Immersion is the degree to which a person can feel wrapped in the virtual world through a defined interface. Virtual reality interface devices such as the Nintendo® Wii and its peripheral nunchuks-balance board, head mounted displays and joystick allow interaction and immersion in unreal environments created from computer software. Virtual environments are highly interactive, generating great activation of visual, vestibular and proprioceptive systems during the execution of a video game. In addition, they are entertaining and safe for the user. Recently, incorporating therapeutic purposes in virtual reality interface devices has allowed them to be used for the rehabilitation of neurological patients, e.g., balance training in older adults and dynamic stability in healthy participants. The improvements observed in neurological diseases (chronic stroke and cerebral palsy) have been shown by changes in the reorganization of neural networks in patients' brain, along with better hand function and other skills, contributing to their quality of life. The data generated by such studies could substantially contribute to physical rehabilitation strategies.

  12. 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.

  13. Microfabrication, characterization and in vivo MRI compatibility of diamond microelectrodes array for neural interfacing

    Energy Technology Data Exchange (ETDEWEB)

    Hébert, Clément, E-mail: clement.hebert@cea.fr [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France); Warnking, Jan; Depaulis, Antoine [INSERM, U836, Grenoble Institut des Neurosciences, Grenoble (France); Garçon, Laurie Amandine [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France); CEA/INAC/SPrAM/CREAB, 17 rue des Martyrs, 38054 Grenoble Cedex 9 (France); Mermoux, Michel [Université Grenoble Alpes, LEPMI, F-38000 Grenoble (France); CNRS, LEPMI, F-38000 Grenoble (France); Eon, David [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France); Mailley, Pascal [CEA-LETI-DTBS Minatec, 17 rue des Martyres, 38054 Grenoble (France); Omnès, Franck [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France)

    2015-01-01

    Neural interfacing still requires highly stable and biocompatible materials, in particular for in vivo applications. Indeed, most of the currently used materials are degraded and/or encapsulated by the proximal tissue leading to a loss of efficiency. Here, we considered boron doped diamond microelectrodes to address this issue and we evaluated the performances of a diamond microelectrode array. We described the microfabrication process of the device and discuss its functionalities. We characterized its electrochemical performances by cyclic voltammetry and impedance spectroscopy in saline buffer and observed the typical diamond electrode electrochemical properties, wide potential window and low background current, allowing efficient electrochemical detection. The charge storage capacitance and the modulus of the electrochemical impedance were found to remain in the same range as platinum electrodes used for standard commercial devices. Finally we observed a reduced Magnetic Resonance Imaging artifact when the device was implanted on a rat cortex, suggesting that boron doped-diamond is a very promising electrode material allowing functional imaging. - Highlights: • Microfabrication of all-diamond microelectrode array • Evaluation of as-grown nanocrystalline boron-doped diamond for electrical neural interfacing • MRI compatibility of nanocrystalline boron-doped diamond.

  14. Quantum neural network-based EEG filtering for a brain-computer interface.

    Science.gov (United States)

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  15. NEVESIM: event-driven neural simulation framework with a Python interface.

    Science.gov (United States)

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

  16. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  17. A Chronically Implantable Bidirectional Neural Interface for Non-human Primates

    Directory of Open Access Journals (Sweden)

    Misako Komatsu

    2017-09-01

    Full Text Available Optogenetics has potential applications in the study of epilepsy and neuroprostheses, and for studies on neural circuit dynamics. However, to achieve translation to clinical usage, optogenetic interfaces that are capable of chronic stimulation and monitoring with minimal brain trauma are required. We aimed to develop a chronically implantable device for photostimulation of the brain of non-human primates. We used a micro-light-emitting diode (LED array with a flexible polyimide film. The array was combined with a whole-cortex electrocorticographic (ECoG electrode array for simultaneous photostimulation and recording. Channelrhodopsin-2 (ChR2 was virally transduced into the cerebral cortex of common marmosets, and then the device was epidurally implanted into their brains. We recorded the neural activity during photostimulation of the awake monkeys for 4 months. The neural responses gradually increased after the virus injection for ~8 weeks and remained constant for another 8 weeks. The micro-LED and ECoG arrays allowed semi-invasive simultaneous stimulation and recording during long-term implantation in the brains of non-human primates. The development of this device represents substantial progress in the field of optogenetic applications.

  18. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    Directory of Open Access Journals (Sweden)

    Huixia Zhao

    Full Text Available The insect-machine interface (IMI is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L. via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe, ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

  19. 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.

  20. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    Science.gov (United States)

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect-machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

  1. Neuromechanism Study of Insect–Machine Interface: Flight Control by Neural Electrical Stimulation

    Science.gov (United States)

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A.; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect–machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee–machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control. PMID:25409523

  2. Task planning systems with natural language interface

    International Nuclear Information System (INIS)

    Kambayashi, Shaw; Uenaka, Junji

    1989-12-01

    In this report, a natural language analyzer and two different task planning systems are described. In 1988, we have introduced a Japanese language analyzer named CS-PARSER for the input interface of the task planning system in the Human Acts Simulation Program (HASP). For the purpose of a high speed analysis, we have modified a dictionary system of the CS-PARSER by using C language description. It is found that the new dictionary system is very useful for a high speed analysis and an efficient maintenance of the dictionary. For the study of the task planning problem, we have modified a story generating system named Micro TALE-SPIN to generate a story written in Japanese sentences. We have also constructed a planning system with natural language interface by using the CS-PARSER. Task planning processes and related knowledge bases of these systems are explained. A concept design for a new task planning system will be also discussed from evaluations of above mentioned systems. (author)

  3. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  4. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  5. Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

    Directory of Open Access Journals (Sweden)

    Sergio Ortega

    2010-12-01

    Full Text Available This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

  6. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  7. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  8. Systems and methods for monitoring a solid-liquid interface

    Science.gov (United States)

    Stoddard, Nathan G; Lewis, Monte A.; Clark, Roger F

    2013-06-11

    Systems and methods are provided for monitoring a solid-liquid interface during a casting process. The systems and methods enable determination of the location of a solid-liquid interface during the casting process.

  9. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  10. Used Fuel Management System Interface Analyses - 13578

    Energy Technology Data Exchange (ETDEWEB)

    Howard, Robert; Busch, Ingrid [Oak Ridge National Laboratory, P.O. Box 2008, Bldg. 5700, MS-6170, Oak Ridge, TN 37831 (United States); Nutt, Mark; Morris, Edgar; Puig, Francesc [Argonne National Laboratory (United States); Carter, Joe; Delley, Alexcia; Rodwell, Phillip [Savannah River National Laboratory (United States); Hardin, Ernest; Kalinina, Elena [Sandia National Laboratories (United States); Clark, Robert [U.S. Department of Energy (United States); Cotton, Thomas [Complex Systems Group (United States)

    2013-07-01

    Preliminary system-level analyses of the interfaces between at-reactor used fuel management, consolidated storage facilities, and disposal facilities, along with the development of supporting logistics simulation tools, have been initiated to provide the U.S. Department of Energy (DOE) and other stakeholders with information regarding the various alternatives for managing used nuclear fuel (UNF) generated by the current fleet of light water reactors operating in the United States. An important UNF management system interface consideration is the need for ultimate disposal of UNF assemblies contained in waste packages that are sized to be compatible with different geologic media. Thermal analyses indicate that waste package sizes for the geologic media under consideration by the Used Fuel Disposition Campaign may be significantly smaller than the canisters being used for on-site dry storage by the nuclear utilities. Therefore, at some point along the UNF disposition pathway, there could be a need to repackage fuel assemblies already loaded and being loaded into the dry storage canisters currently in use. The implications of where and when the packaging or repackaging of commercial UNF will occur are key questions being addressed in this evaluation. The analysis demonstrated that thermal considerations will have a major impact on the operation of the system and that acceptance priority, rates, and facility start dates have significant system implications. (authors)

  11. Investigation of human system interface design in nuclear power plant

    International Nuclear Information System (INIS)

    Feng Yan; Zhang Yunbo; Wang Zhongqiu

    2012-01-01

    The paper introduces the importance of HFE in designing nuclear power plant, and introduces briefly the content and scope of HFE, discusses human system interface design of new built nuclear power plants. This paper also describes human system interface design of foreign nuclear power plant, and describes in detail human system interface design of domestic nuclear power plant. (authors)

  12. Building intuitive 3D interfaces for virtual reality systems

    Science.gov (United States)

    Vaidya, Vivek; Suryanarayanan, Srikanth; Seitel, Mathias; Mullick, Rakesh

    2007-03-01

    An exploration of techniques for developing intuitive, and efficient user interfaces for virtual reality systems. Work seeks to understand which paradigms from the better-understood world of 2D user interfaces remain viable within 3D environments. In order to establish this a new user interface was created that applied various understood principles of interface design. A user study was then performed where it was compared with an earlier interface for a series of medical visualization tasks.

  13. Generating User Interfaces with the FUSE-System

    OpenAIRE

    Frank Lonczewski; Siegfried Schreiber

    2017-01-01

    With the FUSE(Formal User interface Specification Environment)-System we present a methodology and a set of integrated tools for the automatic generation of graphical user interfaces. FUSE provides tool-based support for all phases (task-, user-, problem domain analysis, design of the logical user interface, design of user interface in a particular layout style) of the user interface development process. Based on a formal specification of dialogue- and layout guidelines, FUSE allows the autom...

  14. Neural control of finger movement via intracortical brain-machine interface

    Science.gov (United States)

    Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

    2017-12-01

    Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe

  15. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  16. Support for User Interfaces for Distributed Systems

    Science.gov (United States)

    Eychaner, Glenn; Niessner, Albert

    2005-01-01

    An extensible Java(TradeMark) software framework supports the construction and operation of graphical user interfaces (GUIs) for distributed computing systems typified by ground control systems that send commands to, and receive telemetric data from, spacecraft. Heretofore, such GUIs have been custom built for each new system at considerable expense. In contrast, the present framework affords generic capabilities that can be shared by different distributed systems. Dynamic class loading, reflection, and other run-time capabilities of the Java language and JavaBeans component architecture enable the creation of a GUI for each new distributed computing system with a minimum of custom effort. By use of this framework, GUI components in control panels and menus can send commands to a particular distributed system with a minimum of system-specific code. The framework receives, decodes, processes, and displays telemetry data; custom telemetry data handling can be added for a particular system. The framework supports saving and later restoration of users configurations of control panels and telemetry displays with a minimum of effort in writing system-specific code. GUIs constructed within this framework can be deployed in any operating system with a Java run-time environment, without recompilation or code changes.

  17. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

    Science.gov (United States)

    Michelson, Nicholas J.; Vazquez, Alberto L.; Eles, James R.; Salatino, Joseph W.; Purcell, Erin K.; Williams, Jordan J.; Cui, X. Tracy; Kozai, Takashi D. Y.

    2018-06-01

    Objective. Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant. Approach. In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes. Main results. Even after carefully accounting for these factors, the presence of viable neurons and lack of glial scarring does not convey single unit recording performance. Significance. To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies.

  18. Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

    Science.gov (United States)

    Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

    2013-06-01

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

  19. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  20. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  1. Desirable Elements for a Particle System Interface

    Directory of Open Access Journals (Sweden)

    Daniel Schroeder

    2014-01-01

    Full Text Available Particle systems have many applications, with the most popular being to produce special effects in video games and films. To permit particle systems to be created quickly and easily, Particle System Interfaces (PSIs have been developed. A PSI is a piece of software designed to perform common tasks related to particle systems for clients, while providing them with a set of parameters whose values can be adjusted to create different particle systems. Most PSIs are inflexible, and when clients require functionality that is not supported by the PSI they are using, they are forced to either find another PSI that meets their requirements or, more commonly, create their own particle system or PSI from scratch. This paper presents three original contributions. First, it identifies 18 features that a PSI should provide in order to be capable of creating diverse effects. If these features are implemented in a PSI, clients will be more likely to be able to accomplish all desired effects related to particle systems with one PSI. Secondly, it introduces a novel use of events to determine, at run time, which particle system code to execute in each frame. Thirdly, it describes a software architecture called the Dynamic Particle System Framework (DPSF. Simulation results show that DPSF possesses all 18 desirable features.

  2. Man-system interface based on automatic speech recognition: integration to a virtual control desk

    Energy Technology Data Exchange (ETDEWEB)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2009-07-01

    This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

  3. Man-system interface based on automatic speech recognition: integration to a virtual control desk

    International Nuclear Information System (INIS)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C.; Nomiya, Diogo V.

    2009-01-01

    This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

  4. Federating resources of information systems: browsing interface (FRISBI)

    NARCIS (Netherlands)

    Malchanau, A.V.; van der Vet, P.E.; Roosendaal, Hans E.; de Bra, P.M.E.

    2003-01-01

    Designing the user interface of a federated system (what we call a browsing interface) must consider the knowledge gap that exists between desires of the users and the needs the systems are built to support. The concept of Habitable Interfaces aims to bridge the knowledge gap by providing kinds of

  5. Waste assay measurement integration system user interface

    International Nuclear Information System (INIS)

    Mousseau, K.C.; Hempstead, A.R.; Becker, G.K.

    1995-01-01

    The Waste Assay Measurement Integration System (WAMIS) is being developed to improve confidence in and lower the uncertainty of waste characterization data. There are two major components to the WAMIS: a data access and visualization component and a data interpretation component. The intent of the access and visualization software is to provide simultaneous access to all data sources that describe the contents of any particular container of waste. The visualization software also allows the user to display data at any level from raw to reduced output. Depending on user type, the software displays a menuing hierarchy, related to level of access, that allows the user to observe only those data sources s/he has been authorized to view. Access levels include system administrator, physicist, QA representative, shift operations supervisor, and data entry. Data sources are displayed in separate windows and presently include (1) real-time radiography video, (2) gamma spectra, (3) passive and active neutron, (4) radionuclide mass estimates, (5) total alpha activity (Ci), (6) container attributes, (7) thermal power (w), and (8) mass ratio estimates for americium, plutonium, and uranium isotopes. The data interpretation component is in the early phases of design, but will include artificial intelligence, expert system, and neural network techniques. The system is being developed on a Pentium PC using Microsoft Visual C++. Future generations of WAMIS will be UNIX based and will incorporate more generically radiographic/tomographic, gamma spectroscopic/tomographics, neutron, and prompt gamma measurements

  6. Optimizing the performance of neural interface devices with hybrid poly(3,4-ethylene dioxythiophene) (PEDOT)

    Science.gov (United States)

    Kuo, Chin-chen

    This thesis describes methods for improving the performance of poly(3,4-ethylenedioxythiophene) (PEDOT) as a direct neural interfacing material. The chronic foreign body response is always a challenge for implanted bionic devices. After long-term implantation (typically 2-4 weeks), insulating glial scars form around the devices, inhibiting signal transmission, which ultimately leads to device failure. The mechanical mismatch at the device-tissue interface is one of the issues that has been associated with chronic foreign body response. Another challenge for using PEDOT as a neural interface material is its mechanical failure after implantation. We observed cracking and delamination of PEDOT coatings on devices after extended implantations. In the first part of this thesis, we present a novel method for directly measuring the mechanical properties of a PEDOT thin film. Before investigating methods to improve the mechanical behavior of PEDOT, a comprehensive understanding of the mechanical properties of PEDOT thin film is required. A PEDOT thin film was machined into a dog-bone shape specimen with a dual beam FIB-SEM. With an OmniProbe, this PEDOT specimen could be attached onto a force sensor, while the other side was attached to OmniProbe. By moving the OmniProbe, the specimen could be deformed in tension, and a force sensor recorded the applied load on the sample simultaneously. Mechanical tensile tests were conducted in the FIB-SEM chamber along with in situ observation. With precise force measurement from the force sensor and the corresponding high resolution SEM images, we were able to convert the data to a stress-strain curve for further analysis. By analyzing these stress-strain curves, we were able to obtain information about PEDOT including the Young's modulus, strength of failure, strain to failure, and toughness (energy to failure). This information should be useful for future material selection and molecular design for specific applications. The second

  7. TFTR centralized torus interface valve control system

    International Nuclear Information System (INIS)

    Pearson, G.G.; Olsen, D.H.

    1983-01-01

    A system developed especially for the TFTR to monitor and control the interface between the vacuum vessel and associated diagnostics will be described in this paper. Diagnostics which must be connected to the machine vacuum are required to do so through a Torus Interface Valve (TIV). Two types of TIV's are used on TFTR. The first type is a non-latching valve which must be held in the opened position by a sustained OPEN command, returning automatically to the closed position when the OPEN command is removed. This type of TIV is used on all systems which never insert a probe into the vacuum vessel through the TIV. The second type of TIV is a latching valve which requires a momentary OPEN command to open and a momentary CLOSE command to close. Each TIV is linked to its own dedicated logic controller. Each logic controller is hardwired to the appropriate TIV OPEN/CLOSED limit switches, probe IN/OUT limit switches, TFTR vacuum vessel pressure setpoint switches, and diagnostic pressure setpoint switches. The logic controller can be configured for local (push-button) or remote (computer) control. Each controller has a uniquely coded keyswitch to determine the configuration. Whether under local or remote control, all OPEN and CLOSE commands must be approved by the TIV controller (TIVC). In the case of systems with probes, the controller must receive a positive indication that the probe is completely backed out before a CLOSE command will be transmitted from the TIVC to the TIV. Before a valve will be opened by a controller, the differential pressure across the valve must be within certain limits

  8. DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity

    Science.gov (United States)

    Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2013-12-01

    Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

  9. DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.

    Science.gov (United States)

    Cowley, Benjamin R; Kaufman, Matthew T; Butler, Zachary S; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V; Yu, Byron M

    2013-12-01

    Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

  10. Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

    Directory of Open Access Journals (Sweden)

    Markus A Wenzel

    Full Text Available Brain-computer interfaces (BCIs that are based on event-related potentials (ERPs can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG. Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI, because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG.The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

  11. DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    Science.gov (United States)

    Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2014-01-01

    Objective Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity. PMID:24216250

  12. Orbiter Interface Unit and Early Communication System

    Science.gov (United States)

    Cobbs, Ronald M.; Cooke, Michael P.; Cox, Gary L.; Ellenberger, Richard; Fink, Patrick W.; Haynes, Dena S.; Hyams, Buddy; Ling, Robert Y.; Neighbors, Helen M.; Phan, Chau T.; hide

    2004-01-01

    This report describes the Orbiter Interface Unit (OIU) and the Early Communication System (ECOMM), which are systems of electronic hardware and software that serve as the primary communication links for the International Space Station (ISS). When a space shuttle is at or near the ISS during assembly and resupply missions, the OIU sends groundor crew-initiated commands from the space shuttle to the ISS and relays telemetry from the ISS to the space shuttle s payload data systems. The shuttle then forwards the telemetry to the ground. In the absence of a space shuttle, the ECOMM handles communications between the ISS and Johnson Space Center via the Tracking and Data Relay Satellite System (TDRSS). Innovative features described in the report include (1) a "smart data-buffering algorithm that helps to preserve synchronization (and thereby minimize loss) of telemetric data between the OIU and the space-shuttle payload data interleaver; (2) an ECOMM antenna-autotracking algorithm that selects whichever of two phased-array antennas gives the best TDRSS signal and electronically steers that antenna to track the TDRSS source; and (3) an ECOMM radiation-latchup controller, which detects an abrupt increase in current indicative of radiation-induced latchup and temporarily turns off power to clear the latchup, restoring power after the charge dissipates.

  13. Neural systems for preparatory control of imitation.

    Science.gov (United States)

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

  14. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  15. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  16. Microfabrication, characterization and in vivo MRI compatibility of diamond microelectrodes array for neural interfacing.

    Science.gov (United States)

    Hébert, Clément; Warnking, Jan; Depaulis, Antoine; Garçon, Laurie Amandine; Mermoux, Michel; Eon, David; Mailley, Pascal; Omnès, Franck

    2015-01-01

    Neural interfacing still requires highly stable and biocompatible materials, in particular for in vivo applications. Indeed, most of the currently used materials are degraded and/or encapsulated by the proximal tissue leading to a loss of efficiency. Here, we considered boron doped diamond microelectrodes to address this issue and we evaluated the performances of a diamond microelectrode array. We described the microfabrication process of the device and discuss its functionalities. We characterized its electrochemical performances by cyclic voltammetry and impedance spectroscopy in saline buffer and observed the typical diamond electrode electrochemical properties, wide potential window and low background current, allowing efficient electrochemical detection. The charge storage capacitance and the modulus of the electrochemical impedance were found to remain in the same range as platinum electrodes used for standard commercial devices. Finally we observed a reduced Magnetic Resonance Imaging artifact when the device was implanted on a rat cortex, suggesting that boron doped-diamond is a very promising electrode material allowing functional imaging. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Salt Repository Project transportation system interface requirements: Transportation system/repository receiving facility interface requirements

    International Nuclear Information System (INIS)

    Smith, L.A.; Insalaco, J.W.; Trainer, T.A.

    1988-01-01

    This report is a preliminary review of the interface between the transportation system and the repository receiving facility for a nuclear waste mined geologic disposal system in salt. Criteria for generic cask and facility designs are developed. These criteria are derived by examining the interfaces that occur as a result of the operations needed to receive nuclear waste at a repository. These criteria provide the basis for design of a safe, operable, practical nuclear waste receiving facility. The processing functions required to move the shipping unit from the gate into the unloading area and back to the gate for dispatch are described. Criteria for a generic receiving facility are discussed but no specific facility design is presented or evaluated. The criteria are stated in general terms to allow application to a wide variety of cask and facility designs. 9 refs., 6 figs., 4 tabs

  18. Human Systems Interface Design Methods Using Ecological Interface Design Principles

    International Nuclear Information System (INIS)

    Hong, Seung Kweon; Park, Jung Chul; Kim, Sun Su; Sim, Kwang Pyo; Yuk, Seung Yul; Choi, Jae Hyeon; Yoon, Seung Hyun

    2009-12-01

    The results of this study categorized into two parts. The first part is the guidelines for EID designs. The procedure to observe for EID design is composed of 6 steps; 1) to define a target system, 2) to make an abstraction hierarchy model, 3) to check the link structure among each components included in the layers of abstraction hierarchy model, 4) to transform information requirements to variables, 5) to make the graphs related to each variables, 6) to check the graphs by visual display design principles and heuristic rules. The second part is an EID design alternative for nuclear power plant. The EID for high level function represents the energy balance and energy flow in each loop of nuclear power plant. The EID for middle level function represents the performance indicators of each equipment involved in the all processes of changing from coolants to steam. The EID for low level function represents the values measured in each equipment such as temperature, pressure, water level and so on

  19. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  20. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  1. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  2. Standard interfaces for program-modular multiprocessor systems

    International Nuclear Information System (INIS)

    Chernykh, E.V.

    1982-01-01

    The peculiarities of the structures of existing and developed standard interfaces used in automation systems for nuclear physical experiments are considered. general structural characteristics of multiprocessor system interfaces are revealed. The comparison of the existing system CAMAC crate and designed standards of COMPEX, E3S and FASTBUS interfaces by capacity and relative cost is carried out. The analysis of the given data shows that operation of any interface is more advantageous at the rates close to capacity values, the relative cost being minimum. In this case the advantage is on the side of interfaces with greater capacity values for which at a moderated decrease of the exchange or requests processing rate the relative costs grow slower. A higher capacity of one-cycle exchange is provided with functional data way specialization in the interface. The conclusion is drawn that most perspective trend in the development of automation systems for high energy physics experiments is using FASTBUS standard

  3. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  4. Modeling the electrode-neuron interface of cochlear implants: effects of neural survival, electrode placement, and the partial tripolar configuration.

