WorldWideScience

Sample records for based neural interface

  1. Hand Gesture and Neural Network Based Human Computer Interface

    Directory of Open Access Journals (Sweden)

    Aekta Patel

    2014-06-01

    Full Text Available Computer is used by every people either at their work or at home. Our aim is to make computers that can understand human language and can develop a user friendly human computer interfaces (HCI. Human gestures are perceived by vision. The research is for determining human gestures to create an HCI. Coding of these gestures into machine language demands a complex programming algorithm. In this project, We have first detected, recognized and pre-processing the hand gestures by using General Method of recognition. Then We have found the recognized image’s properties and using this, mouse movement, click and VLC Media player controlling are done. After that we have done all these functions thing using neural network technique and compared with General recognition method. From this we can conclude that neural network technique is better than General Method of recognition. In this, I have shown the results based on neural network technique and comparison between neural network method & general method.

  2. Human -Computer Interface using Gestures based on Neural Network

    Directory of Open Access Journals (Sweden)

    Aarti Malik

    2014-10-01

    Full Text Available - Gestures are powerful tools for non-verbal communication. Human computer interface (HCI is a growing field which reduces the complexity of interaction between human and machine in which gestures are used for conveying information or controlling the machine. In the present paper, static hand gestures are utilized for this purpose. The paper presents a novel technique of recognizing hand gestures i.e. A-Z alphabets, 0-9 numbers and 6 additional control signals (for keyboard and mouse control by extracting various features of hand ,creating a feature vector table and training a neural network. The proposed work has a recognition rate of 99%. .

  3. Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface

    Science.gov (United States)

    Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur

    2015-02-01

    group of awake and behaving animals. These unique findings provide preliminary evidence that efferent, volitional motor potentials can be recorded from the microchannel-based peripheral neural interface; a critical requirement for any neural interface intended to facilitate direct neural control of external technologies.

  4. Optical Neural Interfaces

    OpenAIRE

    Warden, Melissa R.; Cardin, Jessica A.; Deisseroth, Karl

    2014-01-01

    Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals.

  5. Silicon-based wire electrode array for neural interfaces

    International Nuclear Information System (INIS)

    Objectives. Metal-wire electrode arrays are widely used to record and stimulate neurons. Commonly, these devices are fabricated from a long insulated metal wire by cutting it into the proper length and using the cross-section as the electrode site. The assembly of a micro-wire electrode array with regular spacing is difficult. With the help of micro-machine technology, a silicon-based wire electrode array (SWEA) is proposed to simplify the assembling process and provide a wire-type electrode with tapered tips. Approach. Silicon wires with regular spacing coated with metal are generated from a silicon wafer through micro-fabrication and are ordered into a 3D array. A silicon wafer is cut into a comb-like structure with hexagonal teeth on both sides by anisotropic etching. To establish an array of silicon-based linear needles through isotropic wet etching, the diameters of these hexagonal teeth are reduced; their sharp edges are smoothed out and their tips are sharpened. The needle array is coated with a layer of parylene after metallization. The tips of the needles are then exposed to form an array of linear neural electrodes. With these linear electrode arrays, an array of area electrodes can be fabricated. Main results. A 6  ×  6 array of wire-type electrodes based on silicon is developed using this method. The time required to manually assemble the 3D array decreases significantly with the introduction of micro-fabricated 2D array. Meanwhile, the tip intervals in the 2D array are accurate and are controlled at no more than 1%. The SWEA is effective both in vitro and in vivo. Significance. Using this method, the SWEA can be batch-prepared in advance along with its parameters, such as spacing, length, and diameter. Thus, neural scientists can assemble proper electrode arrays in a short time. (paper)

  6. A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces.

    Science.gov (United States)

    Corradi, Federico; Indiveri, Giacomo

    2015-10-01

    Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45-54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 μV, and consumes 90 μW . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition. PMID:26513801

  7. Human -Computer Interface using Gestures based on Neural Network

    OpenAIRE

    Aarti Malik; Shalini Dhingra

    2014-01-01

    - Gestures are powerful tools for non-verbal communication. Human computer interface (HCI) is a growing field which reduces the complexity of interaction between human and machine in which gestures are used for conveying information or controlling the machine. In the present paper, static hand gestures are utilized for this purpose. The paper presents a novel technique of recognizing hand gestures i.e. A-Z alphabets, 0-9 numbers and 6 additional control signals (for keyboard and mouse contr...

  8. Miniaturized neural interfaces and implants

    Science.gov (United States)

    Stieglitz, Thomas; Boretius, Tim; Ordonez, Juan; Hassler, Christina; Henle, Christian; Meier, Wolfgang; Plachta, Dennis T. T.; Schuettler, Martin

    2012-03-01

    Neural prostheses are technical systems that interface nerves to treat the symptoms of neurological diseases and to restore sensory of motor functions of the body. Success stories have been written with the cochlear implant to restore hearing, with spinal cord stimulators to treat chronic pain as well as urge incontinence, and with deep brain stimulators in patients suffering from Parkinson's disease. Highly complex neural implants for novel medical applications can be miniaturized either by means of precision mechanics technologies using known and established materials for electrodes, cables, and hermetic packages or by applying microsystems technologies. Examples for both approaches will be introduced and discussed. Electrode arrays for recording of electrocorticograms during presurgical epilepsy diagnosis have been manufactured using approved materials and a marking laser to achieve an integration density that is adequate in the context of brain machine interfaces, e.g. on the motor cortex. Microtechnologies have to be used for further miniaturization to develop polymer-based flexible and light weighted electrode arrays to interface the peripheral and central nervous system. Polyimide as substrate and insulation material will be discussed as well as several application examples for nerve interfaces like cuffs, filament like electrodes and large arrays for subdural implantation.

  9. Technology for integrated circuit micropackages for neural interfaces, based on gold–silicon wafer bonding

    International Nuclear Information System (INIS)

    Progress in the development of active neural interface devices requires a very compact method for protecting integrated circuits (ICs). In this paper, a method of forming micropackages is described in detail. The active areas of the chips are sealed in gas-filled cavities of the cap wafer in a wafer-bonding process using Au–Si eutectic. We describe the simple additions to the design of the IC, the post-processing of the active wafer and the required features of the cap wafer. The bonds, which were made at pressure and temperature levels within the range of the tolerance of complementary metal–oxide–semiconductor ICs, are strong enough to meet MIL STD 883G, Method 2019.8 (shear force test). We show results that suggest a method for wafer-scale gross leak testing using FTIR. This micropackaging method requires no special fabrication process and is based on using IC compatible or conventional fabrication steps. (paper)

  10. Topographic guidance based on microgrooved electroactive composite films for neural interface.

    Science.gov (United States)

    Shi, Xiaoyao; Xiao, Yinghong; Xiao, Hengyang; Harris, Gary; Wang, Tongxin; Che, Jianfei

    2016-09-01

    Topographical features are essential to neural interface for better neuron attachment and growth. This paper presents a facile and feasible route to fabricate an electroactive and biocompatible micro-patterned Single-walled carbon nanotube/poly(3,4-ethylenedioxythiophene) composite films (SWNT/PEDOT) for interface of neural electrodes. The uniform SWNT/PEDOT composite films with nanoscale pores and microscale grooves significantly enlarged the electrode-electrolyte interface, facilitated ion transfer within the bulk film, and more importantly, provided topology cues for the proliferation and differentiation of neural cells. Electrochemical analyses indicated that the introduction of PEDOT greatly improved the stability of the SWNT/PEDOT composite film and decreased the electrode/electrolyte interfacial impedance. Further, in vitro culture of rat pheochromocytoma (PC12) cells and MTT testing showed that the grooved SWNT/PEDOT composite film was non-toxic and favorable to guide the growth and extension of neurite. Our results demonstrated that the fabricated microscale groove patterns were not only beneficial in the development of models for nervous system biology, but also in creating therapeutic approaches for nerve injuries. PMID:27295493

  11. Regenerative Electrode Interfaces for Neural Prostheses.

    Science.gov (United States)

    Thompson, Cort H; Zoratti, Marissa J; Langhals, Nicholas B; Purcell, Erin K

    2016-04-01

    Neural prostheses are electrode arrays implanted in the nervous system that record or stimulate electrical activity in neurons. Rapid growth in the use of neural prostheses in research and clinical applications has occurred in recent years, but instability and poor patency in the tissue-electrode interface undermines the longevity and performance of these devices. The application of tissue engineering strategies to the device interface is a promising approach to improve connectivity and communication between implanted electrodes and local neurons, and several research groups have developed new and innovative modifications to neural prostheses with the goal of seamless device-tissue integration. These approaches can be broadly categorized based on the strategy used to maintain and regenerate neurons at the device interface: (1) redesign of the prosthesis architecture to include finer-scale geometries and/or provide topographical cues to guide regenerating neural outgrowth, (2) incorporation of material coatings and bioactive molecules on the prosthesis to improve neuronal growth, viability, and adhesion, and (3) inclusion of cellular grafts to replenish the local neuron population or provide a target site for reinnervation (biohybrid devices). In addition to stabilizing the contact between neurons and electrodes, the potential to selectively interface specific subpopulations of neurons with individual electrode sites is a key advantage of regenerative interfaces. In this study, we review the development of regenerative interfaces for applications in both the peripheral and central nervous system. Current and future development of regenerative interfaces has the potential to improve the stability and selectivity of neural prostheses, improving the patency and resolution of information transfer between neurons and implanted electrodes. PMID:26421660

  12. A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces.

    Science.gov (United States)

    Chen, Yi; Yao, Enyi; Basu, Arindam

    2016-06-01

    Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X. PMID:26672048

  13. Neural networks in electrical capacitance tomography (ECT)-based interface detection

    International Nuclear Information System (INIS)

    Tomographic systems have been tested in separators for estimating the distribution of the three phases (including emulsion and foam) via interface detection in various laboratories. Experiments and simulations show that electrical capacitance tomography (ECT) is capable of detecting the interface accurately in spite of the high conductivity of the water present. Due to the high conductivity of the water/brine in the pipe separator, the sensitivity of the capacitance measurements is to a certain extent immune to variations in material properties. In a series of preliminary tests, capacitance tomography was used to estimate the interface in a pipe separator containing oil and water/brine. Results obtained from laboratory scale models are presented and discussed with some information on the uncertainties involved. Artificial neural networks exhibit enhanced ability to mask variations in unwanted/unimportant parameters in the separation process, thus reducing the complexities involved in the solution of the essentially underdetermined system of equations evolving out of different models developed for the system. Due to ample data being available from tomographic systems, a data driven soft sensor (virtual sensor) approach is also discussed with some considerations on processing times to address the potential of the ECT in real time measurement and control applications

  14. Characterization of a-SiCx:H thin films as an encapsulation material for integrated silicon based neural interface devices

    OpenAIRE

    Hsu, Jui-Mei; Tathireddy, Prashant; Rieth, Loren; Normann, A. Richard; Solzbacher, Florian

    2007-01-01

    A fully integrated, wireless neural interface device is being developed to free patients from the restriction and risk of infection associated with a transcutaneous wired connection. This device requires a hermetic, biocompatible encapsulation layer at the interface between the device and the neural tissue to maintain long-term recording/stimulating performance of the device. Hydrogenated amorphous silicon carbide (a-SiCx:H) films deposited by a plasma enhanced chemical vapor deposition using...

  15. EDITORIAL: Focus on the neural interface Focus on the neural interface

    Science.gov (United States)

    Durand, Dominique M.

    2009-10-01

    The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

  16. Characterization of a-SiCx:H thin films as an encapsulation material for integrated silicon based neural interface devices

    International Nuclear Information System (INIS)

    A fully integrated, wireless neural interface device is being developed to free patients from the restriction and risk of infection associated with a transcutaneous wired connection. This device requires a hermetic, biocompatible encapsulation layer at the interface between the device and the neural tissue to maintain long-term recording/stimulating performance of the device. Hydrogenated amorphous silicon carbide (a-SiCx:H) films deposited by a plasma enhanced chemical vapor deposition using SiH4, CH4, and H2 precursors were investigated as the encapsulation layer for such device. Si-C bond density, measured by Fourier transform infrared absorption spectrometer, suggests that deposition conditions with increased hydrogen dilution, increased temperature, and low silane flow typically result in increase of Si-C bond density. From the variable angle spectroscopic ellipsometry measurement, no dissolution of a-SiCx:H was observed during soaking tests in 90 deg. C phosphate buffered saline. Conformal coating of the a-SiCx:H in Utah electrode array was observed by scanning electron microscope. Electrical properties were studied by impedance spectroscopy to investigate the performance of a-SiCx:H as an encapsulation layer, and the results showed long term stability of the material

  17. 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

    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.

  18. Feasibility study for future implantable neural-silicon interface devices.

    Science.gov (United States)

    Al-Armaghany, Allann; Yu, Bo; Mak, Terrence; Tong, Kin-Fai; Sun, Yihe

    2011-01-01

    The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures. PMID:22254974

  19. NeuroRex: A Clinical Neural Interface Roadmap for EEG-based Brain Machine Interfaces to a Lower Body Robotic Exoskeleton*

    OpenAIRE

    Jose L. Contreras-Vidal; Grossman, Robert G.

    2013-01-01

    In this communication, a translational clinical brain-machine interface (BMI) roadmap for an EEG-based BMI to a robotic exoskeleton (NeuroRex) is presented. This multi-faceted project addresses important engineering and clinical challenges: It addresses the validation of an intelligent, self-balancing, robotic lower-body and trunk exoskeleton (Rex) augmented with EEG-based BMI capabilities to interpret user intent to assist a mobility-impaired person to walk independently. The goal is to impr...

  20. Progress towards biocompatible intracortical microelectrodes for neural interfacing applications

    Science.gov (United States)

    Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

    2015-02-01

    To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing.

  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

    2015-08-01

    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. PMID:26737158

  2. Titania nanotube arrays as interfaces for neural prostheses

    International Nuclear Information System (INIS)

    Neural prostheses have become ever more acceptable treatments for many different types of neurological damage and disease. Here we investigate the use of two different morphologies of titania nanotube arrays as interfaces to advance the longevity and effectiveness of these prostheses. The nanotube arrays were characterized for their nanotopography, crystallinity, conductivity, wettability, surface mechanical properties and adsorption of key proteins: fibrinogen, albumin and laminin. The loosely packed nanotube arrays fabricated using a diethylene glycol based electrolyte, contained a higher presence of the anatase crystal phase and were subsequently more conductive. These arrays yielded surfaces with higher wettability and lower modulus than the densely packed nanotube arrays fabricated using water based electrolyte. Further the adhesion, proliferation and differentiation of the C17.2 neural stem cell line was investigated on the nanotube arrays. The proliferation ratio of the cells as well as the level of neuronal differentiation was seen to increase on the loosely packed arrays. The results indicate that loosely packed nanotube arrays similar to the ones produced here with a DEG based electrolyte, may provide a favorable template for growth and maintenance of C17.2 neural stem cell line. - Highlights: • Titania nanotube arrays can be fabricated with to have loosely or densely packed morphologies. • Titania nanotube arrays support higher C17.2 neural stem cell adhesion and proliferation. • Titania nanotube arrays support higher C17.2 neural stem cell differentiation towards neuronal lineage

  3. Titania nanotube arrays as interfaces for neural prostheses

    Energy Technology Data Exchange (ETDEWEB)

    Sorkin, Jonathan A. [Department of Mechanical Engineering, Colorado State University, Fort Collins CO 80523 (United States); Hughes, Stephen [Department of Chemical and Biological Engineering, Colorado State University, Fort Collins CO 80523 (United States); School of Biomedical Engineering, Colorado State University, Fort Collins CO 80523 (United States); Soares, Paulo [Department of Mechanical Engineering, Polytechnic School, Pontifícia Universidade Católica do Paraná, Curitiba, PR 80215-901 (Brazil); Popat, Ketul C., E-mail: ketul.popat@colostate.edu [Department of Mechanical Engineering, Colorado State University, Fort Collins CO 80523 (United States); School of Biomedical Engineering, Colorado State University, Fort Collins CO 80523 (United States)

    2015-04-01

    Neural prostheses have become ever more acceptable treatments for many different types of neurological damage and disease. Here we investigate the use of two different morphologies of titania nanotube arrays as interfaces to advance the longevity and effectiveness of these prostheses. The nanotube arrays were characterized for their nanotopography, crystallinity, conductivity, wettability, surface mechanical properties and adsorption of key proteins: fibrinogen, albumin and laminin. The loosely packed nanotube arrays fabricated using a diethylene glycol based electrolyte, contained a higher presence of the anatase crystal phase and were subsequently more conductive. These arrays yielded surfaces with higher wettability and lower modulus than the densely packed nanotube arrays fabricated using water based electrolyte. Further the adhesion, proliferation and differentiation of the C17.2 neural stem cell line was investigated on the nanotube arrays. The proliferation ratio of the cells as well as the level of neuronal differentiation was seen to increase on the loosely packed arrays. The results indicate that loosely packed nanotube arrays similar to the ones produced here with a DEG based electrolyte, may provide a favorable template for growth and maintenance of C17.2 neural stem cell line. - Highlights: • Titania nanotube arrays can be fabricated with to have loosely or densely packed morphologies. • Titania nanotube arrays support higher C17.2 neural stem cell adhesion and proliferation. • Titania nanotube arrays support higher C17.2 neural stem cell differentiation towards neuronal lineage.

  4. A Survey of Neural Front End Amplifiers and Their Requirements toward Practical Neural Interfaces

    Directory of Open Access Journals (Sweden)

    Eric Bharucha

    2014-11-01

    Full Text Available When designing an analog front-end for neural interfacing, it is hard to evaluate the interplay of priority features that one must upkeep. Given the competing nature of design requirements for such systems a good understanding of these trade-offs is necessary. Low power, chip size, noise control, gain, temporal resolution and safety are the salient ones. There is a need to expose theses critical features for high performance neural amplifiers as the density and performance needs of these systems increases. This review revisits the basic science behind the engineering problem of extracting neural signal from living tissue. A summary of architectures and topologies is then presented and illustrated through a rich set of examples based on the literature. A survey of existing systems is presented for comparison based on prevailing performance metrics.

  5. Self-aligned tip deinsulation of atomic layer deposited Al2O3 and parylene C coated Utah electrode array based neural interfaces

    International Nuclear Information System (INIS)

    The recently developed alumina and parylene C bilayer encapsulation improved the lifetime of neural interfaces. Tip deinsulation of Utah electrode array based neural interfaces is challenging due to the complex 3D geometries and high aspect ratios of the devices. A three-step self-aligned process was developed for tip deinsulation of bilayer encapsulated arrays. The deinsulation process utilizes laser ablation to remove parylene C, O2 reactive ion etching to remove carbon and parylene residues, and buffered oxide etch to remove alumina deposited by atomic layer deposition, and expose the IrOx tip metallization. The deinsulated iridium oxide area was characterized by scanning electron microscopy, atomic force microscopy, x-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy to determine the morphology, surface morphology, composition, and electrical properties of the deposited layers and deinsulated tips. The alumina layer was found to prevent the formation of micro cracks on iridium oxide during the laser ablation process, which has been previously reported as a challenge for laser deinsulation of parylene films. The charge injection capacity, charge storage capacity, and impedance of deinsulated iridium oxide were characterized to determine the deinsulation efficacy compared to parylene-only insulation. Deinsulated iridium oxide with bilayer encapsulation had higher charge injection capacity (240 versus 320 nC) and similar electrochemical impedance (2.5 versus 2.5 kΩ) compared to deinsulated iridium oxide with only parylene coating for an area of 2 × 10−4 cm2. Tip impedances were in the range of 20–50 kΩ, with a median of 32 kΩ and a standard deviation of 30 kΩ, showing the effectiveness of the self-aligned deinsulation process for alumina and parylene C bilayer encapsulation. The relatively uniform tip impedance values demonstrated the consistency of tip exposures. (paper)

  6. Conjugated Polymer Actuators for Articulating Neural Probes and Electrode Interfaces

    Science.gov (United States)

    Daneshvar, Eugene Dariush

    This thesis investigated the potential use of polypyrrole (PPy) doped with dodecylbenzenesulfonate (DBS) to controllably articulate (bend or guide) flexible neural probes and electrodes. PPy(DBS) actuation performance was characterized in the ionic mixture and temperature found in the brain. Nearly all the ions in aCSF were exchanged into the PPy---the cations Na +, K+, Mg2+, Ca2+, as well as the anion PO43-; Cl- was not present. Nevertheless, deflections in aCSF were comparable to those in NaDBS and they were monotonic with oxidation level: strain increased upon reduction, with no reversal of motion despite the mixture of ionic charges and valences being exchanged. Actuation depended on temperature. Upon warming, the cyclic voltammograms showed additional peaks and an increase of 70% in the consumed charge. Actuation strain was monotonic under these conditions, demonstrating that conducting polymer actuators can indeed be used for neural interface and neural probe applications. In addition, a novel microelectro-mechanical system (MEMS) was developed to measure previously disregarded residual stress in a bilayer actuator. Residual stresses are a major concern for MEMS devices as that they can dramatically influence their yield and functionality. This device introduced a new technique to measure micro-scaled actuation forces that may be useful for characterization of other MEMS actuators. Finally, a functional movable parylene-based neural electrode prototype was developed. Employing PPy(DBS) actuators, electrode projections were successfully controlled to either remain flat or actuate out-of-plane and into a brain phantom during insertion. An electrode projection 800 microm long and 50 microm wide was able to deflect almost 800 microm away from the probe substrate. Applications that do not require insertion into tissue may also benefit from the electrode projections described here. Implantable neural interface devices are a critical component to a broad class of

  7. EEG-Based Classification of New Imagery Tasks Using Three-Layer Feedforward Neural Network Classifier for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2006-10-01

    In this paper proposes the classification method of new imagery tasks for simple binary commands approach to a brain-computer interface (BCI). An analysis of imaginary tasks as “yes/no” have been proposed. Since BCI is very helpful technology for the patients who are suffering from severe motor disabilities. The BCI applications can be realized by using an electroencephalogram (EEG) signals recording at the scalp surface through the electrodes. Six healthy subjects (three males and three females), aged 23-30 years, were volunteered to participate in the experiment. During the experiment, 10-questions were used to be stimuli. The feature extraction of the event-related synchronization and event-related desynchronization (ERD/ERS) responses can be determined by the slope coefficient and Euclidian distance (SCED) method. The method uses the three-layer feedforward neural network based on a simple backpropagation algorithm to classify the two feature vectors. The experimental results of the proposed method show the average accuracy rates of 81.5 and 78.8% when the subjects imagine to “yes” and “no”, respectively.

  8. A CMOS Neural Interface for a Multichannel Vestibular Prosthesis.

    Science.gov (United States)

    Hageman, Kristin N; Kalayjian, Zaven K; Tejada, Francisco; Chiang, Bryce; Rahman, Mehdi A; Fridman, Gene Y; Dai, Chenkai; Pouliquen, Philippe O; Georgiou, Julio; Della Santina, Charles C; Andreou, Andreas G

    2016-04-01

    We present a high-voltage CMOS neural-interface chip for a multichannel vestibular prosthesis (MVP) that measures head motion and modulates vestibular nerve activity to restore vision- and posture-stabilizing reflexes. This application specific integrated circuit neural interface (ASIC-NI) chip was designed to work with a commercially available microcontroller, which controls the ASIC-NI via a fast parallel interface to deliver biphasic stimulation pulses with 9-bit programmable current amplitude via 16 stimulation channels. The chip was fabricated in the ONSemi C5 0.5 micron, high-voltage CMOS process and can accommodate compliance voltages up to 12 V, stimulating vestibular nerve branches using biphasic current pulses up to 1.45±0.06 mA with durations as short as 10 μs/phase. The ASIC-NI includes a dedicated digital-to-analog converter for each channel, enabling it to perform complex multipolar stimulation. The ASIC-NI replaces discrete components that cover nearly half of the 2nd generation MVP (MVP2) printed circuit board, reducing the MVP system size by 48% and power consumption by 17%. Physiological tests of the ASIC-based MVP system (MVP2A) in a rhesus monkey produced reflexive eye movement responses to prosthetic stimulation similar to those observed when using the MVP2. Sinusoidal modulation of stimulus pulse rate from 68-130 pulses per second at frequencies from 0.1 to 5 Hz elicited appropriately-directed slow phase eye velocities ranging in amplitude from 1.9-16.7 (°)/s for the MVP2 and 2.0-14.2 (°)/s for the MVP2A. The eye velocities evoked by MVP2 and MVP2A showed no significant difference ( t-test, p=0.34), suggesting that the MVP2A achieves performance at least as good as the larger MVP2. PMID:25974945

  9. Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications

    OpenAIRE

    Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

    2014-01-01

    To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from tra...

  10. Characterization of a-SiC(x):H thin films as an encapsulation material for integrated silicon based neural interface devices.

    Science.gov (United States)

    Hsu, Jui-Mei; Tathireddy, Prashant; Rieth, Loren; Normann, A Richard; Solzbacher, Florian

    2007-11-01

    A fully integrated, wireless neural interface device is being developed to free patients from the restriction and risk of infection associated with a transcutaneous wired connection. This device requires a hermetic, biocompatible encapsulation layer at the interface between the device and the neural tissue to maintain long-term recording/stimulating performance of the device. Hydrogenated amorphous silicon carbide (a-SiC(x):H) films deposited by a plasma enhanced chemical vapor deposition using SiH(4), CH(4), and H(2) precursors were investigated as the encapsulation layer for such device. Si-C bond density, measured by Fourier transform infrared absorption spectrometer, suggests that deposition conditions with increased hydrogen dilution, increased temperature, and low silane flow typically result in increase of Si-C bond density. From the variable angle spectroscopic ellipsometry measurement, no dissolution of a-SiC(x):H was observed during soaking tests in 90°C phosphate buffered saline. Conformal coating of the a-SiC(x):H in Utah electrode array was observed by scanning electron microscope. Electrical properties were studied by impedance spectroscopy to investigate the performance of a-SiC(x):H as an encapsulation layer, and the results showed long term stability of the material. PMID:18437249

  11. Early interfaced neural activity from chronic amputated nerves

    Directory of Open Access Journals (Sweden)

    Kshitija Garde

    2009-05-01

    Full Text Available Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive inflammation, currently limit their long-term use. Here we demonstrate that enticement of peripheral nerve regeneration through a non-obstructive multi-electrode array, after either acute or chronic nerve amputation, offers a viable alternative to obtain early neural recordings and to enhance long-term interfacing of nerve activity. Non restrictive electrode arrays placed in the path of regenerating nerve fibers allowed the recording of action potentials as early as 8 days post-implantation with high signal-to-noise ratio, as long as 3 months in some animals, and with minimal inflammation at the nerve tissue-metal electrode interface. Our findings suggest that regenerative on-dependent multi-electrode arrays of open design allow the early and stable interfacing of neural activity from amputated peripheral nerves and might contribute towards conveying full neural control and sensory feedback to users of robotic prosthetic devices. .

  12. 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

  13. Robust penetrating microelectrodes for neural interfaces realized by titanium micromachining.

    Science.gov (United States)

    McCarthy, Patrick T; Otto, Kevin J; Rao, Masaru P

    2011-06-01

    Neural prosthetic interfaces based upon penetrating microelectrode devices have broadened our understanding of the brain and have shown promise for restoring neurological functions lost to disease, stroke, or injury. However, the eventual viability of such devices for use in the treatment of neurological dysfunction may be ultimately constrained by the intrinsic brittleness of silicon, the material most commonly used for manufacture of penetrating microelectrodes. This brittleness creates predisposition for catastrophic fracture, which may adversely affect the reliability and safety of such devices, due to potential for fragmentation within the brain. Herein, we report the development of titanium-based penetrating microelectrodes that seek to address this potential future limitation. Titanium provides advantage relative to silicon due to its superior fracture toughness, which affords potential for creation of robust devices that are resistant to catastrophic failure. Realization of these devices is enabled by recently developed techniques which provide opportunity for fabrication of high-aspect-ratio micromechanical structures in bulk titanium substrates. Details are presented regarding the design, fabrication, mechanical testing, in vitro functional characterization, and preliminary in vivo testing of devices intended for acute recording in rat auditory cortex and thalamus, both independently and simultaneously. PMID:21360044

  14. Early Interfaced Neural Activity from Chronic Amputated Nerves

    OpenAIRE

    Garde, Kshitija; Keefer, Edward; Botterman, Barry; Galvan, Pedro; Romero, Mario I.

    2009-01-01

    Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive i...

  15. Early interfaced neural activity from chronic amputated nerves

    OpenAIRE

    Kshitija Garde; Barry Botterman; Pedro Galvan

    2009-01-01

    Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive i...

  16. 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

  17. Development of bioactive conducting polymers for neural interfaces.

    Science.gov (United States)

    Poole-Warren, Laura; Lovell, Nigel; Baek, Sungchul; Green, Rylie

    2010-01-01

    Bioelectrodes for neural recording and neurostimulation are an integral component of a number of neuroprosthetic devices, including the commercially available cochlear implant, and developmental devices, such as the bionic eye and brain-machine interfaces. Current electrode designs limit the application of such devices owing to suboptimal material properties that lead to minimal interaction with the target neural tissue and the formation of fibrotic capsules. In designing an ideal bioelectrode, a number of design criteria must be considered with respect to physical, mechanical, electrical and biological properties. Conducting polymers have the potential to address the synergistic interaction of these properties and show promise as superior coatings for next-generation electrodes in implant devices. PMID:20021239

  18. ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

    Directory of Open Access Journals (Sweden)

    Rylie A Green

    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.

  19. Organic electrode coatings for next-generation neural interfaces.

    Science.gov (United States)

    Aregueta-Robles, Ulises A; Woolley, Andrew J; Poole-Warren, Laura A; Lovell, Nigel H; Green, Rylie A

    2014-01-01

    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. PMID:24904405

  20. Drug release from porous silicon for stable neural interface

    International Nuclear Information System (INIS)

    70 μm-thick porous Si (PSi) layer with the pore size of 11.1 ± 7.6 nm was formed on an 8-in. Si wafer via an anodization process for the microfabrication of a microelectrode to record neural signals. To reduce host tissue responses to the microelectrode and achieve a stable neural interface, water-soluble dexamethesone (Dex) was loaded into the PSi via incubation with the drug solution overnight. After the drug loading process, the pore size of PSi reduced to 4.7 ± 2.6 nm on the basis of scanning electron microscopic (SEM) images, while its wettability was remarkably enhanced. Fluorescence images demonstrated that Dex was loaded into the porous structure of the PSi. Degradation rate of the PSi was investigated by incubation in distilled water for 21 days. Moreover, the drug release profile of the Dex-loaded PSi was a combination of an initial burst release and subsequent sustained release. To evaluate cellular responses to the drug release from the PSi, primary astrocytes were seeded on the surface of samples. After 2 days of culture, the Dex-loaded PSi could not only moderately prevent astrocyte adhesion in comparison with Si, but also more effectively suppress the activation of primary astrocytes than unloaded PSi due to the drug release. Therefore, it might be an effective method to reduce host tissue responses and stabilize the quality of the recorded neural signal by means of loading drugs into the PSi component of the microelectrode.

  1. Drug release from porous silicon for stable neural interface

    Science.gov (United States)

    Sun, Tao; Tsang, Wei Mong; Park, Woo-Tae

    2014-02-01

    70 μm-thick porous Si (PSi) layer with the pore size of 11.1 ± 7.6 nm was formed on an 8-in. Si wafer via an anodization process for the microfabrication of a microelectrode to record neural signals. To reduce host tissue responses to the microelectrode and achieve a stable neural interface, water-soluble dexamethesone (Dex) was loaded into the PSi via incubation with the drug solution overnight. After the drug loading process, the pore size of PSi reduced to 4.7 ± 2.6 nm on the basis of scanning electron microscopic (SEM) images, while its wettability was remarkably enhanced. Fluorescence images demonstrated that Dex was loaded into the porous structure of the PSi. Degradation rate of the PSi was investigated by incubation in distilled water for 21 days. Moreover, the drug release profile of the Dex-loaded PSi was a combination of an initial burst release and subsequent sustained release. To evaluate cellular responses to the drug release from the PSi, primary astrocytes were seeded on the surface of samples. After 2 days of culture, the Dex-loaded PSi could not only moderately prevent astrocyte adhesion in comparison with Si, but also more effectively suppress the activation of primary astrocytes than unloaded PSi due to the drug release. Therefore, it might be an effective method to reduce host tissue responses and stabilize the quality of the recorded neural signal by means of loading drugs into the PSi component of the microelectrode.