    Science.gov (United States)

    Goldwyn, Joshua H; Bierer, Steven M; Bierer, Julie Arenberg

    2010-09-01

    The partial tripolar electrode configuration is a relatively novel stimulation strategy that can generate more spatially focused electric fields than the commonly used monopolar configuration. Focused stimulation strategies should improve spectral resolution in cochlear implant users, but may also be more sensitive to local irregularities in the electrode-neuron interface. In this study, we develop a practical computer model of cochlear implant stimulation that can simulate neural activation in a simplified cochlear geometry and we relate the resulting patterns of neural activity to basic psychophysical measures. We examine how two types of local irregularities in the electrode-neuron interface, variations in spiral ganglion nerve density and electrode position within the scala tympani, affect the simulated neural activation patterns and how these patterns change with electrode configuration. The model shows that higher partial tripolar fractions activate more spatially restricted populations of neurons at all current levels and require higher current levels to excite a given number of neurons. We find that threshold levels are more sensitive at high partial tripolar fractions to both types of irregularities, but these effects are not independent. In particular, at close electrode-neuron distances, activation is typically more spatially localized which leads to a greater influence of neural dead regions. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  5. Interface management for the Mined Geologic Disposal System

    International Nuclear Information System (INIS)

    Ashlock, K.J.

    1998-03-01

    The purpose of this paper is to present the interface management process that is to be used for Mined Geologic Disposal System (MGDS) development. As part of the systems engineering and integration performed on the Yucca Mountain Project (YMP), interface management is critical in the development of the potential MGDS. The application of interface management on the YMP directly addresses integration between physical elements of the MGDS and the organizations responsible for their development

  6. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  7. Critical interfaces in geosynthetic multilayer liner system of a landfill

    Directory of Open Access Journals (Sweden)

    Qian Xuede

    2008-12-01

    Full Text Available This study is to identify the critical interface in a geosynthetic multilayer liner system by examining the effects of the interface shear strength of liner components, leachate level, leachate buildup cases, and peak and residual interface strengths. According to current landfill design procedures, conducting stability analysis along the same interface at both the back slope and base may result in a non-conservative result. The critical interfaces with the minimum factor of safety are generally found at different locations along the back slope and base. The critical interface for a multilayer liner system cannot simply be assumed during stability analysis. It can shift from one interface to another with changes in the leachate level and with different leachate buildup cases. The factor of safety for an interface with a high friction angle and low apparent cohesion generally drops much more quickly than it does under inverse conditions when the leachate level increases. The failure interface in a liner system under residual conditions is usually different from the failure interface under peak conditions.

  8. Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

    Science.gov (United States)

    Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

    2015-05-01

    Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

  9. User interface design in safety parameter display systems

    International Nuclear Information System (INIS)

    Schultz, E.E. Jr.; Johnson, G.L.

    1988-01-01

    The extensive installation of computerized safety Parameter Display Systems (SPDSs) in nuclear power plants since the Three-Mile Island accident has enhanced plant safety. It has also raised new issues of how best to ensure an effective interface between human operators and the plant via computer systems. New developments in interface technologies since the current generation of SPDSs was installed can contribute to improving display interfaces. These technologies include new input devices, three-dimensional displays, delay indicators, and auditory displays. Examples of how they might be applied to improve current SPDSs are given. These examples illustrate how the new use interface technology could be applied to future nuclear plant displays

  10. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  11. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  12. A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

    International Nuclear Information System (INIS)

    Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

    2006-01-01

    In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

  13. User interfaces of information retrieval systems and user friendliness

    OpenAIRE

    Polona Vilar; Maja Žumer

    2008-01-01

    The paper deals with the characteristics of user interfaces of information retrieval systems with the emphasis on design and evaluation. It presents users’ information retrieval tasks and the functions which are offered through interfaces. Design rules, guidelines and standards are presented, as well as criteria and methods for evaluation. Special emphasis is placed on the concept of user friendliness as one of the most important characteristic of the user interfaces. Various definitions of u...

  14. Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

    Science.gov (United States)

    Mastinu, Enzo; Doguet, Pascal; Botquin, Yohan; Hakansson, Bo; Ortiz-Catalan, Max

    2017-08-01

    Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

  15. Bifurcation and chaos in neural excitable system

    International Nuclear Information System (INIS)

    Jing Zhujun; Yang Jianping; Feng Wei

    2006-01-01

    In this paper, we investigate the dynamical behaviors of neural excitable system without periodic external current (proposed by Chialvo [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] and with periodic external current as system's parameters vary. The existence and stability of three fixed points, bifurcation of fixed points, the conditions of existences of fold bifurcation, flip bifurcation and Hopf bifurcation are derived by using bifurcation theory and center manifold theorem. The chaotic existence in the sense of Marotto's definition of chaos is proved. We then give the numerical simulated results (using bifurcation diagrams, computations of Maximum Lyapunov exponent and phase portraits), which not only show the consistence with the analytic results but also display new and interesting dynamical behaviors, including the complete period-doubling and inverse period-doubling bifurcation, symmetry period-doubling bifurcations of period-3 orbit, simultaneous occurrence of two different routes (invariant cycle and period-doubling bifurcations) to chaos for a given bifurcation parameter, sudden disappearance of chaos at one critical point, a great abundance of period windows (period 2 to 10, 12, 19, 20 orbits, and so on) in transient chaotic regions with interior crises, strange chaotic attractors and strange non-chaotic attractor. In particular, the parameter k plays a important role in the system, which can leave the chaotic behavior or the quasi-periodic behavior to period-1 orbit as k varies, and it can be considered as an control strategy of chaos by adjusting the parameter k. Combining the existing results in [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] with the new results reported in this paper, a more complete description of the system is now obtained

  16. A graphical user-interface for propulsion system analysis

    Science.gov (United States)

    Curlett, Brian P.; Ryall, Kathleen

    1993-01-01

    NASA LeRC uses a series of computer codes to calculate installed propulsion system performance and weight. The need to evaluate more advanced engine concepts with a greater degree of accuracy has resulted in an increase in complexity of this analysis system. Therefore, a graphical user interface was developed to allow the analyst to more quickly and easily apply these codes. The development of this interface and the rationale for the approach taken are described. The interface consists of a method of pictorially representing and editing the propulsion system configuration, forms for entering numerical data, on-line help and documentation, post processing of data, and a menu system to control execution.

  17. User interfaces of information retrieval systems and user friendliness

    Directory of Open Access Journals (Sweden)

    Polona Vilar

    2008-01-01

    Full Text Available The paper deals with the characteristics of user interfaces of information retrieval systems with the emphasis on design and evaluation. It presents users’ information retrieval tasks and the functions which are offered through interfaces. Design rules, guidelines and standards are presented, as well as criteria and methods for evaluation. Special emphasis is placed on the concept of user friendliness as one of the most important characteristic of the user interfaces. Various definitions of user friendliness are presented and their elements are also discussed. In the end, the paper shows how user interfaces should be designed, taken into consideration all these criteria.

  18. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  19. Human machine interface for research reactor instrumentation and control system

    International Nuclear Information System (INIS)

    Mohd Sabri Minhat; Mohd Idris Taib; Izhar Abu Hussin; Zareen Khan Abdul Jalil Khan; Nurfarhana Ayuni Joha

    2010-01-01

    Most present design of Human Machine Interface for Research Reactor Instrumentation and Control System is modular-based, comprise of several cabinets such as Reactor Protection System, Control Console, Information Console as well as Communication Console. The safety, engineering and human factor will be concerned for the design. Redundancy and separation of signal and power supply are the main factor for safety consideration. The design of Operator Interface absolutely takes consideration of human and environmental factors. Physical parameters, experiences, trainability and long-established habit patterns are very important for user interface, instead of the Aesthetic and Operator-Interface Geometry. Physical design for New Instrumentation and Control System of RTP are proposed base on the state-of- the-art Human Machine Interface design. (author)

  20. Interface management for the Mined Geologic Disposal System

    International Nuclear Information System (INIS)

    Ashlock, K.J.; Sellers, M.D.

    1998-01-01

    The Management and Operations (M and O) contractor for the Department of Energy's (DOE) Office of Civilian Radioactive Waste Management (OCRWM) program exists to support DOE in the successful development and operation of an integrated system to manage the nation's spent nuclear fuel and high-level wastes. As part of the system engineering and integration performed on the Yucca Mountain Project (YMP), interface management is critical in the development of the Mined Geologic Disposal System (MGDS). The application of interface management on the YMP directly addresses integration between physical elements of the MGDS and the organizations responsible for their development. An initiative to utilize interface management and the interface control document development process for organizational interfaces is also being pursued to help ensure consistent use of information by multiple organizations

  1. Applying machine learning to build a website interface adaptation system

    OpenAIRE

    MATESHUK EGOR; CHERNYSHEV ALEXANDER

    2015-01-01

    In this article we present the architecture and model of a website interface optimization system. We describe how we use clustering and genetic algorithms to automatically select a website interface with the highest conversion from website visitor to website user. In particular, we describe an algorithm for streamed clustering, which allows for real-time analysis of high traffic website users.

  2. Age Based User Interface in Mobile Operating System

    OpenAIRE

    Sharma, Sumit; Sharma, Rohitt; Singh, Paramjit; Mahajan, Aditya

    2012-01-01

    This paper proposes the creation of different interfaces in the mobile operating system for different age groups. The different age groups identified are kids, elderly people and all others. The motive behind creating different interfaces is to make the smartphones of today's world usable to all age groups.

  3. Connected Lighting System Interoperability Study Part 1: Application Programming Interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Gaidon, Clement [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Poplawski, Michael [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2017-10-31

    First in a series of studies that focuses on interoperability as realized by the use of Application Programming Interfaces (APIs), explores the diversity of such interfaces in several connected lighting systems; characterizes the extent of interoperability that they provide; and illustrates challenges, limitations, and tradeoffs that were encountered during this exploration.

  4. DESIGN OF A VISUAL INTERFACE FOR ANN BASED SYSTEMS

    Directory of Open Access Journals (Sweden)

    Ramazan BAYINDIR

    2008-01-01

    Full Text Available Artificial intelligence application methods have been used for control of many systems with parallel of technological development besides conventional control techniques. Increasing of artificial intelligence applications have required to education in this area. In this paper, computer based an artificial neural network (ANN software has been presented to learning and understanding of artificial neural networks. By means of the developed software, the training of the artificial neural network according to the inputs provided and a test action can be performed by changing the components such as iteration number, momentum factor, learning ratio, and efficiency function of the artificial neural networks. As a result of the study a visual education set has been obtained that can easily be adapted to the real time application.

  5. The intelligent user interface for NASA's advanced information management systems

    Science.gov (United States)

    Campbell, William J.; Short, Nicholas, Jr.; Rolofs, Larry H.; Wattawa, Scott L.

    1987-01-01

    NASA has initiated the Intelligent Data Management Project to design and develop advanced information management systems. The project's primary goal is to formulate, design and develop advanced information systems that are capable of supporting the agency's future space research and operational information management needs. The first effort of the project was the development of a prototype Intelligent User Interface to an operational scientific database, using expert systems and natural language processing technologies. An overview of Intelligent User Interface formulation and development is given.

  6. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  7. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  8. Guidance for Human-system Interfaces to Automatic Systems

    Energy Technology Data Exchange (ETDEWEB)

    O' Hara, J.M.; Higgins, J.; Stephen Fleger; Valerie Barnes

    2010-09-27

    Automation is ubiquitous in modern complex systems, and commercial nuclear- power plants are no exception. Automation is applied to a wide range of functions, including monitoring and detection, situation assessment, response planning, and response implementation. Automation has become a 'team player' supporting personnel in nearly all aspects of system operation. In light of its increasing use and importance in new- and future-plants, guidance is needed to conduct safety reviews of the operator's interface with automation. The objective of this research was to develop such guidance. We first characterized the important HFE aspects of automation, including six dimensions: Levels, functions, processes, modes, flexibility, and reliability. Next, we reviewed literature on the effects of all of these aspects of automation on human performance, and on the design of human-system interfaces (HSIs). Then, we used this technical basis established from the literature to identify general principles for human-automation interaction and to develop review guidelines. The guidelines consist of the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration.

  9. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  10. ScienceOrganizer System and Interface Summary

    Science.gov (United States)

    Keller, Richard M.; Norvig, Peter (Technical Monitor)

    2001-01-01

    ScienceOrganizer is a specialized knowledge management tool designed to enhance the information storage, organization, and access capabilities of distributed NASA science teams. Users access ScienceOrganizer through an intuitive Web-based interface that enables them to upload, download, and organize project information - including data, documents, images, and scientific records associated with laboratory and field experiments. Information in ScienceOrganizer is "threaded", or interlinked, to enable users to locate, track, and organize interrelated pieces of scientific data. Linkages capture important semantic relationships among information resources in the repository, and these assist users in navigating through the information related to their projects.

  11. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  12. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  13. Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

    Directory of Open Access Journals (Sweden)

    Cipriani Christian

    2011-09-01

    Full Text Available Abstract Background The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. Methods Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. Results The results showed that motor information (e.g., grip types and single finger movements could be extracted with classification accuracy around 85% (for three classes plus rest and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. Conclusions These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

  14. Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.

    Science.gov (United States)

    Micera, Silvestro; Rossini, Paolo M; Rigosa, Jacopo; Citi, Luca; Carpaneto, Jacopo; Raspopovic, Stanisa; Tombini, Mario; Cipriani, Christian; Assenza, Giovanni; Carrozza, Maria C; Hoffmann, Klaus-Peter; Yoshida, Ken; Navarro, Xavier; Dario, Paolo

    2011-09-05

    The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

  15. User Interface Aspects of a Human-Hand Simulation System

    Directory of Open Access Journals (Sweden)

    Beifang Yi

    2005-10-01

    Full Text Available This paper describes the user interface design for a human-hand simulation system, a virtual environment that produces ground truth data (life-like human hand gestures and animations and provides visualization support for experiments on computer vision-based hand pose estimation and tracking. The system allows users to save time in data generation and easily create any hand gestures. We have designed and implemented this user interface with the consideration of usability goals and software engineering issues.

  16. Transport and metabolism at blood-brain interfaces and in neural cells: relevance to bilirubin-induced encephalopathy

    Directory of Open Access Journals (Sweden)

    Silvia eGazzin

    2012-05-01

    Full Text Available Bilirubin, the end-product of heme catabolism, circulates in non pathological plasma mostly as a protein-bound species. When bilirubin concentration builds up, the free fraction of the molecule increases. Unbound bilirubin then diffuses across blood-brain interfaces into the brain, where it accumulates and exerts neurotoxic effects. In this classical view of bilirubin neurotoxicity, blood-brain interfaces act merely as structural barriers impeding the penetration of the pigment-bound carrier protein, and neural cells are considered as passive targets of its toxicity. Yet, the role of blood-brain interfaces in the occurrence of bilirubin encephalopathy appears more complex than being simple barriers to the diffusion of bilirubin, and neural cells such as astrocytes and neurons can play an active role in controlling the balance between the neuroprotective and neurotoxic effects of bilirubin. This article reviews the emerging in vivo and in vitro data showing that transport and metabolic detoxification mechanisms at the blood-brain and blood-CSF barriers may modulate bilirubin flux across both cellular interfaces, and that these protective functions can be affected in chronic hyperbilirubinemia. Then the in vivo and in vitro arguments in favor of the physiological antioxidant function of intracerebral bilirubin are presented, as well as with the potential role of transporters such as ABCC-1 and metabolizing enzymes such as cytochromes P-450 in setting the cerebral cell- and structure-specific toxicity of bilirubin following hyperbilirubinemia. The relevance of these data to the pathophysiology of bilirubin-induced neurological diseases is discussed.

  17. VisTool: A user interface and visualization development system

    DEFF Research Database (Denmark)

    Xu, Shangjin

    system – to simplify user interface development. VisTool allows user interface development without real programming. With VisTool a designer assembles visual objects (e.g. textboxes, ellipse, etc.) to visualize database contents. In VisTool, visual properties (e.g. color, position, etc.) can be formulas...... programming. However, in Software Engineering, software engineers who develop user interfaces do not follow it. In many cases, it is desirable to use graphical presentations, because a graphical presentation gives a better overview than text forms, and can improve task efficiency and user satisfaction....... However, it is more difficult to follow the classical usability approach for graphical presentation development. These difficulties result from the fact that designers cannot implement user interface with interactions and real data. We developed VisTool – a user interface and visualization development...

  18. Use of neural networks in the analysis of complex systems

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  19. Microprocessor controlled dual parameter ADC system with a CAMAC interface

    Energy Technology Data Exchange (ETDEWEB)

    Perry, D G; Nickell, Jr, J D [Los Alamos Scientific Lab., NM (USA)

    1978-09-01

    Presented here is the design of a dual parameter ADC system which is controlled by a microprocessor and also interfaced to CAMAC. The system was designed to be mobile in that it may work wherever there is a CAMAC crate. In such cases where the CAMAC system is inoperative, the system may operate in a stand-alone mode.

  20. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  1. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Conductive nanogel-interfaced neural microelectrode arrays with electrically controlled in-situ delivery of manganese ions enabling high-resolution MEMRI for synchronous neural tracing with deep brain stimulation.

    Science.gov (United States)

    Huang, Wei-Chen; Lo, Yu-Chih; Chu, Chao-Yi; Lai, Hsin-Yi; Chen, You-Yin; Chen, San-Yuan

    2017-04-01

    Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl 2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn 2+ . In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn 2+ ions, so that the system can release Mn 2+ ions rather than MnCl 2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high-resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based microelectrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional

  3. Web services interface of SSRF archive data analysis system

    International Nuclear Information System (INIS)

    Li Lin; Shen Liren; Zhu Qing; Wan Tianmin

    2007-01-01

    Accelerator database stores various static parameters and real-time data of accelerator. SSRF (Shanghai Synchrotron Radiation Facility) adopts relational database to save the data. We developed a data retrieval system based on XML Web Services for accessing the archive data. It includes a bottom layer interface and an interface applicable for accelerator physics. Client samples exemplifying how to consume the interface are given. The users can browse, retrieve and plot data by the client samples. Also, we give a method to test its stability. The test result and performance are described. (authors)

  4. Applying of USB interface technique in nuclear spectrum acquisition system

    International Nuclear Information System (INIS)

    Zhou Jianbin; Huang Jinhua

    2004-01-01

    This paper introduces applying of USB technique and constructing nuclear spectrum acquisition system via PC's USB interface. The authors choose the USB component USB100 module and the W77E58μc to do the key work. It's easy to apply USB interface technique, when USB100 module is used. USB100 module can be treated as a common I/O component for the μc controller, and can be treated as a communication interface (COM) when connected to PC' USB interface. It's easy to modify the PC's program for the new system with USB100 module. The authors can smoothly change from ISA, RS232 bus to USB bus. (authors)

  5. Man-machine interfaces analysis system based on computer simulation

    International Nuclear Information System (INIS)

    Chen Xiaoming; Gao Zuying; Zhou Zhiwei; Zhao Bingquan

    2004-01-01

    The paper depicts a software assessment system, Dynamic Interaction Analysis Support (DIAS), based on computer simulation technology for man-machine interfaces (MMI) of a control room. It employs a computer to simulate the operation procedures of operations on man-machine interfaces in a control room, provides quantified assessment, and at the same time carries out analysis on operational error rate of operators by means of techniques for human error rate prediction. The problems of placing man-machine interfaces in a control room and of arranging instruments can be detected from simulation results. DIAS system can provide good technical supports to the design and improvement of man-machine interfaces of the main control room of a nuclear power plant

  6. Design interface management system for nuclear power plant project

    International Nuclear Information System (INIS)

    Wang Jun

    2012-01-01

    Design interfaces exist between different participants and during the whole course of a nuclear power project, and include different disciplinary requirements. The purpose of interface management is to establish a procedure, which can be efficiently used to control the complex design interfaces and ensure its compliance with NPP design requirements. To this end, a complete work procedures and relationship will be defined and classified, so as to set up the structure of interface management system. The system consists of three levels, i.e. working procedure level, management tool level and technical document level. Two management routes, i.e. administration route and technical route, are adopted so as to conduct management efficiently. (author)

  7. Neural Correlates of User-initiated Motor Success and Failure - A Brain-Computer Interface Perspective.

    Science.gov (United States)

    Yazmir, Boris; Reiner, Miriam

    2018-05-15

    Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  8. The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism.

    Science.gov (United States)

    Eagleson, Kathie L; Xie, Zhihui; Levitt, Pat

    2017-03-01

    People with autism spectrum disorder and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy-the influence of one gene on distinct phenotypes-raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multifunctional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with autism spectrum disorder, reduces transcription and disrupts socially relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways and has a complex protein interactome that is enriched in autism spectrum disorder and other NDD candidates. The interactome is coregulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, affecting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  9. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  10. Reservation system with graphical user interface

    KAUST Repository

    Mohamed, Mahmoud A. Abdelhamid; Jamjoom, Hani T.; Podlaseck, Mark E.; Qu, Huiming; Shae, Zon-Yin; Sheopuri, Anshul

    2012-01-01

    Techniques for providing a reservation system are provided. The techniques include displaying a scalable visualization object, wherein the scalable visualization object comprises an expanded view element of the reservation system depicting

  11. Human-system interface for CAREM nuclear reactor

    International Nuclear Information System (INIS)

    Abaurre, Norberto F.; Flury, Celso A.; Pierini, Juan P.; Etchepareborda, Andres; Breitembuecher, Alfredo J.; Lema, Fabian M.

    2009-01-01

    Associated with activities to be developed by our working group on the construction of the reactor training simulator for the CAREM, we have planned the design of human-system interface (HSI) of the main control room. The goal of this study is to describe the planning and methodology used for the HSI interface design. The products of this process are the layout specifications of the Control Room and the screens specifications for control software. (author)

  12. Multiple multichannel spectra acquisition and processing system with intelligent interface

    International Nuclear Information System (INIS)

    Chen Ying; Wei Yixiang; Qu Jianshi; Zheng Futang; Xu Shengkui; Xie Yuanming; Qu Xing; Ji Weitong; Qiu Xuehua

    1986-01-01

    A Multiple multichannel spectra acquisition and processing system with intelligent interface is described. Sixteen spectra measured with various lengths, channel widths, back biases and acquisition times can be identified and collected by the intelligent interface simultaneously while the connected computer is doing data processing. The execution time for the Ge(Li) gamma-ray spectrum analysis software on IBM PC-XT is about 55 seconds

  13. Nimrod 4080 computer - Nixie data system interface

    International Nuclear Information System (INIS)

    Smith, J.V.

    1977-02-01

    The Injector Control System employs an ADC/multiplexer system to convert analogue data from the various beamline elements and supply this to the Controls CAMAC for numeric (Nixie) display. Facilities were incorporated to allow this data to be sent via a line driver to the Diagnostics CAMAC. This report describes the system developed to acquire and log the Nixie data. (author)

  14. Representation of neural networks as Lotka-Volterra systems

    International Nuclear Information System (INIS)

    Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

    1999-01-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

  15. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  16. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  17. The File System Interface is an Anachronism

    OpenAIRE

    Ellard, Daniel

    2003-01-01

    Contemporary file systems implement a set of abstractions and semantics that are suboptimal for many (if not most) purposes. The philosophy of using the simple mechanisms of the file system as the basis for a vast array of higher-level mechanisms leads to inefficient and incorrect implementations. We propose several extensions to the canonical file system model, including explicit support for lock files, indexed files, and resource forks, and the benefit of session semantics for write updates...

  18. The Small Explorer Data System - A data system based on standard interfaces

    Science.gov (United States)

    Smith, Brian S.; Hengemihle, Jerome

    1990-01-01

    The Small Explorer Data System was developed by NASA Goddard Space Flight Center using a 'standard interfaces' approach. Standard interfaces make it adaptable to a wide variety of missions. The paper describes the Small Explorer Data System with particular emphasis on the standard interfaces incorporated in both the hardware and software.

  19. Reservation system with graphical user interface

    KAUST Repository

    Mohamed, Mahmoud A. Abdelhamid

    2012-01-05

    Techniques for providing a reservation system are provided. The techniques include displaying a scalable visualization object, wherein the scalable visualization object comprises an expanded view element of the reservation system depicting information in connection with a selected interval of time and a compressed view element of the reservation system depicting information in connection with one or more additional intervals of time, maintaining a visual context between the expanded view and the compressed view within the visualization object, and enabling a user to switch between the expanded view and the compressed view to facilitate use of the reservation system.

  20. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  1. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  2. 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.

  3. Advanced Power Electronic Interfaces for Distributed Energy Systems Part 1: Systems and Topologies

    Energy Technology Data Exchange (ETDEWEB)

    Kramer, W.; Chakraborty, S.; Kroposki, B.; Thomas, H.

    2008-03-01

    This report summarizes power electronic interfaces for DE applications and the topologies needed for advanced power electronic interfaces. It focuses on photovoltaic, wind, microturbine, fuel cell, internal combustion engine, battery storage, and flywheel storage systems.

  4. Microfluidic systems for stem cell-based neural tissue engineering.

    Science.gov (United States)

    Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

    2016-07-05

    Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

  5. Tecnatom's operation system interfaces and their evolution in time

    International Nuclear Information System (INIS)

    Trueba, Pedro

    1998-01-01

    The author comments the evolution of operation system interfaces produced by the Tecnatom Company, notably for the support in the construction of the Spanish nuclear power plants. A system can typically be divided into a data acquisition system, a central processing system, and a graphical system. The author discusses and comments the main functional applications which are: real time data displays, data analysis functions, and other utilities (file management, data storing, file reloading)

  6. Interface requirements in nuclear medicine devices and systems

    International Nuclear Information System (INIS)

    Maguire, G.Q. Jr.; Brill, A.B.; Noz, M.E.

    1982-01-01

    Interface designs for three nuclear medicine imaging systems, and computer networking strategies proposed for medical imaging departments are presented. Configurations for two positron-emission-tomography devices (PET III and ECAT) and a general-purpose tomography instrument (the UNICON) are analyzed in terms of specific performance parameters. Interface designs for these machines are contrasted in terms of utilization of standard versus custom modules, cost, and ease of modification, upgrade, and support. The requirements of general purpose systems for medical image analysis, display, and archiving, are considered, and a realizable state-of-the-art system is specfied, including a suggested timetable

  7. Surfaces and Interfaces of Magnetoelectric Oxide Systems

    Science.gov (United States)

    Cao, Shi

    Magnetoelectric materials Cr2O3, hexagonal LuFeO 3 and YbFeO3 are studied in this thesis. The surface of chromia (Cr2O3) has a surface electronic structure distinct from the bulk. Our work shows that placing a Cr2O3 single crystal into a single domain state will result in net Cr2O 3 spin polarization at the boundary, even in the presence of a gold overlayer. From the Cr 2p3/2 X-ray magnetic circular dichroism signal, there is clear evidence of interface polarization with overlayers of both Pd and Pt on chromia. Cobalt thin films on Cr2O3(0001) show larger magnetic contrast in magnetic force microscopy indicating enhancement of perpendicular anisotropy induced by Cr2O3. The interfacial charge transfer between mechanically exfoliated few-layer graphene and Cr2O3(0001) surfaces has been investigated showing hole doping of few-layer graphene. Density functional theory calculations furthermore confirm the p-type nature of the graphene on top of chromia, and suggest that the chromia is able to induce a significant carrier spin polarization in the graphene layer. The surface termination and the nominal valence states for hexagonal LuFeO3 thin films were characterized. The stable surface terminates in a Fe-O layer. This is consistent wit the results of density functional calculations. The structural transition at about 1000 °C, from the hexagonal to the orthorhombic phase of LuFeO3, has been investigated in thin films of LuFeO3. The electronic structure for the conduction bands of both hexagonal and orthorhombic LuFeO3 thin films have been measured. Dramatic differences in both the spectral features and the linear dichroism are observed. We have also studied the ferrimagnetism in h-YbFeO3 by measuring the magnetization of Fe and Yb separately. The results directly show antialignment of magnetization of Yb and Fe ions in h-YbFeO3 at low temperature, with an exchange field on Yb of about 17 kOe. All ferrimagnets, by default, are magnetoelectrics. These findings directly

  8. Maintenance Effectiveness and Target Observation System and its ERP Interface

    International Nuclear Information System (INIS)

    Soon, Han Seong; Kim, Gi Yong; Seo, Mi Ro; Jeong, Hun Jong; Choi, Kwang Hee; Hong, Sung Yull

    2005-01-01

    Maintenance effectiveness and target observation system (MENTOS) is a maintenance rule (MR) implementation software for plant personnel to collect, edit, store, and analyze all information required for the MR implementation. Potential users and the developers of MENTOS have decided that MENTOS is implemented in the ERP system of KHNP. This article describes MENTOS briefly and introduces the ERP interface of MENTOS

  9. CAMAC interface module for PACE ADC system

    Energy Technology Data Exchange (ETDEWEB)

    Dalton, C G; Mischke, R E [Los Alamos Scientific Lab., N.Mex. (USA); Scott, D T

    1977-03-15

    This report describes a CAMAC module designed to buffer and transfer data from the Tennelec multiplexed ADC system called PACE to a computer. It can be operated in either of two modes: as an eight-deep, first-in-first-out (FIFO) circular buffer, or in channel mode with a single buffer reserved for each PACE channel.

  10. Interfacing system isolation experience review. Final report, August 1991

    International Nuclear Information System (INIS)

    1991-08-01

    A light water reactor power plant has auxiliary systems interconnected with the reactor coolant system that are not designed for reactor operating pressure. These principally include the shutdown heat removal systems and various emergency core cooling injection systems. There are multiple isolation valves that prevent rector vessel pressure from causing overpressurization in low pressure interfacing systems. Combinations of hardware failures or operational errors are necessary to expose these systems to overpressurization. This experience review provides insights regarding the risk that an auxiliary system might become over pressurized from the reactor system. While analyses show that for the pressures involved the probability of auxiliary system failure is low, the auxiliary system conceivably might fail outside of containment while the plant is at power. Such a potential event has come to be called an interfacing system loss of coolant accident (ISLOCA). This report provides a compilation of occurrences where valve leakage, valve failure, or valve mispositioning played a role in the ability to maintain interfacing system isolation. Seventeen U.S. BWR events, twenty three U.S. PWR events and one foreign event are discussed in the report. Eleven of the U.S. BWR events and ten U.S. PWR events are judged to relate directly to the so-called ISLOCA event in that they fulfilled one or more of the failures necessary to cause an ISLOCA. (author)

  11. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  12. System Integration and Interface Transition Issues.

    Science.gov (United States)

    1977-04-01

    OC - 4- u -O m4 U V L.- I~V 0~ C 0 - i CC 0 .iOC30~i .- ~. C > u uU O! ul Wi 0) i~ LUn CL04) z w 0 CL-0r I.- ~ ~~~~ in0 6 - 2-A 0 ~ 4) 0 zEC u~5. 0...Systems Design and Documentation - An Introduction to the HIPO Method, Van Nostrand Reinhold Co. (1976). [34] Peter Freeman, "Toward Improved Review of...Software Design," Proc. National Computer Conf. 44, AFIPS Press (1975) pp 329-334. [35] Peter G. Neumann, "Software Development & Proofs of Multi-Level

  13. Operator interface to the ORIC control system

    International Nuclear Information System (INIS)

    Ludemann, C.A.; Casstevens, B.J.

    1983-01-01

    The Oak Ridge Isochronous Cyclotron (ORIC) was built in the early 1960s with a hard-wired manual control system. Presently, it serves as a variable-energy heavy-ion cyclotron with an internal ion source, or as an energy booster for the new 25 MV tandem electrostatic accelerator of the Holifield Heavy Ion Facility. One factor which has kept the cyclotron the productive research tool it is today is the gradual transfer of its control functions to a computer-based system beginning in the 1970s. This particular placement of a computer between an accelerator and its operators afforded some unique challenges and opportunities that would not be encountered today. Historically, the transformation began at a time when computers were just beginning to gain acceptance as reliable operational tools. Veteran operators with tens of years of accelerator experience justifiably expressed skepticism that this improvement would aid them, particularly if they had to re-learn how to operate the machine. The confidence of the operators was gained when they realized that one of the primary principles of ergonomics was being upheld. The computer software and hardware was being designed to serve them and not the computer

  14. Advanced human-system interface design review guidelines

    International Nuclear Information System (INIS)

    O'Hara, J.M.

    1990-01-01

    Advanced, computer-based, human-system interface designs are emerging in nuclear power plant (NPP) control rooms. These developments may have significant implications for plant safety in that they will greatly affect the ways in which operators interact with systems. At present, however, the only guidance available to the US Nuclear Regulatory Commission (NRC) for the review of control room-operator interfaces, NUREG-0700, was written prior to these technological changes and is thus not designed to address them. The objective of the project reported in this paper is to develop an Advanced Control Room Design Review Guideline for use in performing human factors reviews of advanced operator interfaces. This guideline will be implemented, in part, as a portable, computer-based, interactive document for field use. The paper describes the overall guideline development methodology, the present status of the document, and the plans for further guideline testing and development. 21 refs., 3 figs

  15. Integrated evolutionary computation neural network quality controller for automated systems

    Energy Technology Data Exchange (ETDEWEB)

    Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  16. Studying the glial cell response to biomaterials and surface topography for improving the neural electrode interface

    Science.gov (United States)

    Ereifej, Evon S.

    Neural electrode devices hold great promise to help people with the restoration of lost functions, however, research is lacking in the biomaterial design of a stable, long-term device. Current devices lack long term functionality, most have been found unable to record neural activity within weeks after implantation due to the development of glial scar tissue (Polikov et al., 2006; Zhong and Bellamkonda, 2008). The long-term effect of chronically implanted electrodes is the formation of a glial scar made up of reactive astrocytes and the matrix proteins they generate (Polikov et al., 2005; Seil and Webster, 2008). Scarring is initiated when a device is inserted into brain tissue and is associated with an inflammatory response. Activated astrocytes are hypertrophic, hyperplastic, have an upregulation of intermediate filaments GFAP and vimentin expression, and filament formation (Buffo et al., 2010; Gervasi et al., 2008). Current approaches towards inhibiting the initiation of glial scarring range from altering the geometry, roughness, size, shape and materials of the device (Grill et al., 2009; Kotov et al., 2009; Kotzar et al., 2002; Szarowski et al., 2003). Literature has shown that surface topography modifications can alter cell alignment, adhesion, proliferation, migration, and gene expression (Agnew et al., 1983; Cogan et al., 2005; Cogan et al., 2006; Merrill et al., 2005). Thus, the goals of the presented work are to study the cellular response to biomaterials used in neural electrode fabrication and assess surface topography effects on minimizing astrogliosis. Initially, to examine astrocyte response to various materials used in neural electrode fabrication, astrocytes were cultured on platinum, silicon, PMMA, and SU-8 surfaces, with polystyrene as the control surface. Cell proliferation, viability, morphology and gene expression was measured for seven days in vitro. Results determined the cellular characteristics, reactions and growth rates of astrocytes

  17. Multi-dimensional design window search system using neural networks in reactor core design

    International Nuclear Information System (INIS)

    Kugo, Teruhiko; Nakagawa, Masayuki

    2000-02-01

    In the reactor core design, many parametric survey calculations should be carried out to decide an optimal set of basic design parameter values. They consume a large amount of computation time and labor in the conventional way. To support directly design work, we investigate a procedure to search efficiently a design window, which is defined as feasible design parameter ranges satisfying design criteria and requirements, in a multi-dimensional space composed of several basic design parameters. We apply the present method to the neutronics and thermal hydraulics fields and develop the multi-dimensional design window search system using it. The principle of the present method is to construct the multilayer neural network to simulate quickly a response of an analysis code through a training process, and to reduce computation time using the neural network without parametric study using analysis codes. The system works on an engineering workstation (EWS) with efficient man-machine interface for pre- and post-processing. This report describes the principle of the present method, the structure of the system, the guidance of the usages of the system, the guideline for the efficient training of neural networks, the instructions of the input data for analysis calculation and so on. (author)

  18. Temporal neural networks and transient analysis of complex engineering systems

    Science.gov (United States)

    Uluyol, Onder

    A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

  19. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  20. Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system

    Directory of Open Access Journals (Sweden)

    Daniel Brüderle

    2009-06-01

    Full Text Available Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated.

  1. Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system.

    Science.gov (United States)

    Brüderle, Daniel; Müller, Eric; Davison, Andrew; Muller, Eilif; Schemmel, Johannes; Meier, Karlheinz

    2009-01-01

    Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated.

  2. DIII-D Neutral Beam control system operator interface

    International Nuclear Information System (INIS)

    Harris, J.J.; Campbell, G.L.

    1993-10-01

    A centralized graphical user interface has been added to the DIII-D Neutral Beam (NB) control systems for status monitoring and remote control applications. This user interface provides for automatic data acquisition, alarm detection and supervisory control of the four NB programmable logic controllers (PLC) as well as the Mode Control PLC. These PLCs are used for interlocking, control and status of the NB vacuum pumping, gas delivery, and water cooling systems as well as beam mode status and control. The system allows for both a friendly user interface as well as a safe and convenient method of communicating with remote hardware that formerly required interns to access. In the future, to enable high level of control of PLC subsystems, complete procedures is written and executed at the touch of a screen control panel button. The system consists of an IBM compatible 486 computer running the FIX DMACS trademark for Windows trademark data acquisition and control interface software, a Texas Instruments/Siemens communication card and Phoenix Digital optical communications modules. Communication is achieved via the TIWAY (Texas Instruments protocol link utilizing both fiber optic communications and a copper local area network (LAN). Hardware and software capabilities will be reviewed. Data and alarm reporting, extended monitoring and control capabilities will also be discussed

  3. Graphical interface for PPDS system based on PAW

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Stolyarskij, Yu.V.

    1992-01-01

    The program interface allowing to display graphically experimental measurements results and evaluations of various physical reactions parameters stored in the REACTIONS database of PPDS system is described. PAW package is used for experimental data visualization. 7 refs.; 2 figs.; 3 tabs

  4. Speech control interface for Eurocontrol’s LINK2000+ system

    Directory of Open Access Journals (Sweden)

    Dan-Cristian ION

    2012-06-01

    Full Text Available This paper continues recent research of the authors, considering the use of speech recognition in air traffic control. It proposes the use of a voice control interface for Eurocontrol’s LINK2000+ system, offering an alternative means to improve air transport safety and efficiency.

  5. A graphical user-interface control system at SRRC

    International Nuclear Information System (INIS)

    Chen, J.S.; Wang, C.J.; Chen, S.J.; Jan, G.J.

    1993-01-01

    A graphical user interface control system of 1.3 GeV synchrotron radiation light source was designed and implemented for the beam transport line (BTL) and storage ring (SR). A modern control technique has been used to implement and control the third generation synchrotron light source. Two level computer hardware configuration, that includes process and console computers as a top level and VME based intelligent local controller as a bottom level, was setup and tested. Both level computers are linked by high speed Ethernet data communication network. A database includes static and dynamic databases as well as access routines were developed. In order to commission and operate the machine friendly, the graphical man machine interface was designed and coded. The graphical user interface (GUI) software was installed on VAX workstations for the BTL and SR at the Synchrotron Radiation Research Center (SRRC). The over all performance has been evaluated at 10Hz update rate. The results showed that the graphical operator interface control system is versatile system and can be implemented into the control system of the accelerator. It will provide the tool to control and monitor the equipments of the radiation light source especially for machine commissioning and operation

  6. An approach to developing user interfaces for space systems

    Science.gov (United States)

    Shackelford, Keith; McKinney, Karen

    1993-08-01

    Inherent weakness in the traditional waterfall model of software development has led to the definition of the spiral model. The spiral model software development lifecycle model, however, has not been applied to NASA projects. This paper describes its use in developing real time user interface software for an Environmental Control and Life Support System (ECLSS) Process Control Prototype at NASA's Marshall Space Flight Center.

  7. Optimized controllers for enhancing dynamic performance of PV interface system

    Directory of Open Access Journals (Sweden)

    Mahmoud A. Attia

    2018-05-01

    Full Text Available The dynamic performance of PV interface system can be improved by optimizing the gains of the Proportional–Integral (PI controller. In this work, gravitational search algorithm and harmony search algorithm are utilized to optimal tuning of PI controller gains. Performance comparison between the PV system with optimized PI gains utilizing different techniques are carried out. Finally, the dynamic behavior of the system is studied under hypothetical sudden variations in irradiance. The examination of the proposed techniques for optimal tuning of PI gains is conducted using MATLAB/SIMULINK software package. The main contribution of this work is investigating the dynamic performance of PV interfacing system with application of gravitational search algorithm and harmony search algorithm for optimal PI parameters tuning. Keywords: Photovoltaic power systems, Gravitational search algorithm, Harmony search algorithm, Genetic algorithm, Artificial intelligence

  8. Time-recovering PCI-AER interface for bio-inspired spiking systems

    Science.gov (United States)

    Paz-Vicente, R.; Linares-Barranco, A.; Cascado, D.; Vicente, S.; Jimenez, G.; Civit, A.

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) inject a sequence of events at some point of the AER structure. This is necessary for testing and debugging complex AER systems. This paper presents a PCI to AER interface, that dispatches a sequence of events received from the PCI bus with embedded timing information to establish when each event will be delivered. A set of specialized states machines has been introduced to recovery the possible time delays introduced by the asynchronous AER bus. On the input channel, the interface capture events assigning a timestamp and delivers them through the PCI bus to MATLAB applications. It has been implemented in real time hardware using VHDL and it has been tested in a PCI-AER board, developed by authors, that includes a Spartan II 200 FPGA. The demonstration hardware is currently capable to send and receive events at a peak rate of 8,3 Mev/sec, and a typical rate of 1 Mev/sec.

  9. User Interface Technology Transfer to NASA's Virtual Wind Tunnel System

    Science.gov (United States)

    vanDam, Andries

    1998-01-01

    Funded by NASA grants for four years, the Brown Computer Graphics Group has developed novel 3D user interfaces for desktop and immersive scientific visualization applications. This past grant period supported the design and development of a software library, the 3D Widget Library, which supports the construction and run-time management of 3D widgets. The 3D Widget Library is a mechanism for transferring user interface technology from the Brown Graphics Group to the Virtual Wind Tunnel system at NASA Ames as well as the public domain.

  10. Functional Interface Considerations within an Exploration Life Support System Architecture

    Science.gov (United States)

    Perry, Jay L.; Sargusingh, Miriam J.; Toomarian, Nikzad

    2016-01-01

    As notional life support system (LSS) architectures are developed and evaluated, myriad options must be considered pertaining to process technologies, components, and equipment assemblies. Each option must be evaluated relative to its impact on key functional interfaces within the LSS architecture. A leading notional architecture has been developed to guide the path toward realizing future crewed space exploration goals. This architecture includes atmosphere revitalization, water recovery and management, and environmental monitoring subsystems. Guiding requirements for developing this architecture are summarized and important interfaces within the architecture are discussed. The role of environmental monitoring within the architecture is described.

  11. Advanced operator interface design for CANDU-3 fuel handling system

    International Nuclear Information System (INIS)

    Arapakota, D.

    1995-01-01

    The Operator Interface for the CANDU 3 Fuel Handling (F/H) System incorporates several improvements over the existing designs. A functionally independent sit-down CRT (cathode-ray tube) based Control Console is provided for the Fuel Handling Operator in the Main Control Room. The Display System makes use of current technology and provides a user friendly operator interface. Regular and emergency control operations can be carried out from this control console. A stand-up control panel is provided as a back-up with limited functionality adequate to put the F/H System in a safe state in case of an unlikely non-availability of the Plant Display System or the F/H Control System'. The system design philosophy, hardware configuration and the advanced display system features are described in this paper The F/H Operator Interface System developed for CANDU 3 can be adapted to CANDU 9 as well as to the existing stations. (author)

  12. Advanced operator interface design for CANDU-3 fuel handling system

    Energy Technology Data Exchange (ETDEWEB)

    Arapakota, D [Atomic Energy of Canada Ltd., Saskatoon, SK (Canada)

    1996-12-31

    The Operator Interface for the CANDU 3 Fuel Handling (F/H) System incorporates several improvements over the existing designs. A functionally independent sit-down CRT (cathode-ray tube) based Control Console is provided for the Fuel Handling Operator in the Main Control Room. The Display System makes use of current technology and provides a user friendly operator interface. Regular and emergency control operations can be carried out from this control console. A stand-up control panel is provided as a back-up with limited functionality adequate to put the F/H System in a safe state in case of an unlikely non-availability of the Plant Display System or the F/H Control System`. The system design philosophy, hardware configuration and the advanced display system features are described in this paper The F/H Operator Interface System developed for CANDU 3 can be adapted to CANDU 9 as well as to the existing stations. (author).

  13. The organizational context of error tolerant interface systems

    International Nuclear Information System (INIS)

    Sepanloo, K.; Meshkati, N.; Kozuh, M.

    1995-01-01

    Human error has been recognized as the main contributor to the occurrence of incidents in large technological systems such as nuclear power plants. Recent researches have concluded that human errors are unavoidable side effects of exploration of acceptable performance during adaptation to the unknown changes in the environment. To assist the operators in coping with unforeseen situations, the innovative error tolerant interface systems have been proposed to provide the operators with opportunities to make hypothetical tests without having to carry them out directly on the plant in potentially irreversible conditions. On the other hand, the degree of success of introduction of any new system into a tightly-coupled complex socio-technological system is known to be a great deal dependent upon the degree of harmony of that system with the organization s framework and attitudes. Error tolerant interface systems with features of simplicity, transparency, error detectability and recoverability provide a forgiving cognition environment where the effects of errors are observable and recoverable. The nature of these systems are likely to be more consistent with flexible and rather plain organizational structures, in which static and punitive concepts of human error are modified on the favour of dynamic and adaptive approaches. In this paper the features of error tolerant interface systems are explained and their consistent organizational structures are explored. (author)

  14. The organizational context of error tolerant interface systems

    Energy Technology Data Exchange (ETDEWEB)

    Sepanloo, K [Nuclear Safety Department, Tehran (Iran, Islamic Republic of); Meshkati, N [Institute of Safety and Systems Management, Los Angeles (United States); Kozuh, M [Josef Stefan Institute, Ljubljana (Slovenia)

    1996-12-31

    Human error has been recognized as the main contributor to the occurrence of incidents in large technological systems such as nuclear power plants. Recent researches have concluded that human errors are unavoidable side effects of exploration of acceptable performance during adaptation to the unknown changes in the environment. To assist the operators in coping with unforeseen situations, the innovative error tolerant interface systems have been proposed to provide the operators with opportunities to make hypothetical tests without having to carry them out directly on the plant in potentially irreversible conditions. On the other hand, the degree of success of introduction of any new system into a tightly-coupled complex socio-technological system is known to be a great deal dependent upon the degree of harmony of that system with the organization s framework and attitudes. Error tolerant interface systems with features of simplicity, transparency, error detectability and recoverability provide a forgiving cognition environment where the effects of errors are observable and recoverable. The nature of these systems are likely to be more consistent with flexible and rather plain organizational structures, in which static and punitive concepts of human error are modified on the favour of dynamic and adaptive approaches. In this paper the features of error tolerant interface systems are explained and their consistent organizational structures are explored. (author) 11 refs.