  2. Drug release from porous silicon for stable neural interface

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Tao, E-mail: taosun@hotmail.com.hk [Institute of Microelectronics, Agency for Science, Technology and Research (A-STAR) (Singapore); Tsang, Wei Mong [Institute of Microelectronics, Agency for Science, Technology and Research (A-STAR) (Singapore); Park, Woo-Tae [Institute of Microelectronics, Agency for Science, Technology and Research (A-STAR) (Singapore); Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul (Korea, Republic of)

    2014-02-15

    70 μm-thick porous Si (PSi) layer with the pore size of 11.1 ± 7.6 nm was formed on an 8-in. Si wafer via an anodization process for the microfabrication of a microelectrode to record neural signals. To reduce host tissue responses to the microelectrode and achieve a stable neural interface, water-soluble dexamethesone (Dex) was loaded into the PSi via incubation with the drug solution overnight. After the drug loading process, the pore size of PSi reduced to 4.7 ± 2.6 nm on the basis of scanning electron microscopic (SEM) images, while its wettability was remarkably enhanced. Fluorescence images demonstrated that Dex was loaded into the porous structure of the PSi. Degradation rate of the PSi was investigated by incubation in distilled water for 21 days. Moreover, the drug release profile of the Dex-loaded PSi was a combination of an initial burst release and subsequent sustained release. To evaluate cellular responses to the drug release from the PSi, primary astrocytes were seeded on the surface of samples. After 2 days of culture, the Dex-loaded PSi could not only moderately prevent astrocyte adhesion in comparison with Si, but also more effectively suppress the activation of primary astrocytes than unloaded PSi due to the drug release. Therefore, it might be an effective method to reduce host tissue responses and stabilize the quality of the recorded neural signal by means of loading drugs into the PSi component of the microelectrode.

  3. Poly(3,4-ethylenedioxythiophene) as a Micro-Neural Interface Material for Electrostimulation

    OpenAIRE

    Seth J Wilks; Sarah M Richardson-Burn; Hendricks, Jeffrey L.; David Martin; Otto, Kevin J.

    2009-01-01

    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-ba...

  4. 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

  5. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

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

    Directory of Open Access Journals (Sweden)

    Mario I Romero

    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.

  7. Graphene-Based Interfaces Do Not Alter Target Nerve Cells.

    Science.gov (United States)

    Fabbro, Alessandra; Scaini, Denis; León, Verónica; Vázquez, Ester; Cellot, Giada; Privitera, Giulia; Lombardi, Lucia; Torrisi, Felice; Tomarchio, Flavia; Bonaccorso, Francesco; Bosi, Susanna; Ferrari, Andrea C; Ballerini, Laura; Prato, Maurizio

    2016-01-26

    Neural-interfaces rely on the ability of electrodes to transduce stimuli into electrical patterns delivered to the brain. In addition to sensitivity to the stimuli, stability in the operating conditions and efficient charge transfer to neurons, the electrodes should not alter the physiological properties of the target tissue. Graphene is emerging as a promising material for neuro-interfacing applications, given its outstanding physico-chemical properties. Here, we use graphene-based substrates (GBSs) to interface neuronal growth. We test our GBSs on brain cell cultures by measuring functional and synaptic integrity of the emerging neuronal networks. We show that GBSs are permissive interfaces, even when uncoated by cell adhesion layers, retaining unaltered neuronal signaling properties, thus being suitable for carbon-based neural prosthetic devices. PMID:26700626

  8. 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.

  9. Human facial neural activities and gesture recognition for machine-interfacing applications

    Directory of Open Access Journals (Sweden)

    Hamedi M

    2011-12-01

    Full Text Available M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transportation Research Alliance, 3Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia (UTM, Johor Bahru, Malaysia; 4College of Innovative Management, Valaya Alongkorn Rajabhat University, Pathum Thani, 5Nanoscale Science and Engineering Research Alliance (N'SERA, Advanced Research Center for Photonics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, ThailandAbstract: The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy

  10. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

    Directory of Open Access Journals (Sweden)

    Michael Polanco

    2016-06-01

    Full Text Available The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes.

  11. Neural Network based Consumption Forecasting

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    2016-01-01

    active participation in the future smart grid environment. One of the main obstacles for making optimal energy consumption is to have good predictions of the future energy consumption. This study is based on real consumption data from eight houses in Denmark. There are designed two different prediction...... models. It is shown that both of the predictions model produce a better consumption prediction then a naïve model. Seen in this perspective is it concluded that it is possible to use Artificial Neural Networks for predicting the energy consumption in ordinary family houses....

  12. A neurally-interfaced hand prosthesis tuned inter-hemispheric communication

    OpenAIRE

    Tombini, M.; Tecchio, F.; Porcaro, C.; Assenza, G.; Di Pino, G.; Pellegrino, G.; Rossini, P.M.

    2012-01-01

    Purpose: This work investigates how a direct bidirectional connection between brain and hand prosthesis modifies the bi-hemispheric sensorimotor system devoted to the movement control of the lost limb. Hand prostheses are often unable to satisfy users' expectations, mostly due to the poor performance of their interfacing system. Neural Interfaces implanted inside nerves of the stump offer the advantage of using the bidirectional neural pathways 'naturally' dispatching signals to control prope...

  13. Brain emotional learning based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Abdolreza Asadi Ghanbari

    2012-09-01

    Full Text Available A brain computer interface (BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Classification is crucial as it has a substantial effect on the BCI speed and bit rate. Recent developments of brain-computer interfaces (BCIs bring forward some challenging problems to the machine learning community, of which classification of time-varying electrophysiological signals is a crucial one. Constructing adaptive classifiers is a promising approach to deal with this problem. In this paper, we introduce adaptive classifiers for classify electroencephalogram (EEG signals. The adaptive classifier is brain emotional learning based adaptive classifier (BELBAC, which is based on emotional learning process. The main purpose of this research is to use a structural model based on the limbic system of mammalian brain, for decision making and control engineering applications. We have adopted a network model developed by Moren and Balkenius, as a computational model that mimics amygdala, orbitofrontal cortex, thalamus, sensory input cortex and generally, those parts of the brain thought responsible for processing emotions. The developed method was compared with other methods used for EEG signals classification (support vector machine (SVM and two different neural network types (MLP, PNN. The result analysis demonstrated an efficiency of the proposed approach.

  14. Adaptive Control Based On Neural Network

    OpenAIRE

    Wei, Sun; Lujin, Zhang; Jinhai, Zou; Siyi, Miao

    2009-01-01

    In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed cont...

  15. Brain-computer interfaces: an overview of the hardware to record neural signals from the cortex.

    Science.gov (United States)

    Stieglitz, Thomas; Rubehn, Birthe; Henle, Christian; Kisban, Sebastian; Herwik, Stanislav; Ruther, Patrick; Schuettler, Martin

    2009-01-01

    Brain-computer interfaces (BCIs) record neural signals from cortical origin with the objective to control a user interface for communication purposes, a robotic artifact or artificial limb as actuator. One of the key components of such a neuroprosthetic system is the neuro-technical interface itself, the electrode array. In this chapter, different designs and manufacturing techniques will be compared and assessed with respect to scaling and assembling limitations. The overview includes electroencephalogram (EEG) electrodes and epicortical brain-machine interfaces to record local field potentials (LFPs) from the surface of the cortex as well as intracortical needle electrodes that are intended to record single-unit activity. Two exemplary complementary technologies for micromachining of polyimide-based arrays and laser manufacturing of silicone rubber are presented and discussed with respect to spatial resolution, scaling limitations, and system properties. Advanced silicon micromachining technologies have led to highly sophisticated intracortical electrode arrays for fundamental neuroscientific applications. In this chapter, major approaches from the USA and Europe will be introduced and compared concerning complexity, modularity, and reliability. An assessment of the different technological solutions comparable to a strength weaknesses opportunities, and threats (SWOT) analysis might serve as guidance to select the adequate electrode array configuration for each control paradigm and strategy to realize robust, fast, and reliable BCIs. PMID:19660664

  16. Microprocessor-based interface for oceanography

    Science.gov (United States)

    Hansen, G. R.

    1979-01-01

    Ocean floor imaging system incorporates five identical microprocessor-based interface units each assigned to specific sonar instrument to simplify system. Central control module based on same microprocessor eliminates need for custom tailoring hardware interfaces for each instrument.

  17. Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces

    Science.gov (United States)

    Wang, Yiwen; Principe, Jose C.

    2010-10-01

    Recently, the authors published a sequential decoding algorithm for motor brain-machine interfaces (BMIs) that infers movement directly from spike trains and produces a new kinematic output every time an observation of neural activity is present at its input. Such a methodology also needs a special instantaneous neuronal encoding model to relate instantaneous kinematics to every neural spike activity. This requirement is unlike the tuning methods commonly used in computational neuroscience, which are based on time windows of neural and kinematic data. This paper develops a novel, online, encoding model that uses the instantaneous kinematic variables (position, velocity and acceleration in 2D or 3D space) to estimate the mean value of an inhomogeneous Poisson model. During BMI decoding the mapping from neural spikes to kinematics is one to one and easy to implement by simply reading the spike times directly. Due to the high temporal resolution of the encoding, the delay between motor cortex neurons and kinematics needs to be estimated in the encoding stage. Mutual information is employed to select the optimal time index defined as the lag for which the spike event is maximally informative with respect to the kinematics. We extensively compare the windowed tuning models with the proposed method. The big difference between them resides in the high firing rate portion of the tuning curve, which is rather important for BMI-decoding performance. This paper shows that implementing such an instantaneous tuning model in sequential Monte Carlo point process estimation based on spike timing provides statistically better kinematic reconstructions than the linear and exponential spike-tuning models.

  18. 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

  19. 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

  20. EEG Based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Syed M. Saddique

    2009-08-01

    Full Text Available Brain-Computer Interface (BCI has added a new value to efforts being made under human machine interfaces. It has not only introduced new dimensions in machine control but the researchers round the globe are still exploring the possible uses of such applications. BCIs have given a hope where alternative communication channels can be created for the persons having severe motor disabilities. This work is based upon utilizing the brain signals of a human being via scalp Electroencephalography (EEG to get the control of a robot’s navigation which can be visualized as controlling one’s surrounding environment without physical strain. In this work when a person thinks of a motor activity, it gets performed. The procedure includes acquisition and analysis of brain signals via EEG equipment, development of a classification system using AI techniques and propagating the subsequent control signals to Lego-robot via parallel port. This has been depicted in [1] as a generic block diagram.

  1. Organic electrode coatings for next-generation neural interfaces

    OpenAIRE

    Aregueta-Robles, Ulises A.; Woolley, Andrew J.; Poole-Warren, Laura A.; Lovell, Nigel H.; Rylie A Green

    2014-01-01

    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,...

  2. ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

    OpenAIRE

    Rylie A Green

    2014-01-01

    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,...

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

    International Nuclear Information System (INIS)

    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)

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

    Science.gov (United States)

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

    2012-06-01

    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. PMID:22617339

  5. Approaches for the efficient extraction and processing of biopotentials in implantable neural interfacing microsystems.

    Science.gov (United States)

    Gosselin, Benoit

    2011-01-01

    The accelerating pace of research in neurosciences and rehabilitation engineering has created a considerable demand for implantable microsystems capable of interfacing with large groups of neurons. Such microsystems must provide multiple recording channels incorporating low-noise amplifiers, filters, data converters, neural signal processing circuitry, power management units and low-power transmitters to extract and wirelessly transfer the relevant neural data outside the body for computing and storage. This paper is reviewing several electronic recording strategies to address the challenge of operating large numbers of channels to gather the neural information from several neurons within very low-power constraints. PMID:22255671

  6. Using brain-computer interfaces to induce neural plasticity and restore function

    Science.gov (United States)

    Grosse-Wentrup, Moritz; Mattia, Donatella; Oweiss, Karim

    2011-04-01

    Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.

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

    Science.gov (United States)

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

    2013-06-01

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

  8. Unfolding code for neutron spectrometry based on neural nets technology

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)

    2012-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  9. Unfolding code for neutron spectrometry based on neural nets technology

    International Nuclear Information System (INIS)

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the Robust Design of Artificial Neural Networks Methodology. The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  10. Nanoporous Gold as a Neural Interface Coating: Effects of Topography, Surface Chemistry, and Feature Size

    Science.gov (United States)

    Chapman, Christopher A. R.; Chen, Hao; Stamou, Marianna; Biener, Juergen; Biener, Monika M.; Lein, Pamela J.; Seker, Erkin

    2015-01-01

    Designing neural-electrode interfaces that maintain close physical coupling of neurons to the electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface (astrogliosis), which is an obstacle to reliable neuron-electrode coupling. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a promising candidate to reduce astrogliosis solely through topography by taking advantage of its tunable length scale. In the present in vitro study on np-Au’s interaction with cortical neuron-glia co-cultures, we demonstrate that the nanostructure of np-Au is achieving close physical coupling of neurons through maintaining a high neuron-to-astrocyte surface coverage ratio. Atomic layer deposition-based surface modification was employed to decouple the effect of morphology from surface chemistry. Additionally, length scale effects were systematically studied by controlling the characteristic feature size of np-Au through variations of the dealloying conditions. Our results show that np-Au nanotopography, not surface chemistry, reduces astrocyte surface coverage while maintaining high neuronal coverage, and may enhance the neuron-electrode coupling through nanostructure-mediated suppression of scar tissue formation. PMID:25706691

  11. Cryptography based on delayed chaotic neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Yu Wenwu [Department of Mathematics, Southeast University, Nanjing 210096 (China); Cao Jinde [Department of Mathematics, Southeast University, Nanjing 210096 (China)]. E-mail: jdcao@seu.edu.cn

    2006-08-14

    In this Letter, a novel approach of encryption based on chaotic Hopfield neural networks with time varying delay is proposed. We use the chaotic neural network to generate binary sequences which will be used for masking plaintext. The plaintext is masked by switching of chaotic neural network maps and permutation of generated binary sequences. Simulation results were given to show the feasibility and effectiveness in the proposed scheme of this Letter. As a result, chaotic cryptography becomes more practical in the secure transmission of large multi-media files over public data communication network.

  12. Cryptography based on delayed chaotic neural networks

    International Nuclear Information System (INIS)

    In this Letter, a novel approach of encryption based on chaotic Hopfield neural networks with time varying delay is proposed. We use the chaotic neural network to generate binary sequences which will be used for masking plaintext. The plaintext is masked by switching of chaotic neural network maps and permutation of generated binary sequences. Simulation results were given to show the feasibility and effectiveness in the proposed scheme of this Letter. As a result, chaotic cryptography becomes more practical in the secure transmission of large multi-media files over public data communication network

  13. User Interface Development Based on Ontologies

    Institute of Scientific and Technical Information of China (English)

    A; S; Kleshchev; M; Y; Chernyakhovskaya; V; V; Gribova

    2002-01-01

    The user interface is a central component of any mo de rn application program. It determines how well end users accept, learn, and effi ciently work with the application program. The user interface is very difficult to design, to implement, to modify. It takes approximately 70% of the time requ ired for designing an application program. All the existing tools for user interface design can be divided into two basic c ategories-Interface Builders and Model-based Interface development tools, whic h trace t...

  14. SAR ATR Based on Convolutional Neural Network

    OpenAIRE

    Tian Zhuangzhuang; Zhan Ronghui; Hu Jiemin; Zhang Jun

    2016-01-01

    This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recogni...

  15. Neural Operant Conditioning as a Core Mechanism of Brain-Machine Interface Control

    Directory of Open Access Journals (Sweden)

    Yoshio Sakurai

    2016-08-01

    Full Text Available The process of changing the neuronal activity of the brain to acquire rewards in a broad sense is essential for utilizing brain-machine interfaces (BMIs, which is essentially operant conditioning of neuronal activity. Currently, this is also known as neural biofeedback, and it is often referred to as neurofeedback when human brain activity is targeted. In this review, we first illustrate biofeedback and operant conditioning, which are methodological background elements in neural operant conditioning. Then, we introduce research models of neural operant conditioning in animal experiments and demonstrate that it is possible to change the firing frequency and synchronous firing of local neuronal populations in a short time period. We also debate the possibility of the application of neural operant conditioning and its contribution to BMIs.

  16. Brain Computer Interface. Comparison of Neural Networks Classifiers.

    OpenAIRE

    Martínez Pérez, Jose Luis; Barrientos Cruz, Antonio

    2008-01-01

    Brain Computer Interface is an emerging technology that allows new output paths to communicate the user’s intentions without use of normal output ways, such as muscles or nerves (Wolpaw, J. R.; et al., 2002).In order to obtain its objective BCI devices shall make use of classifier which translate the inputs provided by user’s brain signal to commands for external devices. The primary uses of this technology will benefit persons with some kind blocking disease as for example: ALS, brainstem st...