  15. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  16. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  17. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

  18. User interface to an ICAI system that teaches discrete math

    OpenAIRE

    Calcote, Roy Keith.; Howard, Richard Anthony

    1990-01-01

    Approved for public release; distribution is unlimited. The main thrust of this thesis is the design of a usable Intelligent Computer Aided Instruction (ICAI) user interface that does not use a natural language processor and runs on a personal computer. Discrete Mathematics is the knowledge domain for this project and the Discrete Math Tutor (DMT) is the name of the tutoring system. The DMT will allow the average student to benefit from a tutoring system now and not have to wait until the ...

  19. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

  20. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  1. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

  2. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  4. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

  5. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

    Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.

    1992-09-01

    We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)

  6. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

    The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

  7. Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

    Science.gov (United States)

    Blakely, Tim M.; Miller, Kai J.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.

    2014-01-01

    Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. PMID:25599079

  8. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  9. Advanced human-system interface design review guidelines

    International Nuclear Information System (INIS)

    O'Hara, J.M.

    1990-01-01

    Advanced, computer-based, human-system interface designs are emerging in nuclear power plant control rooms as a result of several factors. These include: (1) incorporation of new systems such as safety parameter display systems, (2) backfitting of current control rooms with new technologies when existing hardware is no longer supported by equipment vendors, and (3) development of advanced control room concepts. Control rooms of the future will be developed almost exclusively with advanced instrumentation and controls based upon digital technology. In addition, the control room operator will be interfacing with more intelligent systems which will be capable of providing information processing support to the operator. These developments may have significant implications for plant safety in that they will greatly affect the operator's role in the system as well as the ways in which he interacts with it. At present, however, the only guidance available to the Nuclear Regulatory Commission (NRC) for the review of control room-operator interfaces is NUREG-0700. It is a document which was written prior to these technological changes and is, therefore, tailored to the technologies used in traditional control rooms. Thus, the present guidance needs to be updated since it is inadequate to serve as the basis for NRC staff review of such advanced or hybrid control room designs. The objective of the project reported in this paper is to develop an Advanced Control Room Design Review Guideline suitable for use in performing human factors reviews of advanced operator interfaces. This guideline will take the form of a portable, interactive, computer-based document that may be conveniently used by an inspector in the field, as well as a text-based document

  10. Project and implementation of the human/system interface laboratory

    International Nuclear Information System (INIS)

    Carvalho, Paulo Victor R. de; Obadia, Isaac Jose; Vidal, Mario Cesar Rodriguez

    2002-01-01

    Analog instrumentation is being increasingly replaced by digital technology in new nuclear power plants, such as Angra III, as well as in existing operating plants, such as Angra I and II, for modernization and life-extension projects. In this new technological environment human factors issues aims to minimize failures in nuclear power plants operation due to human error. It is well known that 30% to 50% of the detected unforeseen problems involve human errors. Presently, human factors issues must be considered during the development of advanced human-system interfaces for the plant. IAEA has considered the importance of those issues and has published TECDOC's and Safety Series Issues on the matter. Thus, there is a need to develop methods and criteria to asses, compare, optimize and validate the human-system interface associated with totally new or hybrid control rooms. Also, the use of computer based operator aids is en evolving area. In order to assist on the development of methods and criteria and to evaluate the effects of the new design concepts and computerized support systems on operator performance, research simulators with advanced control rooms technology, such the IEN's Human System Interface Laboratory, will provide the necessary setting. (author)

  11. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  12. Analysis of the DWPF glass pouring system using neural networks

    International Nuclear Information System (INIS)

    Calloway, T.B. Jr.; Jantzen, C.M.

    1997-01-01

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

  13. Ecological user interface for emergency management decision support systems

    DEFF Research Database (Denmark)

    Andersen, V.

    2003-01-01

    The user interface for decision support systems is normally structured for presenting relevant data for the skilled user in order to allow fast assessment and action of the hazardous situation, or for more complex situations to present the relevant rules and procedures to be followed in order to ...... of this paper is to discuss the possibility of using the same principles for emergency management with the aim of improved performance in complex and unanticipated situations....

  14. Examining the work-home interface: an ecological systems perspective

    OpenAIRE

    MacKinnon, Richard A,

    2012-01-01

    This dissertation outlines a mixed-methods investigation of work-life balance, examining the construct from an ecological systems theory perspective. This necessitated research at the individual, group, organisational and wider societal levels and included three studies: two using quantitative methodology and one using qualitative.\\ud The quantitative phase included two studies that examined the experience of the home-work interface from the perspective of the employee, examining the impact o...

  15. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  16. ATCA-based ATLAS FTK input interface system

    Energy Technology Data Exchange (ETDEWEB)

    Okumura, Yasuyuki [Chicago U., EFI; Liu, Tiehui Ted [Fermilab; Olsen, Jamieson [Fermilab; Iizawa, Tomoya [Waseda U.; Mitani, Takashi [Waseda U.; Korikawa, Tomohiro [Waseda U.; Yorita, Kohei [Waseda U.; Annovi, Alberto [Frascati; Beretta, Matteo [Frascati; Gatta, Maurizio [Frascati; Sotiropoulou, C-L. [Aristotle U., Thessaloniki; Gkaitatzis, Stamatios [Aristotle U., Thessaloniki; Kordas, Konstantinos [Aristotle U., Thessaloniki; Kimura, Naoki [Aristotle U., Thessaloniki; Cremonesi, Matteo [Chicago U., EFI; Yin, Hang [Fermilab; Xu, Zijun [Peking U.

    2015-04-27

    The first stage of the ATLAS Fast TracKer (FTK) is an ATCA-based input interface system, where hits from the entire silicon tracker are clustered and organized into overlapping eta-phi trigger towers before being sent to the tracking engines. First, FTK Input Mezzanine cards receive hit data and perform clustering to reduce data volume. Then, the ATCA-based Data Formatter system will organize the trigger tower data, sharing data among boards over full mesh backplanes and optic fibers. The board and system level design concepts and implementation details, as well as the operation experiences from the FTK full-chain testing, will be presented.

  17. UNIBUS processor interface for a FASTBUS data acquisition system

    International Nuclear Information System (INIS)

    Larwill, M.; Lagerlund, T.D.; Barsotti, E.; Taff, L.M.; Franzen, J.

    1981-01-01

    Current work on a FASTBUS data acquisition system at Fermilab is described. The system will consist of three pieces of FASTBUS hardware: a UNIBUS processor interface (UPI), a dual-ported bulk memory, and a FASTBUS ''event builder'' (i.e., data acquisition processor). Primary efforts have been on specifying and constructing a UPI. The present specification includes capability for all basic FASTBUS operations, including list processing of consecutive FASTBUS operations. Some possible FASTBUS data acquisition system architectures employing the UPI are discussed along with some detailed specifications of the UPI itself

  18. SOME EXAMPLES OF APPLIED SYSTEMS WITH SPEECH INTERFACE

    Directory of Open Access Journals (Sweden)

    V. A. Zhitko

    2017-01-01

    Full Text Available Three examples of applied systems with a speech interface are considered in the article. The first two of these provide the end user with the opportunity to ask verbally the question and to hear the response from the system, creating an addition to the traditional I / O via the keyboard and computer screen. The third example, the «IntonTrainer» system, provides the user with the possibility of voice interaction and is designed for in-depth self-learning of the intonation of oral speech.

  19. ATCA-based ATLAS FTK input interface system

    CERN Document Server

    Okumura, Y; The ATLAS collaboration; Olsen, J; Iizawa, T; Mitani, T; Korikawa, T; Yorita, K; Annovi, A; Beretta, M; Gatta, M; Sotiropoulou, C; Gkaitatzis, S; Kordas, K; Kimura, N; Cremonesi, M; Yin, H; Xu, Z

    2014-01-01

    The first stage of the ATLAS Fast TracKer (FTK) is an ATCA-based input interface system, where hits from the entire silicon tracker must be clustered and organized into overlapping eta-phi trigger towers before being sent to the tracking processors. First, FTK Input Mezzanine cards receive hit data and perform clustering to reduce data volume. Then, the ATCA-based Data Formatter system will organize the trigger tower data, sharing data among boards over a full-mesh backplane. The board and system level performance studies and implementation details, as well as the operation experiences from the FTK full-chain testing, will be presented.

  20. Neural Computations in a Dynamical System with Multiple Time Scales.

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

  1. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  2. Failure Propagation Modeling and Analysis via System Interfaces

    Directory of Open Access Journals (Sweden)

    Lin Zhao

    2016-01-01

    Full Text Available Safety-critical systems must be shown to be acceptably safe to deploy and use in their operational environment. One of the key concerns of developing safety-critical systems is to understand how the system behaves in the presence of failures, regardless of whether that failure is triggered by the external environment or caused by internal errors. Safety assessment at the early stages of system development involves analysis of potential failures and their consequences. Increasingly, for complex systems, model-based safety assessment is becoming more widely used. In this paper we propose an approach for safety analysis based on system interface models. By extending interaction models on the system interface level with failure modes as well as relevant portions of the physical system to be controlled, automated support could be provided for much of the failure analysis. We focus on fault modeling and on how to compute minimal cut sets. Particularly, we explore state space reconstruction strategy and bounded searching technique to reduce the number of states that need to be analyzed, which remarkably improves the efficiency of cut sets searching algorithm.

  3. Neural network-based expert system for severe accident management

    International Nuclear Information System (INIS)

    Klopp, G.T.; Silverman, E.B.

    1992-01-01

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway at Commonwealth Edison Company (CECo). Phase I successfully demonstrated the feasibility of Artificial Neural Networks to support several of the objectives of severe accident management. Simulated accident scenarios were generated by the Modular Accident Analysis Program (MAAP) code currently in use by CECo as part of their Individual Plant Evaluations (IPE)/Accident Management Program. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. The results of this work would form the foundation of a demonstration system which included expert system performance features. These capabilities included the ability to: (1) Predict the time available prior to support plate (and reactor vessel) failure; (2) Calculate the time remaining until recovery actions were too late to prevent core damage; (3) Predict future parameter values of each of the MAAP parameter variables; and (4) Detect simulated sensor failure and provide best-value estimates for further processing in the presence of a sensor failure. A variety of accident scenarios for the Zion and Dresden plants were used to train and test the neural network expert system. These included large and small break LOCAs as well as a range of transient events. 3 refs., 1 fig., 1 tab

  4. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  5. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  6. A neural interface provides long-term stable natural touch perception.

    Science.gov (United States)

    Tan, Daniel W; Schiefer, Matthew A; Keith, Michael W; Anderson, James Robert; Tyler, Joyce; Tyler, Dustin J

    2014-10-08

    Touch perception on the fingers and hand is essential for fine motor control, contributes to our sense of self, allows for effective communication, and aids in our fundamental perception of the world. Despite increasingly sophisticated mechatronics, prosthetic devices still do not directly convey sensation back to their wearers. We show that implanted peripheral nerve interfaces in two human subjects with upper limb amputation provided stable, natural touch sensation in their hands for more than 1 year. Electrical stimulation using implanted peripheral nerve cuff electrodes that did not penetrate the nerve produced touch perceptions at many locations on the phantom hand with repeatable, stable responses in the two subjects for 16 and 24 months. Patterned stimulation intensity produced a sensation that the subjects described as natural and without "tingling," or paresthesia. Different patterns produced different types of sensory perception at the same location on the phantom hand. The two subjects reported tactile perceptions they described as natural tapping, constant pressure, light moving touch, and vibration. Changing average stimulation intensity controlled the size of the percept area; changing stimulation frequency controlled sensation strength. Artificial touch sensation improved the subjects' ability to control grasping strength of the prosthesis and enabled them to better manipulate delicate objects. Thus, electrical stimulation through peripheral nerve electrodes produced long-term sensory restoration after limb loss. Copyright © 2014, American Association for the Advancement of Science.

  7. MRS [monitored retrievable storage] to transportation system interfaces

    International Nuclear Information System (INIS)

    Row, T.H.; Croff, A.G.

    1987-01-01

    In March 1987, the US Department of Energy presented to Congress the proposal to construct and operate a facility for the monitored retrievable storage (MRS) of spent fuel at a site on the Clinch River in the Roane County portions of Oak Ridge. In discussing the MRS to Transportation System Interfaces, the authors provide a blending of the technical and institutional issues, for they do not believe the solutions to success of this enterprise lie wholly in one area. The authors cover: early chronology of the MRS; comparison of total-system life cycle cost estimates of the authorized system and improved-performance system (i.e., the system that includes a facility for MRS); transportation costs resulting from shipping, security and cask; assumptions for dedicated rail transport from MRS to repository; and significant results from the Total System Life Cycle Cost (TSLCC) analysis of the improved performance system. (AT)

  8. Interface Management for a NASA Flight Project Using Model-Based Systems Engineering (MBSE)

    Science.gov (United States)

    Vipavetz, Kevin; Shull, Thomas A.; Infeld, Samatha; Price, Jim

    2016-01-01

    The goal of interface management is to identify, define, control, and verify interfaces; ensure compatibility; provide an efficient system development; be on time and within budget; while meeting stakeholder requirements. This paper will present a successful seven-step approach to interface management used in several NASA flight projects. The seven-step approach using Model Based Systems Engineering will be illustrated by interface examples from the Materials International Space Station Experiment-X (MISSE-X) project. The MISSE-X was being developed as an International Space Station (ISS) external platform for space environmental studies, designed to advance the technology readiness of materials and devices critical for future space exploration. Emphasis will be given to best practices covering key areas such as interface definition, writing good interface requirements, utilizing interface working groups, developing and controlling interface documents, handling interface agreements, the use of shadow documents, the importance of interface requirement ownership, interface verification, and product transition.

  9. Nonlinear dynamical system approaches towards neural prosthesis

    International Nuclear Information System (INIS)

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-01-01

    An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

  10. Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates

    Science.gov (United States)

    Malaga, Karlo A.; Schroeder, Karen E.; Patel, Paras R.; Irwin, Zachary T.; Thompson, David E.; Bentley, J. Nicole; Lempka, Scott F.; Chestek, Cynthia A.; Patil, Parag G.

    2016-02-01

    Objective. We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model. Approach. A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data. Histology was obtained seventeen weeks post-implantation to measure gliosis. Main results. From week 1-3, mean impedance and amplitude increased at rates of 115.8 kΩ/week and 23.1 μV/week, respectively. This initial ramp up in impedance and amplitude was observed across all arrays, and is consistent with biofouling (increasing interface resistivity) and edema clearing (increasing tissue resistivity), respectively, in the model. Beyond week 3, the trends leveled out. Histology showed that thin scars formed around the electrodes. In the model, scarring could not match the in vivo data. However, a thin interface layer at the electrode tip could. Despite having a large effect on impedance, interface resistivity did not have a noticeable effect on amplitude. Significance. This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the

  11. Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

    Science.gov (United States)

    2010-09-24

    system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

  12. Sympathetic neural modulation of the immune system

    International Nuclear Information System (INIS)

    Madden, K.S.

    1989-01-01

    One route by which the central nervous system communicates with lymphoid organs in the periphery is through the sympathetic nervous system (SNS). To study SNS regulation of immune activity in vivo, selective removal of peripheral noradrenergic nerve fibers was achieved by administration of the neurotoxic drug, 6-hydroxydopamine (6-OHDA), to adult mice. To assess SNS influence on lymphocyte proliferation in vitro, uptake of 125 iododeoxyuridine ( 125 IUdR), a DNA precursor, was measured following 6-OHDA treatment. Sympathectomy prior to epicutaneous immunization with TNCB did not alter draining lymph nodes (LN) cell proliferation, whereas 6-OHDA treatment before footpad immunization with KLH reduced DNA synthesis in popliteal LN by 50%. In mice which were not deliberately immunized, sympathectomy stimulated 125 IUdR uptake inguinal and axillary LN, spleen, and bone marrow. In vitro, these LN and spleen cells exhibited decreased proliferation responses to the T cell mitogen, concanavalin A (Con A), whereas lipopolysaccharide (LPS)-stimulated IgG secretion was enhanced. Studies examining 51 Cr-labeled lymphocyte trafficking to LN suggested that altered cell migration may play a part in sympathectomy-induced changes in LN cell function

  13. Interface methods for using intranet portal organizational memory information system.

    Science.gov (United States)

    Ji, Yong Gu; Salvendy, Gavriel

    2004-12-01

    In this paper, an intranet portal is considered as an information infrastructure (organizational memory information system, OMIS) supporting organizational learning. The properties and the hierarchical structure of information and knowledge in an intranet portal OMIS was identified as a problem for navigation tools of an intranet portal interface. The problem relates to navigation and retrieval functions of intranet portal OMIS and is expected to adversely affect user performance, satisfaction, and usefulness. To solve the problem, a conceptual model for navigation tools of an intranet portal interface was proposed and an experiment using a crossover design was conducted with 10 participants. In the experiment, a separate access method (tabbed tree tool) was compared to an unified access method (single tree tool). The results indicate that each information/knowledge repository for which a user has a different structural knowledge should be handled separately with a separate access to increase user satisfaction and the usefulness of the OMIS and to improve user performance in navigation.

  14. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  15. Design of groundwater pollution expert system: forward chaining and interfacing

    International Nuclear Information System (INIS)

    Mongkon Ta-oun; Mohamed Daud; Mohd Zohadie Bardaie; Shamshuddin Jusop

    2000-01-01

    The groundwater pollution expert system (GWPES was developed by C Language Integrate Production System (CLEPS). The control techniques of this system consider some conclusion and then attempts to prove it by searching for supportive information from the database. The inference process goes in forward chaining of this system such as predicting groundwater pollution vulnerability, predicting the effect of nitrogen fertiliser, agricultural impact and project development on groundwater pollution potential. In GWPES, forward chaining system begins with a matching of inputs with the existing database of groundwater environment and activities impact of the project development. While, interaction between an expert system and user is conducted in simple English language. The interaction is highly interactive. A basis design with simple Graphic User Interface (GUI) to input data and by asking simple questions. (author)

  16. Control system user interface for accelerator commissioning and operation

    International Nuclear Information System (INIS)

    Dobrott, D.; Keeley, D.; Kolte, G.; Mikic, Z.; Lee, M.; Corbett, J.; Howry, S.; King, A.

    1991-01-01

    An Interactive Accelerator Interface Module (AIM) has been developed in a workstation environment for the purposes of assisting in the commissioning and operation of any storage ring/collider system. The function of AIM is to integrate modeling and simulation codes into accelerator and beamline control systems for the purpose of rapid on-line data analysis and error-correction, resulting in significant time-saving. A system dependent module provides for the translation of specific control system data files to appropriate input format for application programs within AIM. Interactive screen graphics, including system function diagrams, menus, beamline element status and update information are standard in AIM. AIM is currently connected to the Stanford Linear Collider (SLC) control system, but is easily transportable to other facilities. This paper describes the development of AIM and its applications on SLC

  17. Acquisition System and Detector Interface for Power Pulsed Detectors

    CERN Document Server

    Cornat, R

    2012-01-01

    A common DAQ system is being developed within the CALICE collaboration. It provides a flexible and scalable architecture based on giga-ethernet and 8b/10b serial links in order to transmit either slow control data, fast signals or read out data. A detector interface (DIF) is used to connect detectors to the DAQ system based on a single firmware shared among the collaboration but targeted on various physical implementations. The DIF allows to build, store and queue packets of data as well as to control the detectors providing USB and serial link connectivity. The overall architecture is foreseen to manage several hundreds of thousands channels.

  18. Brain-computer interface after nervous system injury.

    Science.gov (United States)

    Burns, Alexis; Adeli, Hojjat; Buford, John A

    2014-12-01

    Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. © The Author(s) 2014.

  19. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  20. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  1. Molecular tailoring of interfaces for thin film on substrate systems

    Science.gov (United States)

    Grady, Martha Elizabeth

    Thin film on substrate systems appear most prevalently within the microelectronics industry, which demands that devices operate in smaller and smaller packages with greater reliability. The reliability of these multilayer film systems is strongly influenced by the adhesion of each of the bimaterial interfaces. During use, microelectronic components undergo thermo-mechanical cycling, which induces interfacial delaminations leading to failure of the overall device. The ability to tailor interfacial properties at the molecular level provides a mechanism to improve thin film adhesion, reliability and performance. This dissertation presents the investigation of molecular level control of interface properties in three thin film-substrate systems: photodefinable polyimide films on passivated silicon substrates, self-assembled monolayers at the interface of Au films and dielectric substrates, and mechanochemically active materials on rigid substrates. For all three materials systems, the effect of interfacial modifications on adhesion is assessed using a laser-spallation technique. Laser-induced stress waves are chosen because they dynamically load the thin film interface in a precise, noncontacting manner at high strain rates and are suitable for both weak and strong interfaces. Photodefinable polyimide films are used as dielectrics in flip chip integrated circuit packages to reduce the stress between silicon passivation layers and mold compound. The influence of processing parameters on adhesion is examined for photodefinable polyimide films on silicon (Si) substrates with three different passivation layers: silicon nitride (SiNx), silicon oxynitride (SiOxNy), and the native silicon oxide (SiO2). Interfacial strength increases when films are processed with an exposure step as well as a longer cure cycle. Additionally, the interfacial fracture energy is assessed using a dynamic delamination protocol. The high toughness of this interface (ca. 100 J/m2) makes it difficult

  2. Neural networks for feedback feedforward nonlinear control systems.

    Science.gov (United States)

    Parisini, T; Zoppoli, R

    1994-01-01

    This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

  3. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  4. 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.

  5. 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.

  6. Semi-empirical neural network models of controlled dynamical systems

    Directory of Open Access Journals (Sweden)

    Mihail V. Egorchev

    2017-12-01

    Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

  7. Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

    2004-01-01

    In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

  8. Distributed photovoltaic systems - Addressing the utility interface issues

    Science.gov (United States)

    Firstman, S. I.; Vachtsevanos, G. J.

    This paper reviews work conducted in the United States on the impact of dispersed photovoltaic sources upon utility operations. The photovoltaic (PV) arrays are roof-mounted on residential houses and connected, via appropriate power conditioning equipment, to the utility grid. The presence of such small (4-6 Kw) dispersed generators on the distribution network raises questions of a technical, economic and institutional nature. After a brief identification of utility interface issues, the paper addresses such technical concerns as protection of equipment and personnel safety, power quality and utility operational stability. A combination of experimental and analytical approaches has been adopted to arrive at solutions to these problems. Problem areas, under various PV system penetration scenarios, are identified and conceptual designs of protection and control equipment and operating policies are developed so that system reliability is maintained while minimizing capital costs. It is hoped that the resolution of balance-of-system and grid interface questions will ascertain the economic viability of photovoltaic systems and assist in their widespread utilization in the future.

  9. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  10. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  11. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  12. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  13. Operator interface design considerations for a PACS information management system

    Science.gov (United States)

    Steinke, James E.; Nabijee, Kamal H.; Freeman, Rick H.; Prior, Fred W.

    1990-08-01

    As prototype PACS grow into fully digital departmental and hospital-wide systems, effective information storage and retrieval mechanisms become increasingly important. Thus far, designers of PACS workstations have concentrated on image communication and display functionality. The new challenge is to provide appropriate operator interface environments to facilitate information retrieval. The "Marburg Model" 1 provides a detailed analysis of the functions, control flows and data structures used in Radiology. It identifies a set of "actors" who perform information manipulation functions. Drawing on this model and its associated methodology it is possible to identify four modes of use of information systems in Radiology: Clinical Routine, Research, Consultation, and Administration. Each mode has its own specific access requirements and views of information. An operator interface strategy appropriate for each mode will be proposed. Clinical Routine mode is the principal concern of PACS primary diagnosis workstations. In a full PACS implementation, such workstations must provide a simple and consistent navigational aid for the on-line image database, a local work list of cases to be reviewed, and easy access to information from other hospital information systems. A hierarchical method of information access is preferred because it provides the ability to start at high-level entities and iteratively narrow the scope of information from which to select subsequent operations. An implementation using hierarchical, nested software windows which fulfills such requirements shall be examined.

  14. Interactive Editing and Cataloging Interfaces for Modern Digital Library Systems

    CERN Document Server

    Raae, L C; Helstrup, H

    2009-01-01

    The next-generation High Energy Physics information system, INSPIRE, is being built by combining the content from the successful SPIRES database of bibliographic information with the CDS Invenio software being developed at CERN, an open-source platform for large digital library systems. The project is a collaboration between four major particle physics laboratories in Europe and the U.S. New tools are being developed to enable the global cooperation between catalogers at these labs. The BibEdit module will provide a central interface for the editing, enrichment, correction and verification of a record on its way into the system, by processing and presenting data from several supporting modules to the cataloger. The objective is to minimize the time and actions needed by the cataloger to process the record. To create a fast and powerful web application we make use of modern AJAX technology to create a dynamic and responsive user interface, where server communication happens in the background without delaying t...

  15. TGeoCad: an Interface between ROOT and CAD Systems

    International Nuclear Information System (INIS)

    Luzzi, C; Carminati, F

    2014-01-01

    In the simulation of High Energy Physics experiment a very high precision in the description of the detector geometry is essential to achieve the required performances. The physicists in charge of Monte Carlo Simulation of the detector need to collaborate efficiently with the engineers working at the mechanical design of the detector. Often, this collaboration is made hard by the usage of different and incompatible software. ROOT is an object-oriented C++ framework used by physicists for storing, analyzing and simulating data produced by the high-energy physics experiments while CAD (Computer-Aided Design) software is used for mechanical design in the engineering field. The necessity to improve the level of communication between physicists and engineers led to the implementation of an interface between the ROOT geometrical modeler used by the virtual Monte Carlo simulation software and the CAD systems. In this paper we describe the design and implementation of the TGeoCad Interface that has been developed to enable the use of ROOT geometrical models in several CAD systems. To achieve this goal, the ROOT geometry description is converted into STEP file format (ISO 10303), which can be imported and used by many CAD systems

  16. TGeoCad: an Interface between ROOT and CAD Systems

    Science.gov (United States)

    Luzzi, C.; Carminati, F.

    2014-06-01

    In the simulation of High Energy Physics experiment a very high precision in the description of the detector geometry is essential to achieve the required performances. The physicists in charge of Monte Carlo Simulation of the detector need to collaborate efficiently with the engineers working at the mechanical design of the detector. Often, this collaboration is made hard by the usage of different and incompatible software. ROOT is an object-oriented C++ framework used by physicists for storing, analyzing and simulating data produced by the high-energy physics experiments while CAD (Computer-Aided Design) software is used for mechanical design in the engineering field. The necessity to improve the level of communication between physicists and engineers led to the implementation of an interface between the ROOT geometrical modeler used by the virtual Monte Carlo simulation software and the CAD systems. In this paper we describe the design and implementation of the TGeoCad Interface that has been developed to enable the use of ROOT geometrical models in several CAD systems. To achieve this goal, the ROOT geometry description is converted into STEP file format (ISO 10303), which can be imported and used by many CAD systems.

  17. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  18. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  19. Olfactory systems and neural circuits that modulate predator odor fear

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

    Full Text Available When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS and accessory olfactory systems (AOS detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate

  20. Olfactory systems and neural circuits that modulate predator odor fear

    Science.gov (United States)

    Takahashi, Lorey K.

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  1. Development of guidelines to review advanced human-system interfaces

    International Nuclear Information System (INIS)

    O'Hara, J.M.

    1993-01-01

    Advanced control rooms (ACRs) will utilize advanced human-system interface (HSI) technologies that may have significant implications for plant safety in that they will affect the operator's overall role in the system, the method of information presentation, and the ways in which operators interact with the system. The US Nuclear Regulatory Commission (NRC) reviews the HSI aspects of control rooms to ensure that they are designed to good human factors engineering principles and that operator performance and reliability are appropriately supported to protect public health and safety. The principal guidance available to the NRC, however, was developed more than 10 yr ago, considerably prior to these technological changes. Accordingly, the human factors guidance needs to be updated to serve as the basis for NRC review of these advanced designs. The purpose of this paper is to discuss the development, evaluation, and current status of the Advanced HSI Design Review Guideline

  2. Development of guidelines to review advanced human-system interfaces

    International Nuclear Information System (INIS)

    O'Hara, J.M.

    1993-01-01

    Advanced control rooms (ACRS) will utilize advanced human-system interface (HSI) technologies that may have significant implications for plant safety in that they will affect the operators overall role in the system, the method of information presentation, and the ways in which operators interact with the system. The US Nuclear Regulatory Commission (NRC) reviews the HSI aspects of control rooms to ensure that they are designed to good human factors engineering principles and that operator performance and reliability are appropriately supported in order to protect public health and safety. The principal guidance available to the NRC, however, was developed more than ten years ago, well prior to these technological changes. Accordingly, the human factors guidance needs to be updated to serve as the basis for NRC review of these advanced designs. The purpose of this paper is to discuss the development, evaluation, and current status of the Advanced HSI Design Review Guideline, hereafter referred to as the ''Guideline.''

  3. Specific interface area in a thin layer system of two immiscible liquids with vapour generation at the contact interface

    Science.gov (United States)

    Pimenova, Anastasiya V.; Gazdaliev, Ilias M.; Goldobin, Denis S.

    2017-06-01

    For well-stirred multiphase fluid systems the mean interface area per unit volume, or “specific interface area” SV, is a significant characteristic of the system state. In particular, it is important for the dynamics of systems of immiscible liquids experiencing interfacial boiling. We estimate the value of parameter SV as a function of the heat influx {\\dot{Q}}V to the system or the average system overheat above the interfacial boiling point. The derived results can be reformulated for the case of an endothermic chemical reaction between two liquid reagents with the gaseous form of one of the reaction products. The final results are restricted to the case of thin layers, where the potential gravitational energy of bubbles leaving the contact interface is small compared to their surface tension energy.

  4. System identification of an unmanned quadcopter system using MRAN neural

    Science.gov (United States)

    Pairan, M. F.; Shamsudin, S. S.

    2017-12-01

    This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

  5. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1991-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

  6. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1992-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

  7. Design and Evaluation of Human System Interfaces (HSIs)

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2003-07-01

    In the safe operation of nuclear power plants and other complex process industries the performance of the control room crews plays an important role. In this respect a well-functioning and well-designed Human-System Interface (HSI) is crucial for safe and efficient operation of the plant. It is therefore essential that the design, development and evaluation of both control rooms and HSI-solutions are conducted in a well-structured way, applying sound human factors principles and guidelines in all phases of the HSI development process. Many nuclear power plants around the world are currently facing major modernisation of their control rooms. In this process computerised, screen-based HSIs replace old conventional operator interfaces. In new control rooms, both in the nuclear field and in other process industries, fully digital, screen-based control rooms are becoming the standard. It is therefore of particular importance to address the design and evaluation of screen-based HSIs in a systematic and consistent way in order to arrive at solutions which take proper advantage of the possibilities for improving operator support through the use of digital, screen-based HSIs, at the same time avoiding pitfalls and problems in the use of this technology. The Halden Reactor Project, in cooperation with the OECD Nuclear Energy Agency, organised an International Summer School on ''Design and Evaluation of Human-System Interfaces (HSIs)'' in Halden, Norway in the period August 25th - 29th, 2003. The Summer School addressed the different steps in design, development and evaluation of HSIs, and the human factors principles, standards and guidelines which should be followed in this process. The lectures comprised both theoretical background, as well as examples of good and bad HSI design, thereby providing practical advice in design and evaluation of operator interfaces and control room solutions to the participants in the Summer School. This CD contains the

  8. Design and Evaluation of Human System Interfaces (HSIs)

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2003-07-01

    In the safe operation of nuclear power plants and other complex process industries the performance of the control room crews plays an important role. In this respect a well-functioning and well-designed Human-System Interface (HSI) is crucial for safe and efficient operation of the plant. It is therefore essential that the design, development and evaluation of both control rooms and HSI-solutions are conducted in a well-structured way, applying sound human factors principles and guidelines in all phases of the HSI development process. Many nuclear power plants around the world are currently facing major modernisation of their control rooms. In this process computerised, screen-based HSIs replace old conventional operator interfaces. In new control rooms, both in the nuclear field and in other process industries, fully digital, screen-based control rooms are becoming the standard. It is therefore of particular importance to address the design and evaluation of screen-based HSIs in a systematic and consistent way in order to arrive at solutions which take proper advantage of the possibilities for improving operator support through the use of digital, screen-based HSIs, at the same time avoiding pitfalls and problems in the use of this technology. The Halden Reactor Project, in cooperation with the OECD Nuclear Energy Agency, organised an International Summer School on ''Design and Evaluation of Human-System Interfaces (HSIs)'' in Halden, Norway in the period August 25th - 29th, 2003. The Summer School addressed the different steps in design, development and evaluation of HSIs, and the human factors principles, standards and guidelines which should be followed in this process. The lectures comprised both theoretical background, as well as examples of good and bad HSI design, thereby providing practical advice in design and evaluation of operator interfaces and control room solutions to the participants in the Summer School. This CD contains the Proceedings of the

  9. Design and Evaluation of Human System Interfaces (HSIs)

    International Nuclear Information System (INIS)

    2003-01-01

    In the safe operation of nuclear power plants and other complex process industries the performance of the control room crews plays an important role. In this respect a well-functioning and well-designed Human-System Interface (HSI) is crucial for safe and efficient operation of the plant. It is therefore essential that the design, development and evaluation of both control rooms and HSI-solutions are conducted in a well-structured way, applying sound human factors principles and guidelines in all phases of the HSI development process. Many nuclear power plants around the world are currently facing major modernisation of their control rooms. In this process computerised, screen-based HSIs replace old conventional operator interfaces. In new control rooms, both in the nuclear field and in other process industries, fully digital, screen-based control rooms are becoming the standard. It is therefore of particular importance to address the design and evaluation of screen-based HSIs in a systematic and consistent way in order to arrive at solutions which take proper advantage of the possibilities for improving operator support through the use of digital, screen-based HSIs, at the same time avoiding pitfalls and problems in the use of this technology. The Halden Reactor Project, in cooperation with the OECD Nuclear Energy Agency, organised an International Summer School on ''Design and Evaluation of Human-System Interfaces (HSIs)'' in Halden, Norway in the period August 25th - 29th, 2003. The Summer School addressed the different steps in design, development and evaluation of HSIs, and the human factors principles, standards and guidelines which should be followed in this process. The lectures comprised both theoretical background, as well as examples of good and bad HSI design, thereby providing practical advice in design and evaluation of operator interfaces and control room solutions to the participants in the Summer School. This CD contains the Proceedings of the

  10. Grid-interfacing converter systems with enhanced voltage quality for microgrid application : concept and implementation

    NARCIS (Netherlands)

    Wang, F.; Duarte, J.L.; Hendrix, M.A.M.

    2011-01-01

    Grid-interfacing converter systems with enhanced voltage quality are proposed for microgrid application in this paper. By adapting the conventional series-parallel structure, a group of grid-interfacing system topologies are proposed for the purpose of interfacing local generation/microgrid to the

  11. RoboCon: Operator interface for robotic applications. Final report: RoboCon electrical interfacing -- system architecture, and Interfacing NDDS and LabView

    Energy Technology Data Exchange (ETDEWEB)

    Schempf, H.

    1998-04-30

    The first appendix contains detailed specifications of the electrical interfacing employed in Robocon. This includes all electrical signals and power requirement descriptions up to and including the interface entry points for external robots and systems. The reader is first presented with an overview of the overall Robocon electrical system, followed by sub-sections describing each module in detail. The appendices contain listings of power requirements and the electrical connectors and cables used, followed by an overall electrical system diagram. Custom electronics employed are also described. The Network Data Delivery Service (NDDS) is a real-time dissemination communications architecture which allows nodes on a network to publish data and subscribe to data published by other nodes while remaining anonymous. The second appendix explains how to facilitate a seamless interface between NDDS and LabView and provides sample source code used to implement an NDDS consumer which writes a string to a socket.

  12. Report on a comprehensive research study (home welfare apparatus system - interface); Sogo chosa kenkyu (zaitaku fukushi kiki system - interface) hokokusho

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-03-01

    In the light of the increasing role of welfare at home with the advance of the aging society, the paper conducted an investigational study on the R and D of the home welfare apparatus system - interface. In the study, making the most of the leading home care apparatus systems (welfare technohouses) installed at seven places in the country, the paper carried out a stay experiment on how the life is in the welfare house into which home welfare apparatus is integrated, and an experiment to assess biological data on aged people. Especially as to the support apparatus used for smooth life motions in houses such as movement, excretion and bathing, examined were the linkage with house bodies, care apparatus used, mutual interface with welfare apparatus, etc. By the experiments to assess these home welfare apparatus, an analytical study was conducted on the points to be improved in welfare apparatus and housing equipment, and at the same time on the course of the research/development. Concerning a system for the research study, a research promotion committee was established in Technology Research Association of Medical and Welfare Apparatus, the members of which are learned persons from the industrial circle, the government and universities. 111 figs., 36 tabs.

  13. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  14. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  15. 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.

  16. Optimizing Markovian modeling of chaotic systems with recurrent neural networks

    International Nuclear Information System (INIS)

    Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de

    2008-01-01

    In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included

  17. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  18. Interfacing system LOCA risk assessment: Methodology and application

    International Nuclear Information System (INIS)

    Galyean, W.J.; Schroeher, J.A.; Hanson, D.J.

    1991-01-01

    The United States Nuclear Regulatory Commission (NRC) is sponsoring a research program to develop an improved understanding of the human factors hardware, and accident consequence issues that dominate the risk from an Interfacing Systems Loss-of-Coolant Accident (ISLOCA) at a nuclear power plant. To accomplish this program, a methodology has been developed for estimating the core damage frequency and risk associated with an ISLOCA. The steps of the methodology are described with emphasis on one step which is unique, estimation of the probability of rupture of the low pressure systems. A trial application of the methodology was made for a Pressurized Water Reactor (PWR). The results are believed to be plant specific and indicate that human errors during startup and shutdown could be significant contributors to ISLOCA risk at the plant evaluated. 10 refs

  19. Man--machine interface issues for space nuclear power systems

    International Nuclear Information System (INIS)

    Nelson, W.R.; Haugset, K.

    1991-01-01

    The deployment of nuclear reactors in space necessitates an entirely new set of guidelines for the design of the man--machine interface (MMI) when compared to earth-based applications such as commerical nuclear power plants. Although the design objectives of earth- and space-based nuclear power systems are the same, that is, to produce electrical power, the differences in the application environments mean that the operator's role will be significantly different for space-based systems. This paper explores the issues associated with establishing the necessary MMI guidelines for space nuclear power systems. The generic human performance requirements for space-based systems are described, and the operator roles that are utilized for the operation of current and advanced earth-based reactors are briefly summarized. The development of a prototype advanced control room, the Integrated Surveillance and Control System (ISACS) at the Organization for Economic Cooperation and Development (OECD) Halden Reactor Project is introduced. Finally, preliminary ideas for the use of the ISACS system as a test bed for establishing MMI guidelines for space nuclear systems are presented

  20. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  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. Evaluation of a Compact Hybrid Brain-Computer Interface System

    Directory of Open Access Journals (Sweden)

    Jaeyoung Shin

    2017-01-01

    Full Text Available We realized a compact hybrid brain-computer interface (BCI system by integrating a portable near-infrared spectroscopy (NIRS device with an economical electroencephalography (EEG system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA and baseline (BL tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

  3. Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals

    Directory of Open Access Journals (Sweden)

    Patricia Fernández

    2010-12-01

    Full Text Available This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System, a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES and, as a novelty, the myomechanic signals (MMS. In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

  4. Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals.

    Science.gov (United States)

    de la Rosa, Ramon; Alonso, Alonso; Carrera, Albano; Durán, Ramon; Fernández, Patricia

    2010-01-01

    This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

  5. TA-55 facility control system upgrade project - human-system interface functional requirements

    International Nuclear Information System (INIS)

    Atkins, W.H.; Pope, N.G.; Turner, W.J.; Brown, R.E.

    1995-11-01

    The functional requirements for that part of the Technical Area (TA)-55 Operations Center Upgrade Project that involves the human-system interface (HSI) are described in this document. The upgrade project seeks to replace completely the center's existing computerized data acquisition and display system, which consists of the field multiplexer units, Data General computer systems, and associated peripherals and software. The upgrade project has two parts-the Facility Data Acquisition Interface System (FDAIS) and the HSI. The HSI comprises software and hardware to provide a high-level graphical operator interface to the data acquisition system, as well as data archiving, alarm annunciation, and logging. The new system will be built with modern, commercially available components; it will improve reliability and maintainability, and it can be expanded for future needs

  6. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  7. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  8. EDITORIAL: Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface

    Science.gov (United States)

    Fins, Joseph J.; MD; FACP

    2009-10-01

    intrusions on their bodies and their selves. Previously, I suggested that stimulation parameters for the treatment of neuropsychiatric disorders might be manipulated by patients one day. I envisioned a degree of patient discretion, within a pre-set safe range determined by physicians, much like patient-controlled analgesia (PCA) pumps give patients control over the dosing of opioid analgesia [3]. I am glad that such an advance is evolving as a means to preserve batteries in the treatment of motor disorders [16]. I would encourage the neural engineers to embrace the ethical mandate to develop additional platforms that might enhance patient self-determination and foster a greater degree of functional independence. While the neuromodulation community has every reason to celebrate its accomplishments, it would be better served by appreciating that the insertion of a device into the human brain comes with, if not the penumbra of sacrilege, a moral obligation to step out of the shadows and remain clearly available to patients and families over the long haul. Although neuromodulation has liberated many patients from the shackles of disease, we need to appreciate that the hardware that has made this possible can remain tethering. The challenge for the next generation of innovators is to minimize these burdens at this neural interface. By reducing barriers to care that exist in an unprepared health care system and developing more user-friendly technology, the neuromodulation community can expand its reach and broaden the relief provided by these neuro-palliative interventions [17]. Acknowledgements and Disclosures Dr Fins is the recipient of an Investigator Award in Health Policy Research (Minds Apart: Severe Brain Injury and Health Policy) from The Robert Wood Johnson Foundation. He also gratefully acknowledges grant support from the Buster Foundation (Neuroethics and Disorders of Consciousness). He is an unfunded co-investigator of a study of deep brain stimulation in the minimally

  9. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

    Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER

  10. A Review of Hybrid Brain-Computer Interface Systems

    Directory of Open Access Journals (Sweden)

    Setare Amiri

    2013-01-01

    Full Text Available Increasing number of research activities and different types of studies in brain-computer interface (BCI systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

  11. Kinetic Interface

    DEFF Research Database (Denmark)

    2009-01-01

    A kinetic interface for orientation detection in a video training system is disclosed. The interface includes a balance platform instrumented with inertial motion sensors. The interface engages a participant's sense of balance in training exercises.......A kinetic interface for orientation detection in a video training system is disclosed. The interface includes a balance platform instrumented with inertial motion sensors. The interface engages a participant's sense of balance in training exercises....

  12. An investigation on effects of amputee's physiological parameters on maximum pressure developed at the prosthetic socket interface using artificial neural network.

    Science.gov (United States)

    Nayak, Chitresh; Singh, Amit; Chaudhary, Himanshu; Unune, Deepak Rajendra

    2017-10-23

    Technological advances in prosthetics have attracted the curiosity of researchers in monitoring design and developments of the sockets to sustain maximum pressure without any soft tissue damage, skin breakdown, and painful sores. Numerous studies have been reported in the area of pressure measurement at the limb/socket interface, though, the relation between amputee's physiological parameters and the pressure developed at the limb/socket interface is still not studied. Therefore, the purpose of this work is to investigate the effects of patient-specific physiological parameters viz. height, weight, and stump length on the pressure development at the transtibial prosthetic limb/socket interface. Initially, the pressure values at the limb/socket interface were clinically measured during stance and walking conditions for different patients using strain gauges placed at critical locations of the stump. The measured maximum pressure data related to patient's physiological parameters was used to develop an artificial neural network (ANN) model. The effects of physiological parameters on the pressure development at the limb/socket interface were examined using the ANN model. The analyzed results indicated that the weight and stump length significantly affects the maximum pressure values. The outcomes of this work could be an important platform for the design and development of patient-specific prosthetic socket which can endure the maximum pressure conditions at stance and ambulation conditions.

  13. Microfluidic hubs, systems, and methods for interface fluidic modules

    Science.gov (United States)

    Bartsch, Michael S; Claudnic, Mark R; Kim, Hanyoup; Patel, Kamlesh D; Renzi, Ronald F; Van De Vreugde, James L

    2015-01-27

    Embodiments of microfluidic hubs and systems are described that may be used to connect fluidic modules. A space between surfaces may be set by fixtures described herein. In some examples a fixture may set substrate-to-substrate spacing based on a distance between registration surfaces on which the respective substrates rest. Fluidic interfaces are described, including examples where fluid conduits (e.g. capillaries) extend into the fixture to the space between surfaces. Droplets of fluid may be introduced to and/or removed from microfluidic hubs described herein, and fluid actuators may be used to move droplets within the space between surfaces. Continuous flow modules may be integrated with the hubs in some examples.

  14. NASA Access Mechanism - Graphical user interface information retrieval system

    Science.gov (United States)

    Hunter, Judy F.; Generous, Curtis; Duncan, Denise

    1993-01-01

    Access to online information sources of aerospace, scientific, and engineering data, a mission focus for NASA's Scientific and Technical Information Program, has always been limited by factors such as telecommunications, query language syntax, lack of standardization in the information, and the lack of adequate tools to assist in searching. Today, the NASA STI Program's NASA Access Mechanism (NAM) prototype offers a solution to these problems by providing the user with a set of tools that provide a graphical interface to remote, heterogeneous, and distributed information in a manner adaptable to both casual and expert users. Additionally, the NAM provides access to many Internet-based services such as Electronic Mail, the Wide Area Information Servers system, Peer Locating tools, and electronic bulletin boards.