  17. Neural Network Based 3D Surface Reconstruction

    Directory of Open Access Journals (Sweden)

    Vincy Joseph

    2009-11-01

    Full Text Available This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D surface reconstruction model. The neural network combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors can be applied to integration method when reconstructing 3-D objects. Facial images were used for training in the proposed approach

  18. Optimizing growth and post treatment of diamond for high capacitance neural interfaces.

    Science.gov (United States)

    Tong, Wei; Fox, Kate; Zamani, Akram; Turnley, Ann M; Ganesan, Kumaravelu; Ahnood, Arman; Cicione, Rosemary; Meffin, Hamish; Prawer, Steven; Stacey, Alastair; Garrett, David J

    2016-10-01

    Electrochemical and biological properties are two crucial criteria in the selection of the materials to be used as electrodes for neural interfaces. For neural stimulation, materials are required to exhibit high capacitance and to form intimate contact with neurons for eliciting effective neural responses at acceptably low voltages. Here we report on a new high capacitance material fabricated using nitrogen included ultrananocrystalline diamond (N-UNCD). After exposure to oxygen plasma for 3 h, the activated N-UNCD exhibited extremely high electrochemical capacitance greater than 1 mF/cm(2), which originates from the special hybrid sp(2)/sp(3) structure of N-UNCD. The in vitro biocompatibility of the activated N-UNCD was then assessed using rat cortical neurons and surface roughness was found to be critical for healthy neuron growth, with best results observed on surfaces with a roughness of approximately 20 nm. Therefore, by using oxygen plasma activated N-UNCD with appropriate surface roughness, and considering the chemical and mechanical stability of diamond, the fabricated neural interfaces are expected to exhibit high efficacy, long-term stability and a healthy neuron/electrode interface. PMID:27424214

  19. Progress of Flexible Electronics in Neural Interfacing - A Self-Adaptive Non-Invasive Neural Ribbon Electrode for Small Nerves Recording.

    Science.gov (United States)

    Xiang, Zhuolin; Yen, Shih-Cheng; Sheshadri, Swathi; Wang, Jiahui; Lee, Sanghoon; Liu, Yu-Hang; Liao, Lun-De; Thakor, Nitish V; Lee, Chengkuo

    2016-06-01

    A novel flexible neural ribbon electrode with a self-adaptive feature is successfully implemented for various small nerves recording. As a neural interface, the selective recording capability is characterized by having reliable signal acquisitions from the sciatic nerve and its branches such as the peroneal nerve, the tibial nerve, and the sural nerve. PMID:26568483

  20. Layered carbon nanotube-polyelectrolyte electrodes outperform traditional neural interface materials.

    Science.gov (United States)

    Jan, Edward; Hendricks, Jeffrey L; Husaini, Vincent; Richardson-Burns, Sarah M; Sereno, Andrew; Martin, David C; Kotov, Nicholas A

    2009-12-01

    The safety, function, and longevity of implantable neuroprosthetic and cardiostimulating electrodes depend heavily on the electrical properties of the electrode-tissue interface, which in many cases requires substantial improvement. While different variations of carbon nanotube materials have been shown to be suitable for neural excitation, it is critical to evaluate them versus other materials used for bioelectrical interfacing, which have not been done in any study performed so far despite strong interest to this area. In this study, we carried out this evaluation and found that composite multiwalled carbon nanotube-polyelectrolyte (MWNT-PE) multilayer electrodes substantially outperform in one way or the other state-of-the-art neural interface materials available today, namely activated electrochemically deposited iridium oxide (IrOx) and poly(3,4-ethylenedioxythiophene) (PEDOT). Our findings provide the concrete experimental proof to the much discussed possibility that carbon nanotube composites can serve as excellent new material for neural interfacing with a strong possibility to lead to a new generation of implantable electrodes. PMID:19785391

  1. Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies.

    Science.gov (United States)

    Armenta Salas, Michelle; Helms Tillery, Stephen I

    2016-01-01

    The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions. PMID:27601981

  2. Rule extraction based on neural networks for satellite image interpretation

    Science.gov (United States)

    Mascarilla, Laurent

    1994-12-01

    In the frame of an image interpretation system for automatic cartography based on remote sensing image classification improved by a photo interpreter knowledge, we propose a system using neural networks to produce fuzzy production rules. These rules are intended to describe class vegetation context relatively to out image data (generally a G.I.S.) as a human expert could do. In the system, the expert only gives samples of concerned classes via a G.U.I. (Graphic User Interface) connected to a G.I.S. In a first stage, a Kohonen neural network is used to found clusters and membership functions, and then to compute a first set of fuzzy 'IF-THEN' rules with certainty factors. The human expert then updates these rules, and the given samples, according to his own experience. Once satisfying and discriminating classification rules are found, a second kind of neural network using back propagation is used to tune the final set of rules. At the same time, it produces neural nets able to give for each pixel and each class, the realisation degree of the favourable context relatively to the knowledge inferred by the samples.

  3. Navigation with a passive brain based interface

    NARCIS (Netherlands)

    Erp, J.B.F. van; Werkhoven, P.J.; Thurlings, M.E.; Brouwer, A.-M.

    2009-01-01

    In this paper, we describe a Brain Computer Interface (BCI) for navigation. The system is based on detecting brain signals that are elicited by tactile stimulation on the torso indicating the desired direction.

  4. SAR ATR Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tian Zhuangzhuang

    2016-06-01

    Full Text Available This study presents a new method of Synthetic Aperture Radar (SAR image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.

  5. Autonomous robot behavior based on neural networks

    Science.gov (United States)

    Grolinger, Katarina; Jerbic, Bojan; Vranjes, Bozo

    1997-04-01

    The purpose of autonomous robot is to solve various tasks while adapting its behavior to the variable environment, expecting it is able to navigate much like a human would, including handling uncertain and unexpected obstacles. To achieve this the robot has to be able to find solution to unknown situations, to learn experienced knowledge, that means action procedure together with corresponding knowledge on the work space structure, and to recognize working environment. The planning of the intelligent robot behavior presented in this paper implements the reinforcement learning based on strategic and random attempts for finding solution and neural network approach for memorizing and recognizing work space structure (structural assignment problem). Some of the well known neural networks based on unsupervised learning are considered with regard to the structural assignment problem. The adaptive fuzzy shadowed neural network is developed. It has the additional shadowed hidden layer, specific learning rule and initialization phase. The developed neural network combines advantages of networks based on the Adaptive Resonance Theory and using shadowed hidden layer provides ability to recognize lightly translated or rotated obstacles in any direction.

  6. 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

  7. Sketch-based Interfaces and Modeling

    CERN Document Server

    Jorge, Joaquim

    2011-01-01

    The field of sketch-based interfaces and modeling (SBIM) is concerned with developing methods and techniques to enable users to interact with a computer through sketching - a simple, yet highly expressive medium. SBIM blends concepts from computer graphics, human-computer interaction, artificial intelligence, and machine learning. Recent improvements in hardware, coupled with new machine learning techniques for more accurate recognition, and more robust depth inferencing techniques for sketch-based modeling, have resulted in an explosion of both sketch-based interfaces and pen-based computing

  8. Natural language interface for nuclear data bases

    International Nuclear Information System (INIS)

    A natural language interface has been developed for access to information from a data base, simulating a nuclear plant reliability data system (NPRDS), one of the several existing data bases serving the nuclear industry. In the last decade, the importance of information has been demonstrated by the impressive diffusion of data base management systems. The present methods that are employed to access data bases fall into two main categories of menu-driven systems and use of data base manipulation languages. Both of these methods are currently used by NPRDS. These methods have proven to be tedious, however, and require extensive training by the user for effective utilization of the data base. Artificial intelligence techniques have been used in the development of several intelligent front ends for data bases in nonnuclear domains. Lunar is a natural language program for interface to a data base describing moon rock samples brought back by Apollo. Intellect is one of the first data base question-answering systems that was commercially available in the financial area. Ladder is an intelligent data base interface that was developed as a management aid to Navy decision makers. A natural language interface for nuclear data bases that can be used by nonprogrammers with little or no training provides a means for achieving this goal for this industry

  9. NeuroArray: A Universal Interface for Patterning and Interrogating Neural Circuitry with Single Cell Resolution

    OpenAIRE

    Li, Wei; Xu, Zhen; Huang, Junzhe; Lin, Xudong; Luo, Rongcong; Chen, Chia-Hung; Shi, Peng

    2014-01-01

    Recreation of neural network in vitro with designed topology is a valuable tool to decipher how neurons behave when interacting in hierarchical networks. In this study, we developed a simple and effective platform to pattern primary neurons in array formats for interrogation of neural circuitry with single cell resolution. Unlike many surface-chemistry-based patterning methods, our NeuroArray technique is specially designed to accommodate neuron's polarized morphologies to make regular arrays...

  10. 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. PMID:23853339

  11. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Science.gov (United States)

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  12. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Directory of Open Access Journals (Sweden)

    Eric A Pohlmeyer

    Full Text Available Brain-machine interface (BMI systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings. These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  13. Transistor-based interface circuitry

    Science.gov (United States)

    Taubman, Matthew S.

    2007-02-13

    Among the embodiments of the present invention is an apparatus that includes a transistor, a servo device, and a current source. The servo device is operable to provide a common base mode of operation of the transistor by maintaining an approximately constant voltage level at the transistor base. The current source is operable to provide a bias current to the transistor. A first device provides an input signal to an electrical node positioned between the emitter of the transistor and the current source. A second device receives an output signal from the collector of the transistor.

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

    International Nuclear Information System (INIS)

    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.

  15. An integrated interface for peripheral neural system recording and stimulation: system design, electrical tests and in-vivo results.

    Science.gov (United States)

    Carboni, Caterina; Bisoni, Lorenzo; Carta, Nicola; Puddu, Roberto; Raspopovic, Stanisa; Navarro, Xavier; Raffo, Luigi; Barbaro, Massimo

    2016-04-01

    The prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of 2 integrated circuits: a standard CMOS device for neural recording and a HVCMOS device for neural stimulation. The integrated circuits have been realized in 2 different 0.35μ m CMOS processes available from ams. The complete system incorporates 8 channels each including the analog front-end, the A/D conversion, based on a sigma delta architecture and a programmable stimulation module implemented as a 5-bit current DAC; two voltage boosters supply the output stimulation stage with a programmable voltage scalable up to 17V. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μ V r m s , and to selectively elicit the tibial and plantar muscles using different active sites of the electrode. PMID:27007860

  16. Convolutional Neural Network Based dem Super Resolution

    Science.gov (United States)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

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

    Science.gov (United States)

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

    2015-10-01

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

  18. Web-based Interface in Public Cluster

    CERN Document Server

    Akbar, Z

    2007-01-01

    A web-based interface dedicated for cluster computer which is publicly accessible for free is introduced. The interface plays an important role to enable secure public access, while providing user-friendly computational environment for end-users and easy maintainance for administrators as well. The whole architecture which integrates both aspects of hardware and software is briefly explained. It is argued that the public cluster is globally a unique approach, and could be a new kind of e-learning system especially for parallel programming communities.

  19. A Direct Feedback Control Based on Fuzzy Recurrent Neural Network

    Institute of Scientific and Technical Information of China (English)

    李明; 马小平

    2002-01-01

    A direct feedback control system based on fuzzy-recurrent neural network is proposed, and a method of training weights of fuzzy-recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simul ation results indicate that fuzzy-recurrent neural network controller has perfect dynamic and static performances .

  20. SOLVING INVERSE KINEMATICS OF REDUNDANT MANIPULATOR BASED ON NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    For the redundant manipulators, neural network is used to tackle the velocity inverse kinematics of robot manipulators. The neural networks utilized are multi-layered perceptions with a back-propagation training algorithm. The weight table is used to save the weights solving the inverse kinematics based on the different optimization performance criteria. Simulations verify the effectiveness of using neural network.

  1. 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.

  2. NEVESIM: Event-Driven Neural Simulation Framework with a Python Interface

    Directory of Open Access Journals (Sweden)

    Dejan ePecevski

    2014-08-01

    Full Text Available 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.

  3. 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. PMID:25177291

  4. Microfabrication of 3D neural probes with combined electrical and chemical interfaces

    International Nuclear Information System (INIS)

    This paper reports a novel neural probe technology for the manufacture of 3D arrays of electrodes with integrated microchannels. This new technology is based on a silicon island structure and a simple folding procedure. This method simplifies the assembly or packaging process of 3D neural probes, leading to higher yield and lower cost. Prototypes with 3D arrays of electrodes have been successfully developed. Microchannels have been successfully integrated into the 3D neural probes via isotropic XeF2 gas phase etching and a parylene resealing process. The probes have been characterized by scanning electron microscopy imaging, optical imaging, impedance analysis, and atomic force microscopy characterization of the electrode surface. Preliminary animal tests have been carried out to demonstrate the recording functionality of the probes. Flow characteristics of the microchannels were also preliminarily measured.

  5. Microfabrication of 3D neural probes with combined electrical and chemical interfaces

    Science.gov (United States)

    John, Jessin; Li, Yuefa; Zhang, Jinsheng; Loeb, Jeffrey A.; Xu, Yong

    2011-10-01

    This paper reports a novel neural probe technology for the manufacture of 3D arrays of electrodes with integrated microchannels. This new technology is based on a silicon island structure and a simple folding procedure. This method simplifies the assembly or packaging process of 3D neural probes, leading to higher yield and lower cost. Prototypes with 3D arrays of electrodes have been successfully developed. Microchannels have been successfully integrated into the 3D neural probes via isotropic XeF2 gas phase etching and a parylene resealing process. The probes have been characterized by scanning electron microscopy imaging, optical imaging, impedance analysis, and atomic force microscopy characterization of the electrode surface. Preliminary animal tests have been carried out to demonstrate the recording functionality of the probes. Flow characteristics of the microchannels were also preliminarily measured.

  6. Neural Network Based Hausa Language Speech Recognition

    Directory of Open Access Journals (Sweden)

    Matthew K Luka

    2012-05-01

    Full Text Available Speech recognition is a key element of diverse applications in communication systems, medical transcription systems, security systems etc. However, there has been very little research in the domain of speech processing for African languages, thus, the need to extend the frontier of research in order to port in, the diverse applications based on speech recognition. Hausa language is an important indigenous lingua franca in west and central Africa, spoken as a first or second language by about fifty million people. Speech recognition of Hausa Language is presented in this paper. A pattern recognition neural network was used for developing the system.

  7. Adaptive movable neural interfaces for monitoring single neurons in the brain

    Directory of Open Access Journals (Sweden)

    Jit eMuthuswamy

    2011-09-01

    Full Text Available Implantable microelectrodes that are currently used to monitor neuronal activity in the brain in vivo have serious limitations both in acute and chronic experiments. Movable microelectrodes that adapt their position in the brain to maximize the quality of neuronal recording have been suggested and tried as a potential solution to overcome the challenges with the current fixed implantable microelectrodes. While the results so far suggest that movable microelectrodes improve the quality and stability of neuronal recordings from the brain in vivo, the bulky nature of the technologies involved in making these movable microelectrodes limits the throughput (number of neurons that can be recorded from at any given time of these implantable devices. Emerging technologies involving the use of microscale motors and electrodes promise to overcome this limitation. This review summarizes some of the most recent efforts in developing movable neural interfaces using microscale technologies that adapt their position in response to changes in the quality of the neuronal recordings. Key gaps in our understanding of the brain-electrode interface are highlighted. Emerging discoveries in these areas will lead to success in the development of a reliable and stable interface with single neurons that will impact basic neurophysiological studies and emerging cortical prosthetic technologies.