  15. NASA access mechanism: Graphical user interface information retrieval system

    Science.gov (United States)

    Hunter, Judy; Generous, Curtis; Duncan, Denise

    1993-01-01

    Access to online information sources of aerospace, scientific, and engineering data, a mission focus for NASA's Scientific and Technical Information Program, has always been limited to factors such as telecommunications, query language syntax, lack of standardization in the information, and the lack of adequate tools to assist in searching. Today, the NASA STI Program's NASA Access Mechanism (NAM) prototype offers a solution to these problems by providing the user with a set of tools that provide a graphical interface to remote, heterogeneous, and distributed information in a manner adaptable to both casual and expert users. Additionally, the NAM provides access to many Internet-based services such as Electronic Mail, the Wide Area Information Servers system, Peer Locating tools, and electronic bulletin boards.

  16. Space station automation and robotics study. Operator-systems interface

    Science.gov (United States)

    1984-01-01

    This is the final report of a Space Station Automation and Robotics Planning Study, which was a joint project of the Boeing Aerospace Company, Boeing Commercial Airplane Company, and Boeing Computer Services Company. The study is in support of the Advanced Technology Advisory Committee established by NASA in accordance with a mandate by the U.S. Congress. Boeing support complements that provided to the NASA Contractor study team by four aerospace contractors, the Stanford Research Institute (SRI), and the California Space Institute. This study identifies automation and robotics (A&R) technologies that can be advanced by requirements levied by the Space Station Program. The methodology used in the study is to establish functional requirements for the operator system interface (OSI), establish the technologies needed to meet these requirements, and to forecast the availability of these technologies. The OSI would perform path planning, tracking and control, object recognition, fault detection and correction, and plan modifications in connection with extravehicular (EV) robot operations.

  17. Accelerator-control-system interface for intelligent power supplies

    International Nuclear Information System (INIS)

    Cohen, S.

    1992-01-01

    A number of high-current high-precision magnet power supplies have been installed at the proton storage ring at the Los Alamos National Laboratory Accelerator Complex. The units replace existing supplies, powering large dipole magnets in the ring. These bending magnets require a high-current supply that is precise and stable. The control and interface design for these power supplies represents a departure from all others on-site. The supplies have sophisticated microprocessor control on-board and communicate with the accelerator control system via RS-422 (serial communications). The units, built by Alpha Scientific Electronics, Hayward, CA use a high-level ASCII control protocol. The low-level ''front-end'' software used by the accelerator control system has been written to accommodate these new devices. They communicate with the control system through a terminal server port connected to the site-wide ethernet backbone. Details of the software implementation for the analog and digital control of the supplies through the accelerator control system will be presented

  18. Medical Signal-Conditioning and Data-Interface System

    Science.gov (United States)

    Braun, Jeffrey; Jacobus, charles; Booth, Scott; Suarez, Michael; Smith, Derek; Hartnagle, Jeffrey; LePrell, Glenn

    2006-01-01

    A general-purpose portable, wearable electronic signal-conditioning and data-interface system is being developed for medical applications. The system can acquire multiple physiological signals (e.g., electrocardiographic, electroencephalographic, and electromyographic signals) from sensors on the wearer s body, digitize those signals that are received in analog form, preprocess the resulting data, and transmit the data to one or more remote location(s) via a radiocommunication link and/or the Internet. The system includes a computer running data-object-oriented software that can be programmed to configure the system to accept almost any analog or digital input signals from medical devices. The computing hardware and software implement a general-purpose data-routing-and-encapsulation architecture that supports tagging of input data and routing the data in a standardized way through the Internet and other modern packet-switching networks to one or more computer(s) for review by physicians. The architecture supports multiple-site buffering of data for redundancy and reliability, and supports both real-time and slower-than-real-time collection, routing, and viewing of signal data. Routing and viewing stations support insertion of automated analysis routines to aid in encoding, analysis, viewing, and diagnosis.

  19. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  20. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  1. The exploitation of neural networks in automotive engine management systems

    Energy Technology Data Exchange (ETDEWEB)

    Shayler, P.J.; Goodman, M. [University of Nottingham (United Kingdom); Ma, T. [Ford Motor Company, Dagenham (United Kingdom). Research and Engineering Centre

    2000-07-01

    The use of electronic engine control systems on spark ignition engines has enabled a high degree of performance optimisation to be achieved. The range of functions performed by these systems, and the level of performance demanded, is rising and thus so are development times and costs. Neural networks have attracted attention as having the potential to simplify software development and improve the performance of this software. The scope and nature of possible applications is described. In particular, the pattern recognition and classification abilities of networks are applied to crankshaft speed fluctuation data for engine-fault diagnosis, and multidimensional mapping capabilities are investigated as an alternative to large 'lookup' tables and calibration functions. (author)

  2. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  3. Direct process estimation from tomographic data using artificial neural systems

    Science.gov (United States)

    Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

    2001-07-01

    The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

  4. User interface for a tele-operated robotic hand system

    Science.gov (United States)

    Crawford, Anthony L

    2015-03-24

    Disclosed here is a user interface for a robotic hand. The user interface anchors a user's palm in a relatively stationary position and determines various angles of interest necessary for a user's finger to achieve a specific fingertip location. The user interface additionally conducts a calibration procedure to determine the user's applicable physiological dimensions. The user interface uses the applicable physiological dimensions and the specific fingertip location, and treats the user's finger as a two link three degree-of-freedom serial linkage in order to determine the angles of interest. The user interface communicates the angles of interest to a gripping-type end effector which closely mimics the range of motion and proportions of a human hand. The user interface requires minimal contact with the operator and provides distinct advantages in terms of available dexterity, work space flexibility, and adaptability to different users.

  5. User interface for a tele-operated robotic hand system

    Energy Technology Data Exchange (ETDEWEB)

    Crawford, Anthony L

    2015-03-24

    Disclosed here is a user interface for a robotic hand. The user interface anchors a user's palm in a relatively stationary position and determines various angles of interest necessary for a user's finger to achieve a specific fingertip location. The user interface additionally conducts a calibration procedure to determine the user's applicable physiological dimensions. The user interface uses the applicable physiological dimensions and the specific fingertip location, and treats the user's finger as a two link three degree-of-freedom serial linkage in order to determine the angles of interest. The user interface communicates the angles of interest to a gripping-type end effector which closely mimics the range of motion and proportions of a human hand. The user interface requires minimal contact with the operator and provides distinct advantages in terms of available dexterity, work space flexibility, and adaptability to different users.

  6. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

    Science.gov (United States)

    Chai, Rifai; Naik, Ganesh R; Ling, Sai Ho; Nguyen, Hung T

    2017-01-07

    One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

  7. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  8. Neural-network hybrid control for antilock braking systems.

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

  9. Used fuel management system architecture and interface analyses

    Energy Technology Data Exchange (ETDEWEB)

    Nutt, Mark [Argonne National Laboratory, Argonne, IL (United States); Howard, Robert; Busch, Ingrid [Oak Ridge National Laboratory, Oak Ridge, TN (United States); Carter, Joe; Delley, Alexcia [Savannah River National Laboratory, Aiken, SC (United States); Hardin, Ernest; Kalinina, Elena [Sandia National Laboratories, Albuquerque NM (United States); Cotton, Thomas [Complex Systems LLC, Washington, DC (United States)

    2013-07-01

    between at-reactor used fuel management, consolidated storage facilities, and disposal facilities, along with the development of supporting logistics simulation tools, have been initiated to provide the U.S. Department of Energy (DOE) and other stakeholders with information regarding the various alternatives for managing used nuclear fuel (UNF) generated by the current fleet of light water reactors operating in the United States. An important UNF management system interface consideration is the need for ultimate disposal of UNF assemblies contained in waste packages that are sized to be compatible with different geologic media. Thermal analyses indicate that waste package sizes for the geologic media under consideration by the Used Fuel Disposition Campaign may be significantly smaller than the canisters being used for on-site dry storage by the nuclear utilities. Therefore, at some point along the UNF disposition pathway, there could be a need to repackage fuel assemblies already loaded and being loaded into the dry storage canisters currently in use. The implications of where and when the packaging or repackaging of commercial UNF will occur are key questions being addressed in this evaluation. The analysis demonstrated that thermal considerations will have a major impact on the operation of the system and that acceptance priority, rates, and facility start dates have significant system implications. (authors)

  10. Used fuel management system architecture and interface analyses

    International Nuclear Information System (INIS)

    Nutt, Mark; Howard, Robert; Busch, Ingrid; Carter, Joe; Delley, Alexcia; Hardin, Ernest; Kalinina, Elena; Cotton, Thomas

    2013-01-01

    between at-reactor used fuel management, consolidated storage facilities, and disposal facilities, along with the development of supporting logistics simulation tools, have been initiated to provide the U.S. Department of Energy (DOE) and other stakeholders with information regarding the various alternatives for managing used nuclear fuel (UNF) generated by the current fleet of light water reactors operating in the United States. An important UNF management system interface consideration is the need for ultimate disposal of UNF assemblies contained in waste packages that are sized to be compatible with different geologic media. Thermal analyses indicate that waste package sizes for the geologic media under consideration by the Used Fuel Disposition Campaign may be significantly smaller than the canisters being used for on-site dry storage by the nuclear utilities. Therefore, at some point along the UNF disposition pathway, there could be a need to repackage fuel assemblies already loaded and being loaded into the dry storage canisters currently in use. The implications of where and when the packaging or repackaging of commercial UNF will occur are key questions being addressed in this evaluation. The analysis demonstrated that thermal considerations will have a major impact on the operation of the system and that acceptance priority, rates, and facility start dates have significant system implications. (authors)

  11. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  12. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  13. EPES information depth for an overlayer/substrate system with a diffuse interface

    International Nuclear Information System (INIS)

    Zommer, L.

    2009-01-01

    The information depth (ID) of elastic peak electron spectroscopy (EPES) was considered for an overlayer/substrate system with a diffuse interface. The interface was assumed to have an exponential concentration profile. The paradox previously found by Zommer and Jablonski for the Rh/Al and Al/Rh systems with sharp interfaces also occurs for these systems with diffuse interfaces. We compared IDs for diffuse and sharp interfaces. Deviations between the IDs depend on the interface width, overlayer thickness, and selected system for a given primary energy (here 2000 eV). The deviations for the Rh/Al and Al/Rh systems differ profoundly. These results are of importance when interpreting EPES measurements of layered system

  14. Motor-related brain activity during action observation: a neural substrate for electrocorticographic brain-computer interfaces after spinal cord injury

    Directory of Open Access Journals (Sweden)

    Jennifer L Collinger

    2014-02-01

    Full Text Available After spinal cord injury (SCI, motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation, in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, action observation can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG and functional magnetic resonance imaging (fMRI. Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz and the high-gamma band (65-115 Hz which contains significant movement-related information. We observed significant motor-related high-gamma band activity during action observation in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that action observation could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with

  15. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  16. Brain-Computer Interfacing Embedded in Intelligent and Affective Systems

    NARCIS (Netherlands)

    Nijholt, Antinus

    In this talk we survey recent research views on non-traditional brain-computer interfaces (BCI). That is, interfaces that can process brain activity input, but that are designed for the ‘general population’, rather than for clinical purposes. Control of applications can be made more robust by fusing

  17. Brain-machine interfaces for controlling lower-limb powered robotic systems

    Science.gov (United States)

    He, Yongtian; Eguren, David; Azorín, José M.; Grossman, Robert G.; Phat Luu, Trieu; Contreras-Vidal, Jose L.

    2018-04-01

    Objective. Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking. Approach. To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared. Main results. Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others. Significance. We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the

  18. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  19. Decoupling control of vehicle chassis system based on neural network inverse system

    Science.gov (United States)

    Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

    2018-06-01

    Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

  20. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  1. The computerised procedure system COPMA and its user interface

    International Nuclear Information System (INIS)

    Krogsaeter, M.; Larsen, J.; Nilsen, S.; Oewre, F.

    1990-01-01

    At the OECD Halden Reactor Project, the COPMA system has been developed in order to investigate whether procedures can be executed more safety and efficiently if they are computerised, i.e. if the operator uses a CRT-based system instead of written manuals. Procedures are entered in a procedure data base using PED, a procedure editor. Each procedure is given a textual as well as a graphical representation. For the textual representation, the language PROLA is used, a language which has been designed for simple procedure specification. The COPMA online system lets the operator execute procedures that are stored in the procedure data base. The operator interface is a screen divided into non-overlapping windows each serving a different purpose. All commands to the system are given by moving a mouse device around and clicking buttons on top of the mouse. A procedure consists of steps, each step containing a number of instructions. The operator works on one activity at a time, an activity to be seen as a procedure instance. A graph shows the overall procedure (or activity) structure in a window and activity execution is traced in the graph. Another windows shows the instructions of the step currently being executed. The operator steps through the activity by selecting whether and how to execute the listed instructions. COPMA can maintain the status of several activities in parallel, so that the operator can easily switch between different activities. COPMA is linked to a PWR nuclear simulator over Ethernet using the TCP/IP protocol. This gives a number of advantages as compared to conventional written procedures, especially the fact that COPMA can help collect data from the procedure data base automatically

  2. Functional fusion of living systems with synthetic electrode interfaces

    Directory of Open Access Journals (Sweden)

    Oskar Staufer

    2016-02-01

    Full Text Available The functional fusion of “living” biomaterial (such as cells with synthetic systems has developed into a principal ambition for various scientific disciplines. In particular, emerging fields such as bionics and nanomedicine integrate advanced nanomaterials with biomolecules, cells and organisms in order to develop novel strategies for applications, including energy production or real-time diagnostics utilizing biomolecular machineries “perfected” during billion years of evolution. To date, hardware–wetware interfaces that sample or modulate bioelectric potentials, such as neuroprostheses or implantable energy harvesters, are mostly based on microelectrodes brought into the closest possible contact with the targeted cells. Recently, the possibility of using electrochemical gradients of the inner ear for technical applications was demonstrated using implanted electrodes, where 1.12 nW of electrical power was harvested from the guinea pig endocochlear potential for up to 5 h (Mercier, P.; Lysaght, A.; Bandyopadhyay, S.; Chandrakasan, A.; Stankovic, K. Nat. Biotech. 2012, 30, 1240–1243. More recent approaches employ nanowires (NWs able to penetrate the cellular membrane and to record extra- and intracellular electrical signals, in some cases with subcellular resolution (Spira, M.; Hai, A. Nat. Nano. 2013, 8, 83–94. Such techniques include nanoelectric scaffolds containing free-standing silicon NWs (Robinson, J. T.; Jorgolli, M.; Shalek, A. K.; Yoon, M. H.; Gertner, R. S.; Park, H. Nat Nanotechnol. 2012, 10, 180–184 or NW field-effect transistors (Qing, Q.; Jiang, Z.; Xu, L.; Gao, R.; Mai, L.; Lieber, C. Nat. Nano. 2013, 9, 142–147, vertically aligned gallium phosphide NWs (Hällström, W.; Mårtensson, T.; Prinz, C.; Gustavsson, P.; Montelius, L.; Samuelson, L.; Kanje, M. Nano Lett. 2007, 7, 2960–2965 or individually contacted, electrically active carbon nanofibers. The latter of these approaches is capable of recording

  3. User interface design and system integration aspects of core monitoring systems

    International Nuclear Information System (INIS)

    Berg, O.; Bodal, T.; Hornaes, A.; Porsmyr, J.

    2000-01-01

    The present paper describes our experience with the SCORPIO core monitoring system using generic building blocks for the MMI and system integration. In this context the different layers of the software system are discussed starting with the communication system, interfacing of various modules (e.g. physics codes), administration of several modules and generation of graphical user interfaces for different categories of end-users. A method by which re-use of software components can make the system development and maintenance more efficient is described. Examples are given from different system installation projects. The methodology adopted is considered particularly important in the future, as it is anticipated that core monitoring systems will be expanded with new functions (e.g. information from technical specifications, procedures, noise analysis, etc). Further, efficient coupling of off-line tools for core physics calculations and on-line modules in core monitoring can pave the way for cost savings. (authors)

  4. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  5. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  6. A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface.

    Science.gov (United States)

    Romanelli, Pantaleo; Piangerelli, Marco; Ratel, David; Gaude, Christophe; Costecalde, Thomas; Puttilli, Cosimo; Picciafuoco, Mauro; Benabid, Alim; Torres, Napoleon

    2018-05-11

    OBJECTIVE Wireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans. METHODS The authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate ( Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid. RESULTS The implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper

  7. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  8. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

  9. Improvement of computer complex and interface system for compact nuclear simulator

    International Nuclear Information System (INIS)

    Lee, D. Y.; Park, W. M.; Cha, K. H.; Jung, C. H.; Park, J. C.

    1999-01-01

    CNS(Compact Nuclear Simulator) was developed at the end of 1980s, and have been used as training simulator for staffs of KAERI during 10 years. The operator panel interface cards and the graphic interface cards were designed with special purpose only for CNS. As these interface cards were worn out for 10 years, it was very difficult to get spare parts and to repair them. And the interface cards were damaged by over current happened by shortage of lamp in the operator panel. To solve these problem, the project 'Improvement of Compact Nuclear Simulator' was started from 1997. This paper only introduces about the improvement of computer complex and interface system

  10. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  11. Dilational viscoelastic properties of fluid interfaces - III mixed surfactant systems

    Energy Technology Data Exchange (ETDEWEB)

    Djabbarah, N.F.; Wasan, D.T.

    1982-01-01

    The surface viscosity and elasticity of solutions of mixed surfactants were determined using the longitudinal wave technique combined with tracer particle measurements. The recent analysis of Maru et al., which was restricted to insoluble monolayers and to monolayers adsorbed from a single surfactant solution, has now been extended to multicomponent solutions. This analysis can be used not only to estimate the ''net'' viscoelastic properties at gas-liquid interfaces but also to estimate the composition as well as the intrinsic viscoelastic properties. Furthermore, when accompanied by separate measurements of shear viscoelastic properties, the above analysis can be used for the determination of dilational viscosity and elasticity. Surface viscoelasticity measurements were conducted on aqueous solutions of sodium lauryl sulfate and sodium lauryl sulfate-lauryl alcohol. Net surface viscosity and elasticity of sodium lauryl sulfate solutions increased with bulk concentration and reached a maximum at a concentration in the neighborhood of the critical micelle concentration. The presence of small amount of lauryl alcohol caused almost an order of magnitude increase in intrinsic surface viscosity and a similar increase in compositional surface elasticity. A comparison between the values of intrinsic surface viscosity and those of surface shear viscosity indicated that surface dilational viscosity exceeds surface shear viscosity by at least two orders of magnitude. These appear to be the first set of data presented hitherto for the surface dilational properties in addition to surface shear properties for the same mixed surfactant systems.

  12. Human-system interfaces for the ABWR control room

    International Nuclear Information System (INIS)

    Gutierrez, R.; O'Neil, T.J.

    1993-01-01

    The 1300-MW(electric) advanced boiling water reactor (ABWR) design has been developed by General Electric Co. (G.E.) and its technical associates, Hitachi Ltd. and Toshiba Corporation, under the sponsorship of the Tokyo Electric Power Company. Key features of the ABWR include simplification, improved safety and reliability, reduced occupational exposure and radwaste, improved maneuverability, and reduced construction, fuel, and operating costs. The ABWR incorporates the best proven features from BWR designs in Japan, Europe, and the United States, combined with the broad-scope application of leading-edge technology. The first application of the ABWR design will be at TEP-CO's Kashiwazaki-Kariwa nuclear power station units 6 and 7, which are scheduled to begin commercial operation in 1996 and 1997, respectively. The ABWR is also the lead plant in the U.S. standard plant design certification program. It is scheduled to receive final approval from the U.S. Nuclear Regulatory Commission in early 1994. The development of the human system interaces (HSI) for the ABWR is an extension of the operating experience of existing man-machine interfaces currently being used at GE-designed plants

  13. Description of waste pretreatment and interfacing systems dynamic simulation model

    International Nuclear Information System (INIS)

    Garbrick, D.J.; Zimmerman, B.D.

    1995-05-01

    The Waste Pretreatment and Interfacing Systems Dynamic Simulation Model was created to investigate the required pretreatment facility processing rates for both high level and low level waste so that the vitrification of tank waste can be completed according to the milestones defined in the Tri-Party Agreement (TPA). In order to achieve this objective, the processes upstream and downstream of the pretreatment facilities must also be included. The simulation model starts with retrieval of tank waste and ends with vitrification for both low level and high level wastes. This report describes the results of three simulation cases: one based on suggested average facility processing rates, one with facility rates determined so that approximately 6 new DSTs are required, and one with facility rates determined so that approximately no new DSTs are required. It appears, based on the simulation results, that reasonable facility processing rates can be selected so that no new DSTs are required by the TWRS program. However, this conclusion must be viewed with respect to the modeling assumptions, described in detail in the report. Also included in the report, in an appendix, are results of two sensitivity cases: one with glass plant water recycle steams recycled versus not recycled, and one employing the TPA SST retrieval schedule versus a more uniform SST retrieval schedule. Both recycling and retrieval schedule appear to have a significant impact on overall tank usage

  14. System and Method for Providing a Climate Data Analytic Services Application Programming Interface Distribution Package

    Science.gov (United States)

    Schnase, John L. (Inventor); Duffy, Daniel Q. (Inventor); Tamkin, Glenn S. (Inventor)

    2016-01-01

    A system, method and computer-readable storage devices for providing a climate data analytic services application programming interface distribution package. The example system can provide various components. The system provides a climate data analytic services application programming interface library that enables software applications running on a client device to invoke the capabilities of a climate data analytic service. The system provides a command-line interface that provides a means of interacting with a climate data analytic service by issuing commands directly to the system's server interface. The system provides sample programs that call on the capabilities of the application programming interface library and can be used as templates for the construction of new client applications. The system can also provide test utilities, build utilities, service integration utilities, and documentation.

  15. Principle of human system interface (HSI) design for new reactor console of PUSPATI TRIGA Reactor (RTP)

    International Nuclear Information System (INIS)

    Zareen Khan Abdul Jalil Khan; Ridzuan Abdul Mutalib; Mohd Idris Taib; Mohd Khairulezwan Abdul Manan; Nurfarhana Ayuni Joha; Mohd Sabri Minhat; Izhar Abu Hussin

    2013-01-01

    Full-text: This paper will describe the principle of human system interface design for new reactor console in control room at TRIGA reactor facility. In order to support these human system interface challenges in digital reactor console. Software-based instrumentation and control (I and C) system for new reactor console could lead to new human machine integration. The proposed of Human System Interface (HSI) which included the large display panels which shows reactor status, compact and computer-based workstations for monitoring, control and protection function. The proposed Human System Interface (HIS) has been evaluated using various human factor engineering. It can be concluded that the Human System Interface (HIS) is designed as to address the safety related computer controlled system. (author)

  16. A Study of an Assistance SystemUsing a Haptic Interface

    OpenAIRE

    浅川, 貴史

    2011-01-01

    We make a proposal for a music baton system for visual handicapped persons. This system is constituted by an acceleration sensor. a radio module. and a haptic interface device. The acceleration sensor is built in the music baton grip and the data are transmitted by the radio module. A performer has a receiver with the haptic interface device. The receiver's CPU picks up rhythm from the data and vibrates the haptic interface device. This paper is described about an experiment of comparing the ...