  8. A regenerative microchannel neural interface for recording from and stimulating peripheral axons in vivo.

    Science.gov (United States)

    FitzGerald, James J; Lago, Natalia; Benmerah, Samia; Serra, Jordi; Watling, Christopher P; Cameron, Ruth E; Tarte, Edward; Lacour, Stéphanie P; McMahon, Stephen B; Fawcett, James W

    2012-02-01

    Neural interfaces are implanted devices that couple the nervous system to electronic circuitry. They are intended for long term use to control assistive technologies such as muscle stimulators or prosthetics that compensate for loss of function due to injury. Here we present a novel design of interface for peripheral nerves. Recording from axons is complicated by the small size of extracellular potentials and the concentration of current flow at nodes of Ranvier. Confining axons to microchannels of ~100 µm diameter produces amplified potentials that are independent of node position. After implantation of microchannel arrays into rat sciatic nerve, axons regenerated through the channels forming 'mini-fascicles', each typically containing ~100 myelinated fibres and one or more blood vessels. Regenerated motor axons reconnected to distal muscles, as demonstrated by the recovery of an electromyogram and partial prevention of muscle atrophy. Efferent motor potentials and afferent signals evoked by muscle stretch or cutaneous stimulation were easily recorded from the mini-fascicles and were in the range of 35-170 µV. Individual motor units in distal musculature were activated from channels using stimulus currents in the microampere range. Microchannel interfaces are a potential solution for applications such as prosthetic limb control or enhancing recovery after nerve injury. PMID:22258138

  9. Internet-based interface for STRMDEPL08

    Science.gov (United States)

    Reeves, Howard W.; Asher, A. Jeremiah

    2010-01-01

    The core of the computer program STRMDEPL08 that estimates streamflow depletion by a pumping well with one of four analytical solutions was re-written in the Javascript software language and made available through an internet-based interface (web page). In the internet-based interface, the user enters data for one of the four analytical solutions, Glover and Balmer (1954), Hantush (1965), Hunt (1999), and Hunt (2003), and the solution is run for constant pumping for a desired number of simulation days. Results are returned in tabular form to the user. For intermittent pumping, the interface allows the user to request that the header information for an input file for the stand-alone executable STRMDEPL08 be created. The user would add the pumping information to this header information and run the STRMDEPL08 executable that is available for download through the U.S. Geological Survey. Results for the internet-based and stand-alone versions of STRMDEPL08 are shown to match.

  10. The web based user interface of RODOS

    International Nuclear Information System (INIS)

    Full text: The interaction between the RODOS system and its users has three main objectives: (1) operation of the system in its automatic and interactive modes including the processing of meteorological and radiological on-line data, and the choice of module chains for performing the necessary calculations; (2) input of data defining the accident situation, such as source term information, intervention criteria and timing of emergency actions; (3) selection and presentation of results in the form of spatial and temporal distributions of activity concentrations, areas affected by emergency actions and countermeasures, and their radiological and economic consequences. Users of category A have direct access to the RODOS system via local or wide area networks through the client/server protocol Internet/X. Any internet connected X desktop machine, such as Unix workstations from different vendors, X- terminals, Linux PCs, and PCs with X-emulation can be used. A number of X-Windows based graphical user interfaces (GUIs) provide direct access to all functionalities of the RODOS system and allow for handling the various user interactions with the RODOS system described above. Among others, the user can trigger or interrupt the automatic processing mode, execute application programs simultaneously, modify and delete data, import data sets from databases, and change configuration files. As the user interacts directly with in-memory active processes, the system responses immediately after having performed the necessary calculations. For obtaining the requested results, the users must know, which chain of application software has to be selected, how to interact with their interfaces, which sort of initialization data have to be assigned, etc. This flexible interaction with RODOS implies that only experienced and well-trained users are able to operate the system and to obtain correct and sensible information. A new interface has been developed which is based an the commonly used

  11. Neural-net based real-time economic dispatch for thermal power plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  12. Mesh deformation based on artificial neural networks

    Science.gov (United States)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  13. Mesh-based parallel code coupling interface

    Energy Technology Data Exchange (ETDEWEB)

    Wolf, K.; Steckel, B. (eds.) [GMD - Forschungszentrum Informationstechnik GmbH, St. Augustin (DE). Inst. fuer Algorithmen und Wissenschaftliches Rechnen (SCAI)

    2001-04-01

    MpCCI (mesh-based parallel code coupling interface) is an interface for multidisciplinary simulations. It provides industrial end-users as well as commercial code-owners with the facility to combine different simulation tools in one environment. Thereby new solutions for multidisciplinary problems will be created. This opens new application dimensions for existent simulation tools. This Book of Abstracts gives a short overview about ongoing activities in industry and research - all presented at the 2{sup nd} MpCCI User Forum in February 2001 at GMD Sankt Augustin. (orig.) [German] MpCCI (mesh-based parallel code coupling interface) definiert eine Schnittstelle fuer multidisziplinaere Simulationsanwendungen. Sowohl industriellen Anwender als auch kommerziellen Softwarehersteller wird mit MpCCI die Moeglichkeit gegeben, Simulationswerkzeuge unterschiedlicher Disziplinen miteinander zu koppeln. Dadurch entstehen neue Loesungen fuer multidisziplinaere Problemstellungen und fuer etablierte Simulationswerkzeuge ergeben sich neue Anwendungsfelder. Dieses Book of Abstracts bietet einen Ueberblick ueber zur Zeit laufende Arbeiten in der Industrie und in der Forschung, praesentiert auf dem 2{sup nd} MpCCI User Forum im Februar 2001 an der GMD Sankt Augustin. (orig.)

  14. Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

    CERN Document Server

    Sher, Gene I

    2011-01-01

    Though machine learning has been applied to the foreign exchange market for quiet some time now, and neural networks have been shown to yield good results, in modern approaches neural network systems are optimized through the traditional methods, and their input signals are vectors containing prices and other indicator elements. The aim of this paper is twofold, the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and the use of chart images as input to a geometrical regularity aware indirectly encoded neural network systems. This paper presents the benchmark results of neural network based automated currency trading systems evolved using TWEANNs, and compares the generalization capabilities of these direct encoded neural networks which use the standard price vector inputs, and the indirect (substrate) encoded neural networks which use chart images as input. The TWEANN algorithm used to evolve these currency t...

  15. 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.

  16. 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.

  17. Multispectral thermometry based on neural network

    Institute of Scientific and Technical Information of China (English)

    孙晓刚; 戴景民

    2003-01-01

    In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is proposed for data processing while a blackbody furnace and three optical filters with known spectral transmittance curves were used to make up a true target. The experimental results show that the calculated temperatures are in good agreement with the temperature of the blackbody furnace, and the calculated spectral emissivity curves are in good agreement with the spectral transmittance curves of the filters. The method proposed has been proved to be an effective method for solving the problem of true temperature and emissivity measurement, and it can overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials.

  18. Clustering-based selective neural network ensemble

    Institute of Scientific and Technical Information of China (English)

    FU Qiang; HU Shang-xu; ZHAO Sheng-ying

    2005-01-01

    An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller en semble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-offto reduce the prediction error (the sum of bias2 and variance).

  19. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole;

    1998-01-01

    -linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi...

  20. Arabic Interface Analysis Based on Cultural Markers

    Directory of Open Access Journals (Sweden)

    Mohammadi Akheela Khanum

    2012-01-01

    Full Text Available This study examines the Arabic interface design elements that are largely influenced by the cultural values. Cultural markers are examined in websites from educational, business, and media. Cultural values analysis is based on Geert Hofstedes cultural dimensions. The findings show that there are cultural markers which are largely influenced by the culture and that the Hofstedes score for Arab countries is partially supported by the website design components examined in this study. Moderate support was also found for the long term orientation, for which Hoftsede has no score.

  1. Arabic Interface Analysis Based on Cultural Markers

    CERN Document Server

    Khanum, Mohammadi Akheela; Chaurasia, Mousmi A

    2012-01-01

    This study examines the Arabic interface design elements that are largely influenced by the cultural values. Cultural markers are examined in websites from educational, business, and media. Cultural values analysis is based on Geert Hofstede's cultural dimensions. The findings show that there are cultural markers which are largely influenced by the culture and that the Hofstede's score for Arab countries is partially supported by the website design components examined in this study. Moderate support was also found for the long term orientation, for which Hoftsede has no score.

  2. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  3. DESIGN OF A VISUAL INTERFACE FOR ANN BASED SYSTEMS

    OpenAIRE

    Ramazan BAYINDIR; SESVEREN, Ömer

    2008-01-01

    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 networ...

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

    International Nuclear Information System (INIS)

    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)

  5. Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain-Machine Interfaces.

    Science.gov (United States)

    Wang, Yiwen; She, Xiwei; Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang; Principe, Jose

    2016-08-01

    Classic brain-machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes. PMID:26584486

  6. Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network

    Institute of Scientific and Technical Information of China (English)

    WANG Zhenfei; ZHAI Guangqun; WANG Nengchao

    2006-01-01

    An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.

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

    Science.gov (United States)

    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.

  8. 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.

  9. Effect of bias voltage and temperature on lifetime of wireless neural interfaces with Al ₂O₃ and parylene bilayer encapsulation.

    Science.gov (United States)

    Xie, Xianzong; Rieth, Loren; Caldwell, Ryan; Negi, Sandeep; Bhandari, Rajmohan; Sharma, Rohit; Tathireddy, Prashant; Solzbacher, Florian

    2015-02-01

    The lifetime of neural interfaces is a critical challenge for chronic implantations, as therapeutic devices (e.g., neural prosthetics) will require decades of lifetime. We evaluated the lifetime of wireless Utah electrode array (UEA) based neural interfaces with a bilayer encapsulation scheme utilizing a combination of alumina deposited by Atomic Layer Deposition (ALD) and parylene C. Wireless integrated neural interfaces (INIs), equipped with recording version 9 (INI-R9) ASIC chips, were used to monitor the encapsulation performance through radio-frequency (RF) power and telemetry. The wireless devices were encapsulated with 52 nm of ALD Al2O3 and 6 μm of parylene C, and tested by soaking in phosphate buffered solution (PBS) at 57 °C for 4× accelerated lifetime testing. The INIs were also powered continuously through 2.765 MHz inductive power and forward telemetry link at unregulated 5 V. The bilayer encapsulated INIs were fully functional for ∼35 days (140 days at 37 °C equivalent) with consistent power-up frequencies (∼910 MHz), stable RF signal (∼-75 dBm), and 100 % command reception rate. This is ∼10 times of equivalent lifetime of INIs with parylene-only encapsulation (13 days) under same power condition at 37 °C. The bilayer coated INIs without continuous powering lasted over 1860 equivalent days (still working) at 37 °C. Those results suggest that bias stress is a significant factor to accelerate the failure of the encapsulated devices. The INIs failed completely within 5 days of the initial frequency shift of RF signal at 57 °C, which implied that the RF frequency shift is an early indicator of encapsulation/device failure. PMID:25653054

  10. Neural bases of accented speech perception

    OpenAIRE

    Adank, Patti; Nuttall, Helen E.; Banks, Briony; Kennedy-Higgins, Daniel

    2015-01-01

    The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Floccia et al., 2006; Adank et al., 2009). Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented speech, are beginning to be identified. This re...

  11. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428. ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant ostatní: MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  12. DEM interpolation based on artificial neural networks

    Science.gov (United States)

    Jiao, Limin; Liu, Yaolin

    2005-10-01

    This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.

  13. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  14. Web based educational tool for neural network robot control

    Directory of Open Access Journals (Sweden)

    Jure Čas

    2007-05-01

    Full Text Available Abstract— This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC and the graphical user interface (GUI. Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI, running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote’s user. Index Terms—LabVIEW; Matlab/Simulink; Neural network control; remote educational tool; robotics

  15. 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.

  16. A recurrent neural network for closed-loop intracortical brain-machine interface decoders

    Science.gov (United States)

    Sussillo, David; Nuyujukian, Paul; Fan, Joline M.; Kao, Jonathan C.; Stavisky, Sergey D.; Ryu, Stephen; Shenoy, Krishna

    2012-04-01

    Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.

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

    Science.gov (United States)

    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.

  18. Architecture Analysis of an FPGA-Based Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

    Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.

  19. Modeling the Electrode-Neuron Interface of Cochlear Implants: Effects of Neural Survival, Electrode Placement, and the Partial Tripolar Configuration

    OpenAIRE

    Goldwyn, Joshua H.; Bierer, Steven M.; Bierer, Julie A.

    2010-01-01

    The partial tripolar electrode configuration is a relatively novel stimulation strategies 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 activatio...

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

    Science.gov (United States)

    Ereifej, Evon S.

    grown on PMMA resembled closely to that of cells grown on the control surface, thus confirming the biocompatibility of PMMA. Additionally, the astrocyte GFAP gene expressions of cells grown on PMMA were lower than the control, signifying a lack of astrocyte reactivity. Based on the findings from the biomaterials study, it was decided to optimize PMMA by changing the surface characteristic of the material. Through the process of hot embossing, nanopatterns were placed on the surface in order to test the hypothesis that nanopatterning can improve the cellular response to the material. Results of this study agreed with current literature showing that topography effects protein and cell behavior. It was concluded that for the use in neural electrode fabrication and design, the 3600mm/gratings pattern feature sizes were optimal. The 3600 mm/gratings pattern depicted cell alignment along the nanopattern, less protein adsorption, less cell adhesion, proliferation and viability, inhibition of GFAP and MAP2k1 compared to all other substrates tested. Results from the initial biomaterials study also indicated platinum was negatively affected the cells and may not be a suitable material for neural electrodes. This lead to pursuing studies with iridium oxide and platinum alloy wires for the glial scar assay. Iridium oxide advantages of lower impedance and higher charge injection capacity would appear to make iridium oxide more favorable for neural electrode fabrication. However, results of this study demonstrate iridium oxide wires exhibited a more significant reactive response as compared to platinum alloy wires. Astrocytes cultured with platinum alloy wires had less GFAP gene expression, lower average GFAP intensity, and smaller glial scar thickness. Results from the nanopatterning PMMA study prompted a more thorough investigation of the nanopatterning effects using an organotypic brain slice model. PDMS was utilized as the substrate due to its optimal physical properties

  1. Polymer based interfaces as bioinspired 'smart skins'.

    Science.gov (United States)

    De Rossi, Danilo; Carpi, Federico; Scilingo, Enzo Pasquale

    2005-11-30

    This work reports on already achieved results and ongoing research on the development of complex interfaces between humans and external environment, based on organic synthetic materials and used as smart 'artificial skins'. They are conceived as wearable and flexible systems with multifunctional characteristics. Their features are designed to mimic or augment a broad-spectrum of properties shown by biological skins of humans and/or animals. The discussion is here limited to those properties whose mimicry/augmentation is achievable with currently available technologies based on polymers and oligomers. Such properties include tactile sensing, thermal sensing/regulation, environmental energy harvesting, chromatic mimetism, ultra-violet protection, adhesion and surface mediation of mobility. Accordingly, bioinspired devices and structures, proposed as suitable functional analogous of natural architectures, are analysed. They consist of organic piezoelectric sensors, thermoelectric and pyroelectric sensors and generators, photoelectric generators, thermal and ultra-violet protection systems, electro-, photo- and thermo-chromic devices, as well as structures for improved adhesion and reduced fluid-dynamic friction. PMID:16111642

  2. Network Traffic Prediction based on Particle Swarm BP Neural Network

    OpenAIRE

    Yan Zhu; Guanghua Zhang; Jing Qiu

    2013-01-01

    The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and part...

  3. Image watermarking capacity analysis based on Hopfield neural network

    Institute of Scientific and Technical Information of China (English)

    Fan Zhang(张帆); Hongbin Zhang(张鸿宾)

    2004-01-01

    In watermarking schemes, watermarking can be viewed as a form of communication problems. Almost all of previous works on image watermarking capacity are based on information theory, using Shannon formula to calculate the capacity of watermarking. In this paper, we present a blind watermarking algorithm using Hopfield neural network, and analyze watermarking capacity based on neural network. In our watermarking algorithm, watermarking capacity is decided by attraction basin of associative memory.

  4. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    -linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  5. An adaptive interface (KNOWBOT) for nuclear power industry data bases

    International Nuclear Information System (INIS)

    An adaptive interface, KNOWBOT, has been designed to solve some of the problems that face the users of large centralized databases. The interface applies the neural network approach to information retrieval from a database. The database is a subset of the Nuclear Plant Reliability Data System (NPRDS). KNOWBOT preempts an existing database interface and works in conjunction with it. By design, KNOWBOT starts as a tabula rasa but acquires knowledge through its interactions with the user and the database. The interface uses its gained knowledge to personalize the database retrieval process and to induce new queries. In addition, the interface forgets the information that is no longer needed by the user. These self-organizing features of the interface reduce the scope of the database to the subsets that are highly relevant to the user needs. A proof-of-principle version of this interface has been implemented in Common LISP on a Texas Instruments Explorer I workstation. Experiments with KNOWBOT have successfully demonstrated the robustness of the model especially with induction and self-organization

  6. Contractor Prequalification Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jin-long; YANG Lan-rong

    2002-01-01

    Contractor Prequalification involves the screening of contractors by a project owner, according to a given set of criteria, in order to determine their competence to perform the work if awarded the construction contract. This paper introduces the capabilities of neural networks in solving problems related to contractor prequalification. The neural network systems for contractor prequalification has an input vector of 8 components and an output vector of 1 component. The output vector represents whether a contractor is qualified or not qualified to submit a bid on a project.

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

    International Nuclear Information System (INIS)

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway. 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. A second objective of the program was to develop an interactive graphical user interface which presented the system's information in an easily accessible and straightforward manner to the user. This paper describes the technical and regulatory foundation upon which the expert system is based and provides a background on the development of a new severe accident management tool. This tool provides data to assist in; (1) planning and developing priorities for recovery actions, (2) evaluating recovery action feasibility, (3) identifying recovery action options, and (4) assessing the timing and possible effects of potential recovery strategies. These performance characteristics represent the goals identified for the Severe Accident Management Strategies Online Network (SAMSON) which is currently under development. 4 refs, 1 fig., 1 tab

  8. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  9. Dynamically Generated Interfaces in XML Based Architecture

    CERN Document Server

    Gupta, Minit

    2009-01-01

    Providing on-line services on the Internet will require the definition of flexible interfaces that are capable of adapting to the user's characteristics. This is all the more important in the context of medical applications like home monitoring, where no two patients have the same medical profile. Still, the problem is not limited to the capacity of defining generic interfaces, as has been made possible by UIML, but also to define the underlying information structures from which these may be generated. The DIATELIC project deals with the tele-monitoring of patients under peritoneal dialysis. By means of XML abstractions, termed as "medical components", to represent the patient's profile, the application configures the customizable properties of the patient's interface and generates a UIML document dynamically. The interface allows the patient to feed the data manually or use a device which allows "automatic data acquisition". The acquired medical data is transferred to an expert system, which analyses the dat...

  10. Web Database Query Interface Annotation Based on User Collaboration

    Institute of Scientific and Technical Information of China (English)

    LIU Wei; LIN Can; MENG Xiaofeng

    2006-01-01

    A vision based query interface annotation method is used to relate attributes and form elements in form-based web query interfaces, this method can reach accuracy of 82%.And a user participation method is used to tune the result; user can answer "yes" or "no" for existing annotations, or manually annotate form elements.Mass feedback is added to the annotation algorithm to produce more accurate result.By this approach, query interface annotation can reach a perfect accuracy.

  11. Applications of Neural-Based Agents in Computer Game Design

    OpenAIRE

    Qualls, Joseph; Russomanno, David J.

    2009-01-01

    It is clear from the implementation and analysis of the performance of the game Defend and Gather and the many other examples discussed in this chapter that neural-based agents have the ability to overcome some of the shortcomings associated with implementing classical AI techniques in computer game design. Neural networks can be used in many diverse ways in computer games ranging from agent control, environmental evolution, to content generation. As outlined in Section 3 of this chapter, by ...

  12. Neural-Based Models of Semiconductor Devices for SPICE Simulator

    OpenAIRE

    Hanene B. Hammouda; Mongia Mhiri; Zièd Gafsi; Kamel Besbes

    2008-01-01

    The paper addresses a simple and fast new approach to implement Artificial Neural Networks (ANN) models for the MOS transistor into SPICE. The proposed approach involves two steps, the modeling phase of the device by NN providing its input/output patterns, and the SPICE implementation process of the resulting model. Using the Taylor series expansion, a neural based small-signal model is derived. The reliability of our approach is validated through simulations of some circuits in DC and small-...

  13. MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Ge Guangying; Chen Lili; Xu Jianjian

    2005-01-01

    Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.

  14. Dependency-based Convolutional Neural Networks for Sentence Embedding

    OpenAIRE

    Ma, Mingbo; Huang, Liang; Xiang, Bing; Zhou, Bowen

    2015-01-01

    In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships between words. Our model improves the sequential baselines on all three sentiment and question classificat...

  15. Neural Network Predictive Control Based Power System Stabilizer

    OpenAIRE

    Ali Mohamed Yousef

    2012-01-01

    The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC) on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of ...

  16. Artificial neural network based modelling of internal combustion engine performance

    OpenAIRE

    Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal

    2016-01-01

    The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...

  17. Decoupling Control Method Based on Neural Network for Missiles

    Institute of Scientific and Technical Information of China (English)

    ZHAN Li; LUO Xi-shuang; ZHANG Tian-qiao

    2005-01-01

    In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.

  18. Hopfield neural network based on ant system

    Institute of Scientific and Technical Information of China (English)

    洪炳镕; 金飞虎; 郭琦

    2004-01-01

    Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

  19. A Design of Neural-Net Based Decouplers

    Science.gov (United States)

    Tokuda, Makoto; Yamamoto, Toru; Monden, Yoshimi

    In process industries such as the chemical plants, a good control performance cannot be obtained by simply using the linear controllers, since most processes are nonlinear multivariable systems with mutual interactions. And now, in various fields, the neural networks are well known as the representative schemes to describe the nonlinear elements included in the systems. Also, many types of neural-net based control systems have been proposed, since they have the ability of function approximation, the training ability and versatility. However, the neural networks tend to require great deal of training iteration or careful adjustments of user-specified parameters. In this paper, a design method of neural-net based decouplers is proposed for nonlinear multivariable systems. Here, the decoupler is generated by the sum of a static decoupler and a neural-net based decoupler. The former is used so that the influence of mutual interactions is roughly removed, and the latter plays a role of compensating the nonlinearities and decoupling the remaining mutual interactions. Thus, by designing the control system as the hybrid system, the burden in training the neural networks can be considerably reduced. Finally, the effectiveness of the proposed control scheme is evaluated on a simulation example.

  20. Nonlinear control structures based on embedded neural system models.

    Science.gov (United States)

    Lightbody, G; Irwin, G W

    1997-01-01

    This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper. PMID:18255659

  1. Numeral eddy current sensor modelling based on genetic neural network

    International Nuclear Information System (INIS)

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

  2. Learning in neural networks based on a generalized fluctuation theorem

    Science.gov (United States)

    Hayakawa, Takashi; Aoyagi, Toshio

    2015-11-01

    Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

  3. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  4. Numeral eddy current sensor modelling based on genetic neural network

    Institute of Scientific and Technical Information of China (English)

    Yu A-Long

    2008-01-01

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However, the maximum nonlinearity error is 0.075% using the least square method.

  5. A continuously growing web-based interface structure databank

    International Nuclear Information System (INIS)

    The macroscopic properties of materials can be significantly influenced by the presence of microscopic interfaces. The complexity of these interfaces coupled with the vast configurational space in which they reside has been a long-standing obstacle to the advancement of true bottom-up material behavior predictions. In this vein, atomistic simulations have proven to be a valuable tool for investigating interface behavior. However, before atomistic simulations can be utilized to model interface behavior, meaningful interface atomic structures must be generated. The generation of structures has historically been carried out disjointly by individual research groups, and thus, has constituted an overlap in effort across the broad research community. To address this overlap and to lower the barrier for new researchers to explore interface modeling, we introduce a web-based interface structure databank (www.isdb.cee.cornell.edu) where users can search, download and share interface structures. The databank is intended to grow via two mechanisms: (1) interface structure donations from individual research groups and (2) an automated structure generation algorithm which continuously creates equilibrium interface structures. In this paper, we describe the databank, the automated interface generation algorithm, and compare a subset of the autonomously generated structures to structures currently available in the literature. To date, the automated generation algorithm has been directed toward aluminum grain boundary structures, which can be compared with experimentally measured population densities of aluminum polycrystals. (paper)

  6. A symbiotic brain-machine interface through value-based decision making.

    Directory of Open Access Journals (Sweden)

    Babak Mahmoudi

    Full Text Available BACKGROUND: In the development of Brain Machine Interfaces (BMIs, there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC. METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1 and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and

  7. Novel 3D plasmonic nano-electrodes for cellular investigations and neural interfaces

    Science.gov (United States)

    Malerba, Mario; Dipalo, Michele; Messina, Gabriele C.; Amin, Hayder; La Rocca, Rosanna; Shalabaeva, Victoria; Simi, Alessandro; Maccione, Alessandro; Berdondini, Luca; De Angelis, Francesco

    2014-08-01

    We propose the development of an innovative plasmonic-electronic multifunctional platform, capable at the same time of performing chemical analysis and electronic recordings from a cellular interface. The system, based on 3D hollow metallic nanotubes, integrated on customized multi-electrode-arrays, allows the study of neuronal signaling over different lengths, spanning from the molecular, to the cellular, to the network scale. Here we show that the same structures are efficient electric field enhancers, despite the continuous metal layer at the base, which connects them to the electric components of the integrated circuits. The methodology we propose, due to its simplicity and high throughput, has the potential for further improvements both in the field of plasmonics, and in the integration on large areas of commercial active electronic devices.

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

    International Nuclear Information System (INIS)

    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

  9. Colored Noise Prediction Based on Neural Network

    Institute of Scientific and Technical Information of China (English)

    Gao Fei; Zhang Xiaohui

    2003-01-01

    A method for predicting colored noise by introducing prediction of nonhnear time series is presented By adopting three kinds of neural networks prediction models, the colored noise prediction is studied through changing the filter bandwidth for stochastic noise and the sampling rate for colored noise The results show that colored noise can be predicted The prediction error decreases with the increasing of the sampling rate or the narrowing of the filter bandwidth. If the parameters are selected properly, the prediction precision can meet the requirement of engineering implementation. The results offer a new reference way for increasing the ability for detecting weak signal in signal processing system

  10. Image Restoration Technology Based on Discrete Neural network

    Directory of Open Access Journals (Sweden)

    Zhou Duoying

    2015-01-01

    Full Text Available With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, this paper verifies that the discrete neural network has a good convergence and identification capability in the image restoration technology with a better effect than that of the feedforward network. The restoration technology based on the discrete neural network can provide a reliable mathematical model for this field.

  11. Network Traffic Prediction based on Particle Swarm BP Neural Network

    Directory of Open Access Journals (Sweden)

    Yan Zhu

    2013-11-01

    Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.

  12. Novel conjugates of peptides and conjugated polymers for optoelectronics and neural interfaces

    Science.gov (United States)

    Bhagwat, Nandita

    Peptide-polymer conjugates are a novel class of hybrid materials that take advantage of each individual component giving the opportunity to generate materials with unique physical, chemical, mechanical, optical, and electronic properties. In this dissertation peptide-polymer conjugates for two different applications are discussed. The first set of peptide-polymer conjugates were developed as templates to study the intermolecular interactions between electroactive molecules by manipulating the intermolecular distances at nano-scale level. A PEGylated, alpha-helical peptide template was employed to effectively display an array of organic chromophores (oxadiazole containing phenylenevinylene oligomers, Oxa-PPV). Three Oxa-PPV chromophores were strategically positioned on each template, at distances ranging from 6 to 17 A from each other, as dictated by the chemical and structural properties of the peptide. The Oxa-PPV modified PEGylated helical peptides (produced via Heck coupling strategies) were characterized by a variety of spectroscopic methods. Electronic contributions from multiple pairs of chromophores on a scaffold were detectable; the number and relative positioning of the chromophores dictated the absorbance and emission maxima, thus confirming the utility of these polymer--peptide templates for complex presentation of organic chromophores. The rest of the thesis is focused on using poly(3,4-alkylenedioxythiophene) based conjugated polymers as coatings for neural electrodes. This thiophene derivative is of considerable current interest for functionalizing the surfaces of a wide variety of devices including implantable biomedical electronics, specifically neural bio-electrodes. Toward these ends, copolymer films of 3,4-ethylenedioxythiophene (EDOT) with a carboxylic acid functional EDOT (EDOTacid) were electrochemically deposited and characterized as a systematic function of the EDOTacid content (0, 25, 50, 75, and 100%). The chemical surface characterization

  13. Data Mining and Neural Network Techniques in Case Based System

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper first puts forward a case-based system framework basedon data mining techniques. Then the paper examines the possibility of using neural n etworks as a method of retrieval in such a case-based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.

  14. fNIRS-based brain-computer interfaces: a review.

    Science.gov (United States)

    Naseer, Noman; Hong, Keum-Shik

    2015-01-01

    A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips. PMID:25674060

  15. fNIRS-based brain-computer interfaces: a review

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2015-01-01

    Full Text Available A brain-computer interface (BCI is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

  16. Forest Fire Image Intelligent Recognition based on the Neural Network

    OpenAIRE

    Yan Qiang; Bo Pei; Juanjuan Zhao

    2014-01-01

    To avoid the drawbacks caused by the long-distance and large-area features of the outdoor forest fires in the traditional fire detection methods. A new forest fire recognition method based on the neural network is proposed, which recognizes the fire based on the static and dynamic features of the fire. The method combines the multiple parameters of the flames and the shapes of the fire to distinguish fire image. Then the extracted features were tested by the Back Propagation Neural Network. T...

  17. Neural network based electron identification in the ZEUS calorimeter

    International Nuclear Information System (INIS)

    We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

  18. The neural bases of orthographic working memory

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2014-04-01

    First, these results reveal a neurotopography of OWM lesion sites that is well-aligned with results from neuroimaging of orthographic working memory in neurally intact participants (Rapp & Dufor, 2011. Second, the dorsal neurotopography of the OWM lesion overlap is clearly distinct from what has been reported for lesions associated with either lexical or sublexical deficits (e.g., Henry, Beeson, Stark, & Rapcsak, 2007; Rapcsak & Beeson, 2004; these have, respectively, been identified with the inferior occipital/temporal and superior temporal/inferior parietal regions. These neurotopographic distinctions support the claims of the computational distinctiveness of long-term vs. working memory operations. The specific lesion loci raise a number of questions to be discussed regarding: (a the selectivity of these regions and associated deficits to orthographic working memory vs. working memory more generally (b the possibility that different lesion sub-regions may correspond to different components of the OWM system.

  19. Neural second-level trigger system based on calorimetry

    Science.gov (United States)

    Seixas, J. M.; Caloba, L. P.; Souza, M. N.; Braga, A. L.; Rodrigues, A. P.

    1996-06-01

    A second-level triggering system based on calorimetry is analyzed using neural networks. Calorimeter data in a LHC environment is obtained with Monte Carlo simulations and an algorithm for the first-level trigger operation is applied. The surviving events are then available as a 20×20 matrix information corresponding to the calorimeter towers in the region of interest. The dominant background for triggering on electrons is assumed to consist of QCD jets which passed the first-level trigger condition. The main features of the calorimeter are extracted. Matrix information, shower deposition in concentric rings and tail weighting procedures are studied. The processed information is sent to a fully connected backpropagation neural network. In this analysis we also consider pileup effects of an average of 20 minimum bias events. The neural network based system achieved up to 99% electron efficiency with less than 9% of jets being misclassified as electrons. Implementation on digital signal processors is suggested.

  20. Blur identification by multilayer neural network based on multivalued neurons.

    Science.gov (United States)

    Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

    2008-05-01

    A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones. PMID:18467216

  1. 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.

  2. From neural-based object recognition toward microelectronic eyes

    Science.gov (United States)

    Sheu, Bing J.; Bang, Sa Hyun

    1994-01-01

    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  3. Data Process of Diagnose Expert System based on Neural Network

    Directory of Open Access Journals (Sweden)

    Shupeng Zhao

    2013-12-01

    Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network

  4. Addition of visual noise boosts evoked potential-based brain-computer interface.

    Science.gov (United States)

    Xie, Jun; Xu, Guanghua; Wang, Jing; Zhang, Sicong; Zhang, Feng; Li, Yeping; Han, Chengcheng; Li, Lili

    2014-01-01

    Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7-36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs. PMID:24828128

  5. Neural Cell Chip Based Electrochemical Detection of Nanotoxicity

    Directory of Open Access Journals (Sweden)

    Md. Abdul Kafi

    2015-07-01

    Full Text Available Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C, C(RGD4 ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot or three dimensional (rod or pillar like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD, graphene oxide (GO and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

  6. SSVEP and ANN based optimal speller design for Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Irshad Ahmad Ansari

    2015-07-01

    Full Text Available This work put forwards an optimal BCI (Brain Computer Interface speller design based on Steady State Visual Evoked Potentials (SSVEP and Artificial Neural Network (ANN in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of  BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.DOI: 10.15181/csat.v2i2.1059

  7. High-Conductance Thermal Interfaces Based on Carbon Nanotubes Project

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop a novel thermal interface material (TIM) that is based on an array of vertical carbon nanotubes (CNTs) for high heat flux applications. For...