  17. Development of an X Window based operator's interface for a core monitoring system

    International Nuclear Information System (INIS)

    Vegh, J.; Huszar, J.; Laz, J.

    1992-09-01

    The components, functioning and programming concepts of the man-machine interface applied in an upgraded version of the core monitoring system and reactor information system VERONA for WWER-440 type nuclear power reactors, installed at the Paks Nuclear Power Plant, are described. The application of the X Window standard Graphical User Interface facilitated modular interface design and made program development easier and faster. (author) 3 refs.; 13 figs

  18. Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

    Directory of Open Access Journals (Sweden)

    Shivayogi V Hiremath

    2015-06-01

    Full Text Available A brain-computer interface (BCI system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

  19. Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

    Science.gov (United States)

    Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

    2015-01-01

    A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

  20. User interface design principles for the SSM/PMAD automated power system

    International Nuclear Information System (INIS)

    Jakstas, L.M.; Myers, C.J.

    1991-01-01

    Computer-human interfaces are an integral part of developing software for spacecraft power systems. A well designed and efficient user interface enables an engineer to effectively operate the system, while it concurrently prevents the user from entering data which is beyond boundary conditions or performing operations which are out of context. A user interface should also be designed to ensure that the engineer easily obtains all useful and critical data for operating the system and is aware of all faults and states in the system. Martin Marietta, under contract to NASA George C. Marshall Space Flight Center, has developed a user interface for the Space Station Module Power Management and Distribution (SSM/PMAD) automated power system testbed which provides human access to the functionality of the power system, as well as exemplifying current techniques in user interface design. The testbed user interface was designed to enable an engineer to operate the system easily without having significant knowledge of computer systems, as well as provide an environment in which the engineer can monitor and interact with the SSM/PMAD system hardware. The design of the interface supports a global view of the most important data form the various hardware and software components, as well as enabling the user to obtain additional or more detailed data when needed. The components and representations of the SSM/PMAD testbed user interface are examined in this paper. An engineer's interactions with the system are also described

  1. 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

  2. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  3. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  4. Lifetime assessment of atomic-layer-deposited Al2O3-Parylene C bilayer coating for neural interfaces using accelerated age testing and electrochemical characterization.

    Science.gov (United States)

    Minnikanti, Saugandhika; Diao, Guoqing; Pancrazio, Joseph J; Xie, Xianzong; Rieth, Loren; Solzbacher, Florian; Peixoto, Nathalia

    2014-02-01

    The lifetime and stability of insulation are critical features for the reliable operation of an implantable neural interface device. A critical factor for an implanted insulation's performance is its barrier properties that limit access of biological fluids to the underlying device or metal electrode. Parylene C is a material that has been used in FDA-approved implantable devices. Considered a biocompatible polymer with barrier properties, it has been used as a substrate, insulation or an encapsulation for neural implant technology. Recently, it has been suggested that a bilayer coating of Parylene C on top of atomic-layer-deposited Al2O3 would provide enhanced barrier properties. Here we report a comprehensive study to examine the mean time to failure of Parylene C and Al2O3-Parylene C coated devices using accelerated lifetime testing. Samples were tested at 60°C for up to 3 months while performing electrochemical measurements to characterize the integrity of the insulation. The mean time to failure for Al2O3-Parylene C was 4.6 times longer than Parylene C coated samples. In addition, based on modeling of the data using electrical circuit equivalents, we show here that there are two main modes of failure. Our results suggest that failure of the insulating layer is due to pore formation or blistering as well as thinning of the coating over time. The enhanced barrier properties of the bilayer Al2O3-Parylene C over Parylene C makes it a promising candidate as an encapsulating neural interface. Copyright © 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  5. User interface design principles for the SSM/PMAD automated power system

    Science.gov (United States)

    Jakstas, Laura M.; Myers, Chris J.

    1991-01-01

    Martin Marietta has developed a user interface for the space station module power management and distribution (SSM/PMAD) automated power system testbed which provides human access to the functionality of the power system, as well as exemplifying current techniques in user interface design. The testbed user interface was designed to enable an engineer to operate the system easily without having significant knowledge of computer systems, as well as provide an environment in which the engineer can monitor and interact with the SSM/PMAD system hardware. The design of the interface supports a global view of the most important data from the various hardware and software components, as well as enabling the user to obtain additional or more detailed data when needed. The components and representations of the SSM/PMAD testbed user interface are examined. An engineer's interactions with the system are also described.

  6. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  7. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  8. The high level programmer and user interface of the NSLS control system

    International Nuclear Information System (INIS)

    Tang, Y.N.; Smith, J.D.; Sathe, S.

    1993-01-01

    This paper presents the major components of the high level software in the NSLS upgraded control system. Both programmer and user interfaces are discussed. The use of the high-speed work stations, fast network communications, UNIX system, X-window and Motif have greatly changed and improved these interfaces

  9. Adaptive fuzzy-neural-network control for maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  10. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  11. Flood forecasting within urban drainage systems using NARX neural network.

    Science.gov (United States)

    Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi

    2017-11-01

    Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.

  12. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  13. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  14. A neural network method for solving a system of linear variational inequalities

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

    In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

  15. A user interface development tool for space science systems Transportable Applications Environment (TAE) Plus

    Science.gov (United States)

    Szczur, Martha R.

    1990-01-01

    The Transportable Applications Environment Plus (TAE PLUS), developed at NASA's Goddard Space Flight Center, is a portable What You See Is What You Get (WYSIWYG) user interface development and management system. Its primary objective is to provide an integrated software environment that allows interactive prototyping and development that of user interfaces, as well as management of the user interface within the operational domain. Although TAE Plus is applicable to many types of applications, its focus is supporting user interfaces for space applications. This paper discusses what TAE Plus provides and how the implementation has utilized state-of-the-art technologies within graphic workstations, windowing systems and object-oriented programming languages.

  16. Extending the POSIX I/O interface: a parallel file system perspective.

    Energy Technology Data Exchange (ETDEWEB)

    Vilayannur, M.; Lang, S.; Ross, R.; Klundt, R.; Ward, L.; Mathematics and Computer Science; VMWare, Inc.; SNL

    2008-12-11

    The POSIX interface does not lend itself well to enabling good performance for high-end applications. Extensions are needed in the POSIX I/O interface so that high-concurrency HPC applications running on top of parallel file systems perform well. This paper presents the rationale, design, and evaluation of a reference implementation of a subset of the POSIX I/O interfaces on a widely used parallel file system (PVFS) on clusters. Experimental results on a set of micro-benchmarks confirm that the extensions to the POSIX interface greatly improve scalability and performance.

  17. Standards for the user interface - Developing a user consensus. [for Space Station Information System

    Science.gov (United States)

    Moe, Karen L.; Perkins, Dorothy C.; Szczur, Martha R.

    1987-01-01

    The user support environment (USE) which is a set of software tools for a flexible standard interactive user interface to the Space Station systems, platforms, and payloads is described in detail. Included in the USE concept are a user interface language, a run time environment and user interface management system, support tools, and standards for human interaction methods. The goals and challenges of the USE are discussed as well as a methodology based on prototype demonstrations for involving users in the process of validating the USE concepts. By prototyping the key concepts and salient features of the proposed user interface standards, the user's ability to respond is greatly enhanced.

  18. Neural network models for biological waste-gas treatment systems.

    Science.gov (United States)

    Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

    2011-12-15

    This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

  19. Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101 \\xAF 0) interface from a high-dimensional neural network potential

    Science.gov (United States)

    Quaranta, Vanessa; Hellström, Matti; Behler, Jörg; Kullgren, Jolla; Mitev, Pavlin D.; Hermansson, Kersti

    2018-06-01

    Unraveling the atomistic details of solid/liquid interfaces, e.g., by means of vibrational spectroscopy, is of vital importance in numerous applications, from electrochemistry to heterogeneous catalysis. Water-oxide interfaces represent a formidable challenge because a large variety of molecular and dissociated water species are present at the surface. Here, we present a comprehensive theoretical analysis of the anharmonic OH stretching vibrations at the water/ZnO(101 ¯ 0) interface as a prototypical case. Molecular dynamics simulations employing a reactive high-dimensional neural network potential based on density functional theory calculations have been used to sample the interfacial structures. In the second step, one-dimensional potential energy curves have been generated for a large number of configurations to solve the nuclear Schrödinger equation. We find that (i) the ZnO surface gives rise to OH frequency shifts up to a distance of about 4 Å from the surface; (ii) the spectrum contains a number of overlapping signals arising from different chemical species, with the frequencies decreasing in the order ν(adsorbed hydroxide) > ν(non-adsorbed water) > ν(surface hydroxide) > ν(adsorbed water); (iii) stretching frequencies are strongly influenced by the hydrogen bond pattern of these interfacial species. Finally, we have been able to identify substantial correlations between the stretching frequencies and hydrogen bond lengths for all species.

  20. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  1. Ecological Design of Cooperative Human-Machine Interfaces for Safety of Intelligent Transport Systems

    Directory of Open Access Journals (Sweden)

    Orekhov Aleksandr

    2016-01-01

    Full Text Available The paper describes research results in the domain of cooperative intelligent transport systems. The requirements for human-machine interface considering safety issue of for intelligent transport systems (ITSare analyzed. Profiling of the requirements to cooperative human-machine interface (CHMI for such systems including requirements to usability and safety is based on a set of standards for ITSs. An approach and design technique of cooperative human-machine interface for ITSs are suggested. The architecture of cloud-based CHMI for intelligent transport systems has been developed. The prototype of software system CHMI4ITSis described.

  2. State transition storyboards: A tool for designing the Goldstone solar system radar data acquisition system user interface software

    Science.gov (United States)

    Howard, S. D.

    1987-01-01

    Effective user interface design in software systems is a complex task that takes place without adequate modeling tools. By combining state transition diagrams and the storyboard technique of filmmakers, State Transition Storyboards were developed to provide a detailed modeling technique for the Goldstone Solar System Radar Data Acquisition System human-machine interface. Illustrations are included with a description of the modeling technique.

  3. Brain machine interface and limb reanimation technologies: restoring function after spinal cord injury through development of a bypass system.

    Science.gov (United States)

    Lobel, Darlene A; Lee, Kendall H

    2014-05-01

    Functional restoration of limb movement after traumatic spinal cord injury (SCI) remains the ultimate goal in SCI treatment and directs the focus of current research strategies. To date, most investigations in the treatment of SCI focus on repairing the injury site. Although offering some promise, these efforts have met with significant roadblocks because treatment measures that are successful in animal trials do not yield similar results in human trials. In contrast to biologic therapies, there are now emerging neural interface technologies, such as brain machine interface (BMI) and limb reanimation through electrical stimulators, to create a bypass around the site of the SCI. The BMI systems analyze brain signals to allow control of devices that are used to assist SCI patients. Such devices may include a computer, robotic arm, or exoskeleton. Limb reanimation technologies, which include functional electrical stimulation, epidural stimulation, and intraspinal microstimulation systems, activate neuronal pathways below the level of the SCI. We present a concise review of recent advances in the BMI and limb reanimation technologies that provides the foundation for the development of a bypass system to improve functional outcome after traumatic SCI. We also discuss challenges to the practical implementation of such a bypass system in both these developing fields. Copyright © 2014 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

  4. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  5. A HIGH-LEVEL PYTHON INTERFACE TO THE FERMILAB ACNET CONTROL SYSTEM

    Energy Technology Data Exchange (ETDEWEB)

    Piot, P. [Fermilab; Halavanau, A. [Fermilab

    2016-10-19

    This paper discusses the implementation of a python- based high-level interface to the Fermilab acnet control system. The interface has been successfully employed during the commissioning of the Fermilab Accelerator Science & Technology (FAST) facility. Specifically, we present examples of applications at FAST which include the interfacing of the elegant program to assist lattice matching, an automated emittance measurement via the quadrupole-scan method and tranverse transport matrix measurement of a superconducting RF cavity.

  6. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  7. A new evolutionary system for evolving artificial neural networks.

    Science.gov (United States)

    Yao, X; Liu, Y

    1997-01-01

    This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

  8. Human Machine Interface and Usability Issues: Exploring a Preliminary Mission Management System Evaluation Methodology

    National Research Council Canada - National Science Library

    Kardos, Monique

    2001-01-01

    ...) concept demonstrator test during June 2000. The questionnaire was designed to examine usability and interface issues relating to the design of the systems or tools, and to provide suggestions for future iterations of the system...

  9. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  10. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  11. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  12. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  13. User interface for a partially incompatible system software environment with non-ADP users

    Energy Technology Data Exchange (ETDEWEB)

    Loffman, R.S.

    1987-08-01

    Good user interfaces to computer systems and software applications are the result of combining an analysis of user needs with knowledge of interface design principles and techniques. This thesis reports on an interface for an environment: (a) that consists of users who are not computer science or data processing professionals; and (b) which is bound by predetermined software and hardware. The interface was designed which combined these considerations with user interface design principles. Current literature was investigated to establish a baseline of knowledge about user interface design. There are many techniques which can be used to implement a user interface, but all should have the same basic goal, which is to assist the user in the performance of a task. This can be accomplished by providing the user with consistent, well-structured interfaces which also provide flexibility to adapt to differences among users. The interface produced used menu selection and command language techniques to make two different operating system environments appear similar. Additional included features helped to address the needs of different users. The original goal was also to make the transition between the two systems transparent. This was not fully accomplished due to software and hardware limitations.

  14. A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

    Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

  15. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  16. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  17. Graphical User Interfaces and Library Systems: End-User Reactions.

    Science.gov (United States)

    Zorn, Margaret; Marshall, Lucy

    1995-01-01

    Describes a study by Parke-Davis Pharmaceutical Research Library to determine user satisfaction with the graphical user interface-based (GUI) Dynix Marquis compared with the text-based Dynix Classic Online Public Access Catalog (OPAC). Results show that the GUI-based OPAC was preferred by endusers over the text-based OPAC. (eight references) (DGM)

  18. The web-based user interface for EAST plasma control system

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, R.R., E-mail: rrzhang@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Xiao, B.J. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); School of Nuclear Science and Technology, University of Science and Technology of China, Anhui (China); Yuan, Q.P. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Yang, F. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Department of Computer Science, Anhui Medical University, Anhui (China); Zhang, Y. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Johnson, R.D.; Penaflor, B.G. [General Atomics, DIII-D National Fusion Facility, San Diego, CA (United States)

    2014-05-15

    The plasma control system (PCS) plays a vital role at EAST for fusion science experiments. Its software application consists of two main parts: an IDL graphical user interface for setting a large number of plasma parameters to specify each discharge, several programs for performing the real-time feedback control and managing the whole control system. The PCS user interface can be used from any X11 Windows client with privileged access to the PCS computer system. However, remote access to the PCS system via the IDL user interface becomes an extreme inconvenience due to the high network latency to draw or operate the interfaces. In order to realize lower latency for remote access to the PCS system, a web-based system has been developed for EAST recently. The setup data are retrieved from the PCS system and client-side JavaScript draws the interfaces into the user's browser. The user settings are also sent back to the PCS system for controlling discharges. These technologies allow the web-based user interface to be viewed by authorized users with a web browser and have it communicate with PCS server processes directly. It works together with the IDL interface and provides a new way to aid remote participation.

  19. The web-based user interface for EAST plasma control system

    International Nuclear Information System (INIS)

    Zhang, R.R.; Xiao, B.J.; Yuan, Q.P.; Yang, F.; Zhang, Y.; Johnson, R.D.; Penaflor, B.G.

    2014-01-01

    The plasma control system (PCS) plays a vital role at EAST for fusion science experiments. Its software application consists of two main parts: an IDL graphical user interface for setting a large number of plasma parameters to specify each discharge, several programs for performing the real-time feedback control and managing the whole control system. The PCS user interface can be used from any X11 Windows client with privileged access to the PCS computer system. However, remote access to the PCS system via the IDL user interface becomes an extreme inconvenience due to the high network latency to draw or operate the interfaces. In order to realize lower latency for remote access to the PCS system, a web-based system has been developed for EAST recently. The setup data are retrieved from the PCS system and client-side JavaScript draws the interfaces into the user's browser. The user settings are also sent back to the PCS system for controlling discharges. These technologies allow the web-based user interface to be viewed by authorized users with a web browser and have it communicate with PCS server processes directly. It works together with the IDL interface and provides a new way to aid remote participation

  20. Statistical mechanics of complex neural systems and high dimensional data

    International Nuclear Information System (INIS)

    Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

    2013-01-01

    Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks. (paper)

  1. Ion Transfer Voltammetry Associated with Two Polarizable Interfaces Within Water and Moderately Hydrophobic Ionic Liquid Systems

    DEFF Research Database (Denmark)

    Gan, Shiyu; Zhou, Min; Zhang, Jingdong

    2013-01-01

    An electrochemical system composed of two polarizable interfaces (the metallic electrode|water and water|ionic liquid interfaces), namely two‐polarized‐interface (TPI) technique, has been proposed to explore the ion transfer processes between water and moderately hydrophobic ionic liquids (W...... to an extremely narrow polarized potential window (ppw) caused by these moderately hydrophobic ionic components. In this article, we show that TPI technique has virtually eliminated the ppw limitation based on a controlling step of concentration polarization at the electrode|water interface. With the aid...

  2. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  3. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    International Nuclear Information System (INIS)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

    2006-01-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  4. Short report on the evaluation of a graphical user interface for radiation therapy planning systems

    International Nuclear Information System (INIS)

    Martin, M.B.

    1993-01-01

    Since their introduction graphical user interfaces for computing applications have generally appealed more to users than command-line or menu interfaces. Benefits from using a graphical interface include ease-of-use, ease-of-under-standing and increased productivity. For a radiation therapy planning application, an additional potential benefit is that the user regards the planning activity as a closer simulation of the real world situation. A prototype radiation therapy planning system incorporating a graphical user interface was developed on an Apple Macintosh microcomputer. Its graphic interface was then evaluated by twenty-six participants. The results showed markedly that the features associated with a graphic user interface were preferred. 6 refs., 3 figs., 1 tab

  5. Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

    Energy Technology Data Exchange (ETDEWEB)

    OHara, J.M.; Higgins, J.C.

    2010-01-31

    Automation has become ubiquitous in modern complex systems and commercial nuclear power plants are no exception. Beyond the control of plant functions and systems, automation is applied to a wide range of additional functions including monitoring and detection, situation assessment, response planning, response implementation, and interface management. Automation has become a 'team player' supporting plant personnel in nearly all aspects of plant operation. In light of the increasing use and importance of automation in new and future plants, guidance is needed to enable the NRC staff to conduct safety reviews of the human factors engineering (HFE) aspects of modern automation. The objective of the research described in this report was to develop guidance for reviewing the operator's interface with automation. We first developed a characterization of the important HFE aspects of automation based on how it is implemented in current systems. The characterization included five dimensions: Level of automation, function of automation, modes of automation, flexibility of allocation, and reliability of automation. Next, we reviewed literature pertaining to the effects of these aspects of automation on human performance and the design of human-system interfaces (HSIs) for automation. Then, we used the technical basis established by the literature to develop design review guidance. The guidance is divided into the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration. In addition, we identified insights into the automaton design process, operator training, and operations.

  6. Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

    International Nuclear Information System (INIS)

    O'Hara, J.M.; Higgins, J.C.

    2010-01-01

    Automation has become ubiquitous in modern complex systems and commercial nuclear power plants are no exception. Beyond the control of plant functions and systems, automation is applied to a wide range of additional functions including monitoring and detection, situation assessment, response planning, response implementation, and interface management. Automation has become a 'team player' supporting plant personnel in nearly all aspects of plant operation. In light of the increasing use and importance of automation in new and future plants, guidance is needed to enable the NRC staff to conduct safety reviews of the human factors engineering (HFE) aspects of modern automation. The objective of the research described in this report was to develop guidance for reviewing the operator's interface with automation. We first developed a characterization of the important HFE aspects of automation based on how it is implemented in current systems. The characterization included five dimensions: Level of automation, function of automation, modes of automation, flexibility of allocation, and reliability of automation. Next, we reviewed literature pertaining to the effects of these aspects of automation on human performance and the design of human-system interfaces (HSIs) for automation. Then, we used the technical basis established by the literature to develop design review guidance. The guidance is divided into the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration. In addition, we identified insights into the automaton design process, operator training, and operations.

  7. Human-system interface evaluation system for advanced control room based on SQL database

    International Nuclear Information System (INIS)

    Zhang Yan; Zhou Zhiwei; Bian Zhiqiang; Xu Li

    2005-01-01

    User Interface (UI) plays an important role in the advanced control room (ACR) of a nuclear power plant (NPP). In this paper, we present a rule-based ACR Human-system Interface Evaluation System (AHSIES) using expert system technology, which can evaluate UI design shortcomings, propose modification suggestions, and help designer improve the ACR interface design. AHSIES consists of four programs: the UI Editor, the Operation Procedure Manager, the Operation Simulator and the UI Design Evaluator. These four parts respectively function for: editing a set of UI icons employed as the operation screens of an advanced control room; for editing operation procedures aiming at any specified operation with simple language; for simulate the operation sequences dynamically and recording the relevant information for design performance of the UIs; and for evaluating both static and dynamic performance of the ACR UI design according to well established design guidelines and criteria with the information gained from the first three programs. Microsoft SQL Server 2000 DBMS is adopted to manage the voluminous data and its complex relationships. The preliminary test application of AHSIES for a simplified ACR UI design of a PWR NPP has shown that the expert evaluation system is capable of achieving satisfactory evaluation results. (authors)

  8. Human-centered design of the human-system interfaces of medical equipment: thyroid uptake system

    International Nuclear Information System (INIS)

    Monteiro, Jonathan K.R.; Farias, Marcos S.; Santos, Isaac J.A. Luquetti; Monteiro, Beany G.

    2013-01-01

    Technology plays an important role in modern medical centers, making healthcare increasingly complex, relying on complex technical equipment. This technical complexity is particularly noticeable in the nuclear medicine. Poorly design human-system interfaces can increase the risks for human error. The human-centered approach emphasizes the development of the equipment with a deep understanding of the users activities, current work practices, needs and abilities of the users. An important concept of human-centered design is that the ease-of-use of the equipment can be ensured only if users are actively incorporated in all phases of the life cycle of design process. Representative groups of users are exposed to the equipment at various stages in development, in a variety of testing, evaluation and interviewing situations. The users feedback obtained is then used to refine the design, with the result serving as input to the next interaction of design process. The limits of the approach are that the users cannot address any particular future needs without prior experience or knowledge about the equipment operation. The aim of this paper is to present a methodological framework that contributes to the design of the human-system interfaces, through an approach related to the users and their activities. A case study is described in which the methodological framework is being applied in development of new human-system interfaces of the thyroid uptake system. (author)

  9. Developing a Prototype ALHAT Human System Interface for Landing

    Science.gov (United States)

    Hirsh, Robert L.; Chua, Zarrin K.; Heino, Todd A.; Strahan, Al; Major, Laura; Duda, Kevin

    2011-01-01

    The goal of the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project is to safely execute a precision landing anytime/anywhere on the moon. This means the system must operate in any lighting conditions, operate in the presence of any thruster generated regolith clouds, and operate without the help of redeployed navigational aids or prepared landing site at the landing site. In order to reach this ambitious goal, computer aided technologies such as ALHAT will be needed in order to permit these landings to be done safely. Although there will be advanced autonomous capabilities onboard future landers, humans will still be involved (either onboard as astronauts or remotely from mission control) in any mission to the moon or other planetary body. Because many time critical decisions must be made quickly and effectively during the landing sequence, the Descent and Landing displays need to be designed to be as effective as possible at presenting the pertinent information to the operator, and allow the operators decisions to be implemented as quickly as possible. The ALHAT project has established the Human System Interface (HSI) team to lead in the development of these displays and to study the best way to provide operators enhanced situational awareness during landing activities. These displays are prototypes that were developed based on multiple design and feedback sessions with the astronaut office at NASA/ Johnson Space Center. By working with the astronauts in a series of plan/build/evaluate cycles, the HSI team has obtained astronaut feedback from the very beginning of the design process. In addition to developing prototype displays, the HSI team has also worked to provide realistic lunar terrain (and shading) to simulate a "out the window" view that can be adjusted to various lighting conditions (based on a desired date/time) to allow the same terrain to be viewed under varying lighting terrain. This capability will be critical to determining the

  10. A simple low cost speed log interface for oceanographic data acquisition system

    Digital Repository Service at National Institute of Oceanography (India)

    Khedekar, V.D.; Phadte, G.M.