  8. Neural Network-Based Active Control for Offshore Platforms

    Institute of Scientific and Technical Information of China (English)

    周亚军; 赵德有

    2003-01-01

    A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.

  9. Neural-Based Models of Semiconductor Devices for SPICE Simulator

    Directory of Open Access Journals (Sweden)

    Hanene B. Hammouda

    2008-01-01

    Full Text Available The paper addresses a simple and fast new approach to implement Artificial Neural Networks (ANN models for the MOS transistor into SPICE. The proposed approach involves two steps, the modeling phase of the device by NN providing its input/output patterns, and the SPICE implementation process of the resulting model. Using the Taylor series expansion, a neural based small-signal model is derived. The reliability of our approach is validated through simulations of some circuits in DC and small-signal analyses.

  10. 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.

  11. Neural Network Model Based Cluster Head Selection for Power Control

    Directory of Open Access Journals (Sweden)

    Krishan Kumar

    2011-01-01

    Full Text Available Mobile ad-hoc network has challenge of the limited power to prolong the lifetime of the network, because power is a valuable resource in mobile ad-hoc network. The status of power consumption should be continuously monitored after network deployment. In this paper, we propose coverage aware neural network based power control routing with the objective of maximizing the network lifetime. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage. The simulation results show that the proposed scheme can be used in wide area of applications in mobile ad-hoc network.

  12. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LIDong-Mei; WANGZheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.

  13. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LI Dong-Mei; WANG Zheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.

  14. Clustering in mobile ad hoc network based on neural network

    Institute of Scientific and Technical Information of China (English)

    CHEN Ai-bin; CAI Zi-xing; HU De-wen

    2006-01-01

    An on-demand distributed clustering algorithm based on neural network was proposed. The system parameters and the combined weight for each node were computed, and cluster-heads were chosen using the weighted clustering algorithm, then a training set was created and a neural network was trained. In this algorithm, several system parameters were taken into account, such as the ideal node-degree, the transmission power, the mobility and the battery power of the nodes. The algorithm can be used directly to test whether a node is a cluster-head or not. Moreover, the clusters recreation can be speeded up.

  15. Electronic implementation of associative memory based on neural network models

    Science.gov (United States)

    Moopenn, A.; Lambe, John; Thakoor, A. P.

    1987-01-01

    An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.

  16. A Neural Network-based ARX Model of Virgo Noise

    OpenAIRE

    Barone, F.; Rosa, R; Eleuteri, A.; Garufi, F.; Milano, L; Tagliaferri, R.

    1999-01-01

    In this paper a Neural Network based approach is presented to identify the noise in the VIRGO context. VIRGO is an experiment to detect Gravitational Waves by means of a Laser Interferometer. Preliminary results appear to be very promising for data analysis of realistic Interferometer outputs.

  17. On the Nature, Modeling, and Neural Bases of Social Ties

    NARCIS (Netherlands)

    F.A.A.M. Winden, van (Frans); M. Stallen (Mirre); K.R. Ridderinkhof (Richard)

    2008-01-01

    textabstractThis paper addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility interdependence

  18. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  19. Dissipative-based adaptive neural control for nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Yugang NIU; Xingyu WANG; Junwei LU

    2004-01-01

    A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, I.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to nake the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.

  20. Numerical Analysis of Modeling Based on Improved Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Shao Jie

    2014-01-01

    Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

  1. Numerical analysis of modeling based on improved Elman neural network.

    Science.gov (United States)

    Jie, Shao; Li, Wang; WeiSong, Zhao; YaQin, Zhong; Malekian, Reza

    2014-01-01

    A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172

  2. Microchannel-based regenerative scaffold for chronic peripheral nerve interfacing in amputees.

    Science.gov (United States)

    Srinivasan, Akhil; Tahilramani, Mayank; Bentley, John T; Gore, Russell K; Millard, Daniel C; Mukhatyar, Vivek J; Joseph, Anish; Haque, Adel S; Stanley, Garrett B; English, Arthur W; Bellamkonda, Ravi V

    2015-02-01

    Neurally controlled prosthetics that cosmetically and functionally mimic amputated limbs remain a clinical need because state of the art neural prosthetics only provide a fraction of a natural limb's functionality. Here, we report on the fabrication and capability of polydimethylsiloxane (PDMS) and epoxy-based SU-8 photoresist microchannel scaffolds to serve as viable constructs for peripheral nerve interfacing through in vitro and in vivo studies in a sciatic nerve amputee model where the nerve lacks distal reinnervation targets. These studies showed microchannels with 100 μm × 100 μm cross-sectional areas support and direct the regeneration/migration of axons, Schwann cells, and fibroblasts through the microchannels with space available for future maturation of the axons. Investigation of the nerve in the distal segment, past the scaffold, showed a high degree of organization, adoption of the microchannel architecture forming 'microchannel fascicles', reformation of endoneurial tubes and axon myelination, and a lack of aberrant and unorganized growth that might be characteristic of neuroma formation. Separate chronic terminal in vivo electrophysiology studies utilizing the microchannel scaffolds with permanently integrated microwire electrodes were conducted to evaluate interfacing capabilities. In all devices a variety of spontaneous, sensory evoked and electrically evoked single and multi-unit action potentials were recorded after five months of implantation. Together, these findings suggest that microchannel scaffolds are well suited for chronic implantation and peripheral nerve interfacing to promote organized nerve regeneration that lends itself well to stable interfaces. Thus this study establishes the basis for the advanced fabrication of large-electrode count, wireless microchannel devices that are an important step towards highly functional, bi-directional peripheral nerve interfaces. PMID:25522974

  3. A Robust Camera-Based Interface for Mobile Entertainment.

    Science.gov (United States)

    Roig-Maimó, Maria Francesca; Manresa-Yee, Cristina; Varona, Javier

    2016-01-01

    Camera-based interfaces in mobile devices are starting to be used in games and apps, but few works have evaluated them in terms of usability or user perception. Due to the changing nature of mobile contexts, this evaluation requires extensive studies to consider the full spectrum of potential users and contexts. However, previous works usually evaluate these interfaces in controlled environments such as laboratory conditions, therefore, the findings cannot be generalized to real users and real contexts. In this work, we present a robust camera-based interface for mobile entertainment. The interface detects and tracks the user's head by processing the frames provided by the mobile device's front camera, and its position is then used to interact with the mobile apps. First, we evaluate the interface as a pointing device to study its accuracy, and different factors to configure such as the gain or the device's orientation, as well as the optimal target size for the interface. Second, we present an in the wild study to evaluate the usage and the user's perception when playing a game controlled by head motion. Finally, the game is published in an application store to make it available to a large number of potential users and contexts and we register usage data. Results show the feasibility of using this robust camera-based interface for mobile entertainment in different contexts and by different people. PMID:26907288

  4. Robust face recognition using posterior union model based neural networks

    OpenAIRE

    Lin, J.; J., Ming; Crookes, D.

    2009-01-01

    Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched loca...

  5. Quantum Neural Network Based Machine Translator for Hindi to English

    OpenAIRE

    Ravi Narayan; Singh, V.P.; Chakraverty, S.

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze t...

  6. Image Restoration Technology Based on Discrete Neural network

    OpenAIRE

    Zhou Duoying

    2015-01-01

    With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, ...

  7. Data Process of Diagnose Expert System based on Neural Network

    OpenAIRE

    Shupeng Zhao; Miao Tian; Shifang Zhang; Jiuxi Li; Lijuan Du; Ye Wang

    2013-01-01

    Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge...

  8. 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

  9. 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. PMID:25206907

  10. Realizing a family of transition-metal-oxide memristors based on volatile resistive switching at a rectifying metal/oxide interface

    International Nuclear Information System (INIS)

    There is strong interest in creating new memristors due to their significant impact in many fields including digital information systems, analogue circuits and artificial neural networks as a new class of fundamental electronic elements. Here we report a volatile resistive switching effect at a prototypical Schottky metal/oxide interface and realize a family of transition-metal-oxide memristors showing distinct hysteresis characteristics based on the interface. The results not only provide further understanding on the electrical behaviour of metal/oxide interfaces but also indicate the key role of metal/oxide interfaces as basic building blocks in transition-metal–oxide memristors. (paper)

  11. A conductivity-based interface tracking method for microfluidic application

    Science.gov (United States)

    Salgado, Juan David; Horiuchi, Keisuke; Dutta, Prashanta

    2006-05-01

    A novel conductivity-based interface tracking method is developed for 'lab-on-a-chip' applications to measure the velocity of the liquid-gas boundary during the filling process. This interface tracking system consists of two basic components: a fluidic circuit and an electronic circuit. The fluidic circuit is composed of a microchannel network where a number of very thin electrodes are placed in the flow path to detect the location of the liquid-gas interface in order to quantify the speed of a traveling liquid front. The electronic circuit is placed on a microelectronic chip that works as a logical switch. This interface tracking method is used to evaluate the performance of planar electrokinetic micropumps formed on a hybrid poly-di-methyl-siloxane (PDMS)-glass platform. In this study, the thickness of the planar micropump is set to be 10 µm, while the externally applied electric field is ranged from 100 V mm-1 to 200 V mm-1. For a particular geometric and electrokinetic condition, repeatable flow results are obtained from the speed of the liquid-gas interface. Flow results obtained from this interface tracking method are compared to those of other existing flow measuring techniques. The maximum error of this interface tracking sensor is less than 5%, even in an ultra low flow velocity.

  12. Boron-Doped Nanocrystalline Diamond Electrodes for Neural Interfaces: In vivo Biocompatibility Evaluation.

    Science.gov (United States)

    Alcaide, María; Taylor, Andrew; Fjorback, Morten; Zachar, Vladimir; Pennisi, Cristian P

    2016-01-01

    Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended for use as an implantable neurostimulation device. After implantation for 2 and 4 weeks, tissue sections adjacent to the electrodes were obtained for histological analysis. Both types of implants were contained in a thin fibrous encapsulation layer, the thickness of which decreased with time. Although the level of neovascularization around the implants was similar, BDD electrodes elicited significantly thinner fibrous capsules and a milder inflammatory reaction at both time points. These results suggest that BDD films may constitute an appropriate material to support stable performance of implantable neural electrodes over time. PMID:27013949

  13. Boron-doped nanocrystalline diamond electrodes for neural interfaces: In vivo biocompatibility evaluation

    Directory of Open Access Journals (Sweden)

    María eAlcaide

    2016-03-01

    Full Text Available Boron-doped nanocrystalline diamond (BDD electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended for use as an implantable neurostimulation device. After implantation for 2 and 4 weeks, tissue sections adjacent to the electrodes were obtained for histological analysis. Both types of implants were contained in a thin fibrous encapsulation layer, the thickness of which decreased with time. Although the level of neovascularization around the implants was similar, BDD electrodes elicited significantly thinner fibrous capsules and a milder inflammatory reaction at both time points. These results suggest that BDD films may constitute an appropriate material to support stable performance of implantable neural electrodes over time.

  14. Boron-Doped Nanocrystalline Diamond Electrodes for Neural Interfaces: In vivo Biocompatibility Evaluation

    Science.gov (United States)

    Alcaide, María; Taylor, Andrew; Fjorback, Morten; Zachar, Vladimir; Pennisi, Cristian P.

    2016-01-01

    Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended for use as an implantable neurostimulation device. After implantation for 2 and 4 weeks, tissue sections adjacent to the electrodes were obtained for histological analysis. Both types of implants were contained in a thin fibrous encapsulation layer, the thickness of which decreased with time. Although the level of neovascularization around the implants was similar, BDD electrodes elicited significantly thinner fibrous capsules and a milder inflammatory reaction at both time points. These results suggest that BDD films may constitute an appropriate material to support stable performance of implantable neural electrodes over time. PMID:27013949

  15. Neural interface of mirror therapy in chronic stroke patients: A functional magnetic resonance imaging study

    OpenAIRE

    Ashu Bhasin; M V Padma Srivastava; Kumaran, Senthil S; Rohit Bhatia; Sujata Mohanty

    2012-01-01

    Background: Recovery in stroke is mediated by neural plasticity. Neuro-restorative therapies improve recovery after stroke by promoting repair and function. Mirror neuron system (MNS) has been studied widely in humans in stroke and phantom sensations. Materials and Methods: Study subjects included 20 patients with chronic stroke and 10 healthy controls. Patients had clinical disease-severity scores, functional magnetic resonance imaging (fMRI) and diffuse tensor imaging (DTI) at baseline, 8 a...

  16. Boron-doped nanocrystalline diamond electrodes for neural interfaces: In vivo biocompatibility evaluation

    OpenAIRE

    María eAlcaide; Andrew eTaylor; Morten eFjorback; Vladimir eZachar; Cristian Pablo Pennisi

    2016-01-01

    Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended ...

  17. Boron-Doped Nanocrystalline Diamond Electrodes for Neural Interfaces: In vivo Biocompatibility Evaluation

    OpenAIRE

    Alcaide, María; Taylor, Andrew; Fjorback, Morten; Zachar, Vladimir; Pennisi, Cristian P.

    2016-01-01

    Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended ...

  18. Neural Network Predictive Control Based Power System Stabilizer

    Directory of Open Access Journals (Sweden)

    Ali Mohamed Yousef

    2012-04-01

    Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.

  19. A Universal Intelligent System-on-Chip Based Sensor Interface

    Directory of Open Access Journals (Sweden)

    Gabriele Ferri

    2010-08-01

    Full Text Available The need for real-time/reliable/low-maintenance distributed monitoring systems, e.g., wireless sensor networks, has been becoming more and more evident in many applications in the environmental, agro-alimentary, medical, and industrial fields. The growing interest in technologies related to sensors is an important indicator of these new needs. The design and the realization of complex and/or distributed monitoring systems is often difficult due to the multitude of different electronic interfaces presented by the sensors available on the market. To address these issues the authors propose the concept of a Universal Intelligent Sensor Interface (UISI, a new low-cost system based on a single commercial chip able to convert a generic transducer into an intelligent sensor with multiple standardized interfaces. The device presented offers a flexible analog and/or digital front-end, able to interface different transducer typologies (such as conditioned, unconditioned, resistive, current output, capacitive and digital transducers. The device also provides enhanced processing and storage capabilities, as well as a configurable multi-standard output interface (including plug-and-play interface based on IEEE 1451.3. In this work the general concept of UISI and the design of reconfigurable hardware are presented, together with experimental test results validating the proposed device.

  20. Neural Bases of Recovery after Brain Injury

    Science.gov (United States)

    Nudo, Randolph J.

    2011-01-01

    Substantial data have accumulated over the past decade indicating that the adult brain is capable of substantial structural and functional reorganization after stroke. While some limited recovery is known to occur spontaneously, especially within the first month post-stroke, there is currently significant optimism that new interventions based on…

  1. Review of Brain-Machine Interfaces Used in Neural Prosthetics with New Perspective on Somatosensory Feedback through Method of Signal Breakdown.

    Science.gov (United States)

    Vidal, Gabriel W Vattendahl; Rynes, Mathew L; Kelliher, Zachary; Goodwin, Shikha Jain

    2016-01-01

    The brain-machine interface (BMI) used in neural prosthetics involves recording signals from neuron populations, decoding those signals using mathematical modeling algorithms, and translating the intended action into physical limb movement. Recently, somatosensory feedback has become the focus of many research groups given its ability in increased neural control by the patient and to provide a more natural sensation for the prosthetics. This process involves recording data from force sensitive locations on the prosthetics and encoding these signals to be sent to the brain in the form of electrical stimulation. Tactile sensation has been achieved through peripheral nerve stimulation and direct stimulation of the somatosensory cortex using intracortical microstimulation (ICMS). The initial focus of this paper is to review these principles and link them to modern day applications such as restoring limb use to those who lack such control. With regard to how far the research has come, a new perspective for the signal breakdown concludes the paper, offering ideas for more real somatosensory feedback using ICMS to stimulate particular sensations by differentiating touch sensors and filtering data based on unique frequencies. PMID:27313959

  2. Invariant-Based Automatic Testing of AJAX User Interfaces

    NARCIS (Netherlands)

    Mesbah, A.; Van Deursen, A.