    A speed log interface is designed with parallel Binary Coded Decimal output. This design was mainly required for the oceanographic data acquisition system as an interface between the speed log and the computer. However, this can also be used as a...

  11. A New Controller to Enhance PV System Performance Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Roshdy A AbdelRassoul

    2017-06-01

    Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

  12. Perspective of next generation training system from the viewpoints of human interfaces. Human interface kara mita kyoiku kunren system no genjo to kongo no tenbo

    Energy Technology Data Exchange (ETDEWEB)

    Nishida, S [Mitsubishi Electric Corp., Tokyo (Japan)

    1992-12-10

    This paper describes an education and training system placing its emphasis on human interfaces. The paper indicates that the currently used education and training systems lack training functions to cultivate knowledge-based judgment abilities that can find adequate solutions to events that have not been experienced previously; such judgments require deep understanding on the objects; and this training requires a system to aid the comprehension based on knowledges in the realm of recognition science for the 'human understanding'. Next, a pedagogical interface is proposed for aiding the comprehension. The paper enumerates functions indispensable for the comprehension including setting the 'loop for representation and examination', aiding the representation to examination loop, aiding roles by others, and realizing interactions through a hypothetical world. Also enumerated as fundamental techniques to structure such interface are information structuring techniques, groupware techniques, and virtual reality techniques. 19 refs., 10 figs.

  13. Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

    Science.gov (United States)

    Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

    2018-01-01

    The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

  14. Shunt attachment and method for interfacing current collection systems

    Science.gov (United States)

    Denney, Paul E.; Iyer, Natraj C.; Hannan, III, William F.

    1992-01-01

    A composite brush to shunt attachment wherein a volatile component of a composite but mostly metallic brush, used for current collection purposes, does not upon welding or brazing, adversely affect the formation of the interfacial bond with a conductive shunt which carries the current from the zone of the brush. The brush to shunt attachment for a brush material of copper-graphite composite and a shunt of copper, or substituting silver for copper as an alternative, is made through a hot isostatic pressing (HIP). The HIP process includes applying high pressure and temperature simultaneously at the brush to shunt interface, after it has been isolated or canned in a metal casing in which the air adjacent to the interface has been evacuated and the interfacial area has been sealed before the application of pressure and temperature.

  15. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  16. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  17. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  18. Configuration management plan for Machine Interface Test System (MITS)

    International Nuclear Information System (INIS)

    O'Neill, C.K.

    1980-01-01

    The discipline required by this plan will apply from the establishment of a configuration baseline until completion of the final test in the MITS. The plan applies to configured items of hardware and software as well as to the specifications and drawings for these items. The plan encompasses establishment of the facility baseline, interface definition, classes of change, change control, change paper, organizational responsibilities and relationships, test configuration (as opposed to facility), and configuration data retention

  19. Interface of the transport systems research vehicle monochrome display system to the digital autonomous terminal access communication data bus

    Science.gov (United States)

    Easley, W. C.; Tanguy, J. S.

    1986-01-01

    An upgrade of the transport systems research vehicle (TSRV) experimental flight system retained the original monochrome display system. The original host computer was replaced with a Norden 11/70, a new digital autonomous terminal access communication (DATAC) data bus was installed for data transfer between display system and host, while a new data interface method was required. The new display data interface uses four split phase bipolar (SPBP) serial busses. The DATAC bus uses a shared interface ram (SIR) for intermediate storage of its data transfer. A display interface unit (DIU) was designed and configured to read from and write to the SIR to properly convert the data from parallel to SPBP serial and vice versa. It is found that separation of data for use by each SPBP bus and synchronization of data tranfer throughout the entire experimental flight system are major problems which require solution in DIU design. The techniques used to accomplish these new data interface requirements are described.

  20. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  1. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  2. Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Z.; Rizy, D.T.

    1996-02-01

    A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

  3. A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

    Science.gov (United States)

    Hocraffer, Amy; Nam, Chang S

    2017-01-01

    A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Intelligent Systems and Advanced User Interfaces for Design, Operation, and Maintenance of Command Management Systems

    Science.gov (United States)

    Mitchell, Christine M.

    1998-01-01

    Historically Command Management Systems (CMS) have been large, expensive, spacecraft-specific software systems that were costly to build, operate, and maintain. Current and emerging hardware, software, and user interface technologies may offer an opportunity to facilitate the initial formulation and design of a spacecraft-specific CMS as well as a to develop a more generic or a set of core components for CMS systems. Current MOC (mission operations center) hardware and software include Unix workstations, the C/C++ and Java programming languages, and X and Java window interfaces representations. This configuration provides the power and flexibility to support sophisticated systems and intelligent user interfaces that exploit state-of-the-art technologies in human-machine systems engineering, decision making, artificial intelligence, and software engineering. One of the goals of this research is to explore the extent to which technologies developed in the research laboratory can be productively applied in a complex system such as spacecraft command management. Initial examination of some of the issues in CMS design and operation suggests that application of technologies such as intelligent planning, case-based reasoning, design and analysis tools from a human-machine systems engineering point of view (e.g., operator and designer models) and human-computer interaction tools, (e.g., graphics, visualization, and animation), may provide significant savings in the design, operation, and maintenance of a spacecraft-specific CMS as well as continuity for CMS design and development across spacecraft with varying needs. The savings in this case is in software reuse at all stages of the software engineering process.

  5. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  6. PC-based Multiple Information System Interface (PC/MISI) detailed design and implementation plan

    Science.gov (United States)

    Dominick, Wayne D. (Editor); Hall, Philip P.

    1985-01-01

    The design plan for the personal computer multiple information system interface (PC/MISI) project is discussed. The document is intended to be used as a blueprint for the implementation of the system. Each component is described in the detail necessary to allow programmers to implement the system. A description of the system data flow and system file structures is given.

  7. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  8. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    International Nuclear Information System (INIS)

    Tsai, Tai Ming; Wang, Wei Hui

    2009-01-01

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  9. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

    2009-01-15

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  10. System Interface for an Integrated Intelligent Safety System (ISS for Vehicle Applications

    Directory of Open Access Journals (Sweden)

    Mahammad A. Hannan

    2010-01-01

    Full Text Available This paper deals with the interface-relevant activity of a vehicle integrated intelligent safety system (ISS that includes an airbag deployment decision system (ADDS and a tire pressure monitoring system (TPMS. A program is developed in LabWindows/CVI, using C for prototype implementation. The prototype is primarily concerned with the interconnection between hardware objects such as a load cell, web camera, accelerometer, TPM tire module and receiver module, DAQ card, CPU card and a touch screen. Several safety subsystems, including image processing, weight sensing and crash detection systems, are integrated, and their outputs are combined to yield intelligent decisions regarding airbag deployment. The integrated safety system also monitors tire pressure and temperature. Testing and experimentation with this ISS suggests that the system is unique, robust, intelligent, and appropriate for in-vehicle applications.

  11. The Johnson Space Center Management Information Systems (JSCMIS): An interface for organizational databases

    Science.gov (United States)

    Bishop, Peter C.; Erickson, Lloyd

    1990-01-01

    The Management Information and Decision Support Environment (MIDSE) is a research activity to build and test a prototype of a generic human interface on the Johnson Space Center (JSC) Information Network (CIN). The existing interfaces were developed specifically to support operations rather than the type of data which management could use. The diversity of the many interfaces and their relative difficulty discouraged occasional users from attempting to use them for their purposes. The MIDSE activity approached this problem by designing and building an interface to one JSC data base - the personnel statistics tables of the NASA Personnel and Payroll System (NPPS). The interface was designed against the following requirements: generic (use with any relational NOMAD data base); easy to learn (intuitive operations for new users); easy to use (efficient operations for experienced users); self-documenting (help facility which informs users about the data base structure as well as the operation of the interface); and low maintenance (easy configuration to new applications). A prototype interface entitled the JSC Management Information Systems (JSCMIS) was produced. It resides on CIN/PROFS and is available to JSC management who request it. The interface has passed management review and is ready for early use. Three kinds of data are now available: personnel statistics, personnel register, and plan/actual cost.

  12. Computer simulation system of neural PID control on nuclear reactor

    International Nuclear Information System (INIS)

    Chen Yuzhong; Yang Kaijun; Shen Yongping

    2001-01-01

    Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

  13. Computer Program Development Specification for Tactical Interface System.

    Science.gov (United States)

    1981-07-31

    CNTL CNTL TO ONE VT~i.AE CR1 & TWELVE VT100 LCARD READER VIDEO TERMINALS, SIX LA12O) HARD- COPY TERMINALS, & VECTOR GRAPHICS RPO % TERMINAL 17%M DISK...this data into the TIS para - .. meter tables in the TISGBL common area. ICEHANDL will send test interface ICE to PSS in one of two modes: perio- dically...STOPCauss te TI sotwar toexit ,9.*9~ .r .~ * ~%.’h .9~ .. a .~ .. a. 1 , , p * % .’.-:. .m 7 P : SDSS-MMP-BI ." 31 July 1981 TCL commands authorized

  14. The Los Alamos accelerator control system data base: A generic instrumentation interface

    International Nuclear Information System (INIS)

    Dalesio, L.R.

    1990-01-01

    Controlling experimental-physics applications requires a control system that can be quickly integrated and easily modified. One aspect of the control system is the interface to the instrumentation. An instrumentation set has been chosen to implement the basic functions needed to monitor and control these applications. A data-driven interface to this instrumentation set provides the required quick integration of the control system. This type of interface is limited by its built-in capabilities. Therefore, these capabilities must provide an adequate range of functions to be of any use. The data-driven interface must support the instrumentation range requird, the events on which to read or control the instrumentation and a method for manipulating the data to calculate terms or close control loops. The database for the Los Alamos Accelerator Control System addresses these requirements. (orig.)

  15. A Formal Approach to User Interface Design using Hybrid System Theory, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Optimal Synthesis Inc.(OSI) proposes to develop an aiding tool for user interface design that is based on mathematical formalism of hybrid system theory. The...

  16. An Object-Oriented Graphical User Interface for a Reusable Rocket Engine Intelligent Control System

    Science.gov (United States)

    Litt, Jonathan S.; Musgrave, Jeffrey L.; Guo, Ten-Huei; Paxson, Daniel E.; Wong, Edmond; Saus, Joseph R.; Merrill, Walter C.

    1994-01-01

    An intelligent control system for reusable rocket engines under development at NASA Lewis Research Center requires a graphical user interface to allow observation of the closed-loop system in operation. The simulation testbed consists of a real-time engine simulation computer, a controls computer, and several auxiliary computers for diagnostics and coordination. The system is set up so that the simulation computer could be replaced by the real engine and the change would be transparent to the control system. Because of the hard real-time requirement of the control computer, putting a graphical user interface on it was not an option. Thus, a separate computer used strictly for the graphical user interface was warranted. An object-oriented LISP-based graphical user interface has been developed on a Texas Instruments Explorer 2+ to indicate the condition of the engine to the observer through plots, animation, interactive graphics, and text.

  17. Solidification interface shape control in a continuous Czochralski silicon growth system

    Science.gov (United States)

    Wang, Chenlei; Zhang, Hui; Wang, Tihu; Zheng, Lili

    2006-01-01

    In a continuous Czochralski (CCZ) growth system with a shallow and replenished melt proposed earlier, large-diameter crystals may be grown at a high pull rate and reduced melt convection. The proposed system consists of two heaters. In this paper, the relationship between the solidification interface and the power levels is established. An interface control algorithm is developed to achieve the desired interface shape by adjusting the power level of the bottom heater. The control algorithm is incorporated into an existing process model, and the efficiency of the control algorithm is tested.

  18. Interface of RETRAN/MASTER Code System for APR1400

    International Nuclear Information System (INIS)

    Ku, Keuk Jong; Kang, Sang Hee; Kim, Han Gon

    2008-01-01

    MASTER(Multi-purpose Analyzer for Static and Transient Effects of Reactors), which was developed by KAERI, is a nuclear analysis and design code which can simulate the pressurized water reactor core or boiling water reactor core in 3-dimensional geometry. RETRAN is a best-estimate code for transient analysis of Non-LOCA. RETRAN code generates neutron number density in core using point kinetics model which includes feedback reactivities and converts the neutron number density into reactor power. It is conventional that RETRAN code for power generation is roughly to extrapolate feedback reactivities which are provided by MASTER code only one time before transient analysis. The purpose of this paper is to interface RETRAN code with MASTER code by real-time processing and to supply adequate feedback reactivities to RETRAN code. So, we develop interface code called MATRAN for real-time feedback reactivity processing. And for the application of MATRAN code, we compare the results of real-time MATRAN code with those of conventional RETRAN/MASTER code

  19. Efficient Data Transfer Rate and Speed of Secured Ethernet Interface System

    Science.gov (United States)

    Ghanti, Shaila

    2016-01-01

    Embedded systems are extensively used in home automation systems, small office systems, vehicle communication systems, and health service systems. The services provided by these systems are available on the Internet and these services need to be protected. Security features like IP filtering, UDP protection, or TCP protection need to be implemented depending on the specific application used by the device. Every device on the Internet must have network interface. This paper proposes the design of the embedded Secured Ethernet Interface System to protect the service available on the Internet against the SYN flood attack. In this experimental study, Secured Ethernet Interface System is customized to protect the web service against the SYN flood attack. Secured Ethernet Interface System is implemented on ALTERA Stratix IV FPGA as a system on chip and uses the modified SYN flood attack protection method. The experimental results using Secured Ethernet Interface System indicate increase in number of genuine clients getting service from the server, considerable improvement in the data transfer rate, and better response time during the SYN flood attack. PMID:28116350

  20. Efficient Data Transfer Rate and Speed of Secured Ethernet Interface System.

    Science.gov (United States)

    Ghanti, Shaila; Naik, G M

    2016-01-01

    Embedded systems are extensively used in home automation systems, small office systems, vehicle communication systems, and health service systems. The services provided by these systems are available on the Internet and these services need to be protected. Security features like IP filtering, UDP protection, or TCP protection need to be implemented depending on the specific application used by the device. Every device on the Internet must have network interface. This paper proposes the design of the embedded Secured Ethernet Interface System to protect the service available on the Internet against the SYN flood attack. In this experimental study, Secured Ethernet Interface System is customized to protect the web service against the SYN flood attack. Secured Ethernet Interface System is implemented on ALTERA Stratix IV FPGA as a system on chip and uses the modified SYN flood attack protection method. The experimental results using Secured Ethernet Interface System indicate increase in number of genuine clients getting service from the server, considerable improvement in the data transfer rate, and better response time during the SYN flood attack.

  1. Online LDA BASED brain-computer interface system to aid disabled people

    OpenAIRE

    Apdullah Yayık; Yakup Kutlu

    2017-01-01

    This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must...

  2. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  3. Interface models

    DEFF Research Database (Denmark)

    Ravn, Anders P.; Staunstrup, Jørgen

    1994-01-01

    This paper proposes a model for specifying interfaces between concurrently executing modules of a computing system. The model does not prescribe a particular type of communication protocol and is aimed at describing interfaces between both software and hardware modules or a combination of the two....... The model describes both functional and timing properties of an interface...

  4. Formation mechanism for the nanoscale amorphous interface in pulse-welded Al/Fe bimetallic systems

    International Nuclear Information System (INIS)

    Li, Jingjing; Yu, Qian; Zhang, Zijiao; Xu, Wei; Sun, Xin

    2016-01-01

    Pulse or impact welding traditionally has been referred to as “solid-state” welding. By integrating advanced interface characterizations and diffusion calculations, we report that the nanoscale amorphous interface in the pulse-welded Al/Fe bimetallic system is formed by rapid heating and melting of a thin Al layer at the interface, diffusion of iron atoms in the liquid aluminum, and subsequent rapid quenching with diffused iron atoms in solution. This finding challenges the commonly held belief regarding the solid-state nature of the impact-based welding process for dissimilar metals. Elongated ultra-fine grains with high dislocation density and ultra-fine equiaxed grains also are observed in the weld interface vicinity on the steel and aluminum sides, respectively, which further confirms that melting and the subsequent recrystallization occurred on the aluminum side of the interface.

  5. Formation mechanism for the nanoscale amorphous interface in pulse-welded Al/Fe bimetallic systems

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jingjing [Department of Mechanical Engineering, The University of Hawaii at Manoa, Honolulu, Hawaii 96822 (United States); Yu, Qian; Zhang, Zijiao [Department of Materials Science and Engineering, Center for Electron Microscope, Zhejiang University, Hangzhou 310027 (China); Xu, Wei; Sun, Xin, E-mail: xin.sun@pnnl.gov [Pacific Northwest National Laboratory, Richland, Washington 99354 (United States)

    2016-05-16

    Pulse or impact welding traditionally has been referred to as “solid-state” welding. By integrating advanced interface characterizations and diffusion calculations, we report that the nanoscale amorphous interface in the pulse-welded Al/Fe bimetallic system is formed by rapid heating and melting of a thin Al layer at the interface, diffusion of iron atoms in the liquid aluminum, and subsequent rapid quenching with diffused iron atoms in solution. This finding challenges the commonly held belief regarding the solid-state nature of the impact-based welding process for dissimilar metals. Elongated ultra-fine grains with high dislocation density and ultra-fine equiaxed grains also are observed in the weld interface vicinity on the steel and aluminum sides, respectively, which further confirms that melting and the subsequent recrystallization occurred on the aluminum side of the interface.

  6. Compact holographic optical neural network system for real-time pattern recognition

    Science.gov (United States)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  7. 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.

  8. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

  9. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  10. Investigation on the Interface Characteristics of the Thermal Barrier Coating System through Flat Cylindrical Indenters

    Directory of Open Access Journals (Sweden)

    Shifeng Wen

    2014-01-01

    Full Text Available Thermal barrier coating (TBC systems are highly advanced material systems and usually applied to insulate components from large and prolonged heat loads by utilizing thermally insulating materials. In this study, the characteristics of the interface of thermal barrier coating systems have been simulated by the finite-element method (FEM. The emphasis was put on the stress distribution at the interface which is beneath the indenter. The effect of the interface roughness, the thermally grown oxide (TGO layer's thickness, and the modulus ratio (η of the thin film with the substrate has been considered. Finite-element results showed that the influences of the interface roughness and the TGO layer's thickness on stress distribution were important. At the same time, the residual stress distribution has been investigated in detail.

  11. Review of wireless and wearable electroencephalogram systems and brain-computer interfaces--a mini-review.

    Science.gov (United States)

    Lin, Chin-Teng; Ko, Li-Wei; Chang, Meng-Hsiu; Duann, Jeng-Ren; Chen, Jing-Ying; Su, Tung-Ping; Jung, Tzyy-Ping

    2010-01-01

    Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, lightweight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems. Copyright 2009 S. Karger AG, Basel.

  12. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  13. Parent-martensite interface structure in ferrous systems

    International Nuclear Information System (INIS)

    Ma, X.; Pond, R.C.

    2007-01-01

    Recently, a Topological Model of martensitic transformations has been presented wherein the habit plane is a semi-coherent structure, and the transformation mechanism is shown explicitly to be diffusionless. This approach is used here to model martensitic transformations in ferrous alloys. The habit plane comprises coherent (1 1 1) γ parallel (0 1 1) α terraces where the coherency strains are accommodated by a network of dislocations, originating in the martensite phase, and disconnections (transformation dislocations). The disconnections can move conservatively across the interface, thereby effecting the transformation. Since the disconnections exhibit step character, the overall habit plane deviates from the terrace plane. A range of network geometries is predicted corresponding to orientation relationships varying from Nishiyama-Wasserman to Kurdjumov-Sachs. This range of solutions includes habit planes close to {2 9 5}, {5 7 5} and {1 2 1}, in good agreement with experimental observations in various ferrous alloys

  14. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  15. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Psychological Processing in Chronic Pain: A Neural Systems Approach

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

  17. Neurophysiology and neural engineering: a review.

    Science.gov (United States)

    Prochazka, Arthur

    2017-08-01

    Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

  18. Interfaces, syntactic movement, and neural activation: A new perspective on the implementation of language in the brain

    DEFF Research Database (Denmark)

    Christensen, Ken Ramshøj

    2008-01-01

    Studies of language deficits as well as neuroimaging studies indicate that syntactic processing of displaced constituents is implemented in the brain as a distributed cortical network of modules. The data from the present fMRI study on two types of syntactic movement in Danish offers further...... support for such a distributed syntactic network. These results, together with the results from a number of other fMRI studies in the literature, form the basis for the Domain Hypothesis according to which differential activation in the subcomponents of the cortical network reflects computation...... of different syntactic domains—the interface levels between syntax, semantics, and pragmatics. The activation patters result from the interaction between movement and target domain, not (non-) canonicity or working memory per se. Specifically, movement to the CP-domain activates areas including Broca's area...

  19. An ADC-free adaptive interface circuit of resistive sensor for electronic nose system.

    Science.gov (United States)

    Chang, Chia-Lin; Chiu, Shih-Wen; Tang, Kea-Tiong

    2013-01-01

    The initial resistance of chemiresistive gas sensors could be affected by temperature, humidity, and background odors. In a sensing system, the traditional interface circuit always requires an ADC to convert analog signal to digital signal. In this paper, we propose an ADC-free adaptive interface circuit for a resistive gas sensor to read sensor signal and cancel the baseline drift. Furthermore, methanol was used to test the proposed interface circuit, which was connected with a FIGARO® gas sensor. This circuit was fabricated by TSMC 0.18 µm CMOS process, and consumed 86.41 µW under 1 V supply voltage.

  20. A Digital Interface for the Part Designers and the Fixture Designers for a Reconfigurable Assembly System

    Directory of Open Access Journals (Sweden)

    Vishwa V. Kumar

    2013-01-01

    Full Text Available This paper presents a web-based framework for interfacing product designers and fixture designers to fetch the benefits of early supplier involvement (ESI to a reconfigurable assembly system (RAS. The interfacing of the two members requires four steps, namely, collaboration chain, fixture supplier selection, knowledge share, and accommodation of service facilities so as to produce multiple products on a single assembly line. The interfacing not only provokes concurrency in the activities of product and fixture designer but also enables the assembly systems to tackle the spatial and generational variety. Among the four stages of interfacing, two steps are characterized by optimization issues, one from the product customer side and the other from the fixture designer side. To impart promptness in the optimization and hence the interaction, computationally economic tools are also presented in the paper for both of the supplier selection and fixture design optimization.