    2009-01-01

    This paper is a pre-print of: Ali Mesbah and Arie van Deursen. Invariant-Based Automatic Testing of AJAX User Interfaces. In Proceedings of the 31st International Conference on Software Engineering (ICSE’09), Research Papers, Vancouver, Canada, IEEE Computer Society, 2009. AJAX-based Web 2.0 applic

  3. Neural bases for anticipation skill in soccer: an FMRI study.

    Science.gov (United States)

    Bishop, Daniel T; Wright, Michael J; Jackson, Robin C; Abernethy, Bruce

    2013-02-01

    The aim of this study was to examine the neural bases for perceptual-cognitive superiority in a soccer anticipation task using functional magnetic resonance imaging (fMRI). Thirty-nine participants lay in an MRI scanner while performing a video-based task in which they predicted an oncoming opponent's movements. Video clips were occluded at four time points, and participants were grouped according to in-task performance. Early occlusion reduced prediction accuracy significantly for all participants, as did the opponent's execution of a deceptive maneuver; however, high-skill participants were significantly more accurate than their low-skill counterparts under deceptive conditions. This perceptual-cognitive superiority was associated with greater activation of cortical and subcortical structures involved in executive function and oculomotor control. The contributions of the present findings to an existing neural model of anticipation in sport are highlighted. PMID:23404883

  4. Artificial Neural Network Based Approach for short load forecasting

    Directory of Open Access Journals (Sweden)

    Mr. Rajesh Deshmukh

    2011-12-01

    Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.

  5. Composite Taste Recognition Method Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Zhang

    2013-09-01

    Full Text Available In order to make recognition for the composite taste, the paper puts forward the research on the composite taste recognition method based on fuzzy neural network based on the wavelet transform. According to the wavelet transformation and the compression and extraction of the data of the taste signals that are collected by the sensor, we use the fuzzy neutral network as the recognition tool of the taste signal. Besides, we add genetic algorithm to make the function optimization for the network weights and the data processing and fuzzy recognition of the composite taste signal are presented. Finally, we make the test for the network performance. The results show that it has feasibility and effectiveness that the fuzzy neural network is introduced into the fuzzy identification of the taste signals.

  6. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...... well as for DD1 lter and the DD2 lter, as well as functions for Unscented Kalman lters and several versions of particle lters. The toolbox requires MATLAB version 7, but no additional toolboxes are required.......In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  7. ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    BASSEL SOLAIMANE

    2011-01-01

    Full Text Available Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’s blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.

  8. Nanoscale properties of graphene-based interfaces

    OpenAIRE

    Miniussi, Elisa

    2014-01-01

    Il tema fondamentale della mia attività di ricerca di dottorato è stato la produzione e caratterizzazione di interfacce a base di grafene. Negli ultimi dieci anni, il grafene, il singolo strato perfettamente bidimensionale di atomi di carbonio, si è imposto all'attenzione della comunità scientifica come un materiale rivoluzionario con eccezionali proprietà meccaniche, elettroniche e termiche, potenzialmente in grado di superare il silicio nella prossima generazione di dispositivi elettronici...

  9. Image Compression of Neural Network Based on Corner Block

    Directory of Open Access Journals (Sweden)

    Wenjing Zhang

    2014-01-01

    Full Text Available Most information received by the human is acquired through vision. However, image has the largest data amount in three information forms. If the image is not compressed, high transmission rate for digital image transmission and tremendous capacity for digital image storage can hinder the development of digital image. For example, for a color image whose resolution rate is 1280×1024, each pixel needs 24B for storage, and the total data amount is about 3.75MB. If the earth satellite transmits the acquired image to the earth at 30 frames per second, the transmitting data size in 1 second is about 112.5MB. Under the condition of the existing communication capacity, if the image is not compressed, the real-time transmission of most multimedia information can’t be completed. High-speed transmission and storage of digital image has become the biggest obstacle of promoting digital image communication. So it is necessary to compress image. Data compression not only can rapidly transmit various information sources, improve the utilization rage of information channel and reduce transmitted power, but also can save energy and reduce storage capacity. More and more attentions of people have been paid to the application of artificial neural network to image compression, the reason for which is that artificial neural network has good fault tolerance, self-organization and adaptivity compared with traditional compression methods. So the predetermined data coding algorithm is not needed in the process of image compression. Neural network can independently complete the image coding and compression according to the characteristics of image. The paper combines corner detection technology with artificial neural network image compression, and designs a new neural network image compression encoding based on corner block with reasonable structure, high compression rate and rapid convergence rate

  10. Research on Transformer Fault Based on Probabilistic Neural Network

    OpenAIRE

    Li Yingshun; Li Jingjing; Han Junfeng

    2015-01-01

    With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production pr...

  11. ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK

    OpenAIRE

    BASSEL SOLAIMANE; ADNENE CHERIF; SAMEH OUESLATI,

    2011-01-01

    Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS) model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’...

  12. Consensus Attention-based Neural Networks for Chinese Reading Comprehension

    OpenAIRE

    Cui, Yiming; Liu, Ting; Chen, Zhipeng; Wang, Shijin; Hu, Guoping

    2016-01-01

    Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children's Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension...

  13. Fuzzy neural network image filter based on GA

    Institute of Scientific and Technical Information of China (English)

    刘涵; 刘丁; 李琦

    2004-01-01

    A new nonlinear image filter using fuzzy neural network based on genetic algorithm is proposed. The learning of network parameters is performed by genetic algorithm with the efficient binary encoding scheme. In the following,fuzzy reasoning embedded in the network aims at restoring noisy pixels without degrading the quality of fine details. It is shown by experiments that the filter is very effective in removing impulse noise and significantly outperforms conventional filters.

  14. Cryptography based on neural networks - analytical results

    International Nuclear Information System (INIS)

    The mutual learning process between two parity feed-forward networks with discrete and continuous weights is studied analytically, and we find that the number of steps required to achieve full synchronization between the two networks in the case of discrete weights is finite. The synchronization process is shown to be non-self-averaging and the analytical solution is based on random auxiliary variables. The learning time of an attacker that is trying to imitate one of the networks is examined analytically and is found to be much longer than the synchronization time. Analytical results are found to be in agreement with simulations. (letter to the editor)

  15. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  16. Monitoring and control interface based on virtual sensors.

    Science.gov (United States)

    Escobar, Ricardo F; Adam-Medina, Manuel; García-Beltrán, Carlos D; Olivares-Peregrino, Víctor H; Juárez-Romero, David; Guerrero-Ramírez, Gerardo V

    2014-01-01

    In this article, a toolbox based on a monitoring and control interface (MCI) is presented and applied in a heat exchanger. The MCI was programed in order to realize sensor fault detection and isolation and fault tolerance using virtual sensors. The virtual sensors were designed from model-based high-gain observers. To develop the control task, different kinds of control laws were included in the monitoring and control interface. These control laws are PID, MPC and a non-linear model-based control law. The MCI helps to maintain the heat exchanger under operation, even if a temperature outlet sensor fault occurs; in the case of outlet temperature sensor failure, the MCI will display an alarm. The monitoring and control interface is used as a practical tool to support electronic engineering students with heat transfer and control concepts to be applied in a double-pipe heat exchanger pilot plant. The method aims to teach the students through the observation and manipulation of the main variables of the process and by the interaction with the monitoring and control interface (MCI) developed in LabVIEW©. The MCI provides the electronic engineering students with the knowledge of heat exchanger behavior, since the interface is provided with a thermodynamic model that approximates the temperatures and the physical properties of the fluid (density and heat capacity). An advantage of the interface is the easy manipulation of the actuator for an automatic or manual operation. Another advantage of the monitoring and control interface is that all algorithms can be manipulated and modified by the users. PMID:25365462

  17. Monitoring and Control Interface Based on Virtual Sensors

    Directory of Open Access Journals (Sweden)

    Ricardo F. Escobar

    2014-10-01

    Full Text Available In this article, a toolbox based on a monitoring and control interface (MCI is presented and applied in a heat exchanger. The MCI was programed in order to realize sensor fault detection and isolation and fault tolerance using virtual sensors. The virtual sensors were designed from model-based high-gain observers. To develop the control task, different kinds of control laws were included in the monitoring and control interface. These control laws are PID, MPC and a non-linear model-based control law. The MCI helps to maintain the heat exchanger under operation, even if a temperature outlet sensor fault occurs; in the case of outlet temperature sensor failure, the MCI will display an alarm. The monitoring and control interface is used as a practical tool to support electronic engineering students with heat transfer and control concepts to be applied in a double-pipe heat exchanger pilot plant. The method aims to teach the students through the observation and manipulation of the main variables of the process and by the interaction with the monitoring and control interface (MCI developed in LabVIEW©. The MCI provides the electronic engineering students with the knowledge of heat exchanger behavior, since the interface is provided with a thermodynamic model that approximates the temperatures and the physical properties of the fluid (density and heat capacity. An advantage of the interface is the easy manipulation of the actuator for an automatic or manual operation. Another advantage of the monitoring and control interface is that all algorithms can be manipulated and modified by the users.

  18. A study of task-based strategies for adaptively constructive neural networks

    International Nuclear Information System (INIS)

    The authors investigated the strategies for optimizing neural networks under the unified frame based on task, focused for constructive neural networks on two typical and practical schemes, which are adaptively constructive neural networks by growing hidden or layers of hidden nodes and by growing sub net. With the Layer Multinet Model proposed by the research group, the authors investigated task-based algorithms for constructive neural networks, their perspective, strength and weakness

  19. Feature Selection for Neural Network Based Stock Prediction

    Science.gov (United States)

    Sugunnasil, Prompong; Somhom, Samerkae

    We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.

  20. A damage mechanics based general purpose interface/contact element

    Science.gov (United States)

    Yan, Chengyong

    Most of the microelectronics packaging structures consist of layered substrates connected with bonding materials, such as solder or epoxy. Predicting the thermomechanical behavior of these multilayered structures is a challenging task in electronic packaging engineering. In a layered structure the most complex part is always the interfaces between the strates. Simulating the thermo-mechanical behavior of such interfaces, is the main theme of this dissertation. The most commonly used solder material, Pb-Sn alloy, has a very low melting temperature 180sp°C, so that the material demonstrates a highly viscous behavior. And, creep usually dominates the failure mechanism. Hence, the theory of viscoplasticity is adapted to describe the constitutive behavior. In a multilayered assembly each layer has a different coefficient of thermal expansion. Under thermal cycling, due to heat dissipated from circuits, interfaces and interconnects experience low cycle fatigue. Presently, the state-of-the art damage mechanics model used for fatigue life predictions is based on Kachanov (1986) continuum damage model. This model uses plastic strain as a damage criterion. Since plastic strain is a stress path dependent value, the criterion does not yield unique damage values for the same state of stress. In this dissertation a new damage evolution equation based on the second law of thermodynamic is proposed. The new criterion is based on the entropy of the system and it yields unique damage values for all stress paths to the final state of stress. In the electronics industry, there is a strong desire to develop fatigue free interconnections. The proposed interface/contact element can also simulate the behavior of the fatigue free Z-direction thin film interconnections as well as traditional layered interconnects. The proposed interface element can simulate behavior of a bonded interface or unbonded sliding interface, also called contact element. The proposed element was verified against

  1. A design of FPGA based intelligent data handling interfacing card.

    Directory of Open Access Journals (Sweden)

    Anandaraj D

    2015-05-01

    Full Text Available With the increasing demand in the custom built logic for avionics systems, FPGA is used in this proposed interfacing card design. This FPGA based intelligent data handling card (IDHC for the IVHM system, will interface the data from aircraft subsystems to the aircraft digital data bus. This IDHC interfacing card is based on the Virtex-5 FPGA (Field Programmable Gate Array, which provides flexibility by re-programming, so that it can be configured to the required functionality. Fault detection can be done within the FPGA and only the anomalies passed to the computer, so that the bus bandwidth can be utilized effectively and also excessive wiring can be eliminated, that would have been required for multiple individual systems. The work concentrates on designing the schematic using OrCAD.

  2. Neural Net Gains Estimation Based on an Equivalent Model

    Science.gov (United States)

    Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

    2016-01-01

    A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system. PMID:27366146

  3. Spacecraft power system controller based on neural network

    Science.gov (United States)

    El-madany, Hanaa T.; Fahmy, Faten H.; El-Rahman, Ninet M. A.; Dorrah, Hassen T.

    2011-09-01

    Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This work presents the spacecraft orbit determination, dimensioning of the renewable power system, and mathematical modeling of spacecraft power system which are required for simulating the system. The complete system is simulated using MATLAB-SIMULINK. The NN controller outperform PID in the extreme range of non-linearity. Well trained neural controller can operate at different conditions of load current at different orbital periods without any tuning such in case of PID controller. So an artificial neural network (ANN) based model has been developed for the optimum operation of spacecraft power system. An ANN is trained using a back propagation with Levenberg-Marquardt algorithm. The best validation performance is obtained for mean square error is equal to 9.9962×10 -11 at epoch 637. The regression between the network output and the corresponding target is equal to 100% which means a high accuracy. NNC architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the spacecraft power system in low earth orbit (LEO). Therefore, this technique is going to be a very useful tool for the interested designers in space field.

  4. Research on Transformer Fault Based on Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Li Yingshun

    2015-01-01

    Full Text Available With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production process by the use of bionic algorithm. This paper makes a diagnosis of transformer fault by the use of an expert system developed by this paper, and verifies that the probabilistic neural network has a good convergence, fault-tolerant ability and big data handling capability in the fault diagnosis. It is suitable for industrial production, which can provide a reliable mathematical model for the construction of fault diagnosis expert system in the industrial production.

  5. Neural cell image segmentation method based on support vector machine

    Science.gov (United States)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  6. Artificial Neural Network Based Control Strategies for Paddy Drying Process

    Directory of Open Access Journals (Sweden)

    Shekhar F. Lilhare

    2014-10-01

    Full Text Available Paddy drying process depends upon ambient conditions, paddy quality, temperature and mass of hot drying air. Existing techniques of paddy drying process are highly nonlinear. In this paper, a neural network based automated controller for paddy drying is designed. The designed controller manages the steam temperature and blower motor speed to achieve constant paddy drying time. A Layer recurrent neural network is adopted for the controller. Atmospheric conditions such as temperature and humidity along with the size of the paddy are used as input to the network. Experimental results show that the developed controller can be used to control the paddy drying process. Implementation of developed controller will help in controlling the drying time at almost constant value which will definitely improve the quality of rice.

  7. Distribution network planning algorithm based on Hopfield neural network

    Institute of Scientific and Technical Information of China (English)

    GAO Wei-xin; LUO Xian-jue

    2005-01-01

    This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.

  8. Automated neural network-based instrument validation system

    Science.gov (United States)

    Xu, Xiao

    2000-10-01

    In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex non-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant

  9. Natural movement with concurrent brain-computer interface control induces persistent dissociation of neural activity

    OpenAIRE

    Bashford, Luke; Wu, Jing; Sarma, Devapratim; Collins, Kelly; Ojemann, Jeff; Mehring, Carsten

    2016-01-01

    As Brain-computer interface (BCI) technology develops it is likely it may be incorporated into protocols that complement and supplement existing movements of the user. Two possible scenarios for such a control could be: the increasing interest to control artificial supernumerary prosthetics, or in cases following brain injury where BCI can be incorporated alongside residual movements to recover ability. In this study we explore the extent to which the human motor cortex is able to concurrentl...

  10. NEVESIM: Event-Driven Neural Simulation Framework with a Python Interface

    OpenAIRE

    Dejan ePecevski; David eKappel; Zeno eJonke

    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...

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

    OpenAIRE

    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...

  12. Mining learners’ behavior in accessing web-based interface

    OpenAIRE

    Lee, MW; Chen, SY; Liu, X.

    2007-01-01

    Web-based technology has already been adopted as a tool to support teaching and learning in higher education. One criterion affecting the usability of such a technology is the design of web-based interface (WBI) within web-based learning programs. How different users access the WBIs has been investigated by several studies, which mainly analyze the collected data using statistical methods. In this paper, we propose to analyze users’ learning behavior using Data Mining (DM) techniques. Finding...

  13. Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex

    Science.gov (United States)

    Ulloa, Antonio; Horwitz, Barry

    2016-01-01

    A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional

  14. Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex.

    Science.gov (United States)

    Ulloa, Antonio; Horwitz, Barry

    2016-01-01

    A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were "non-task-specific" (NS) neurons that served as noise generators to "task-specific" neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional

  15. Nanotechnology-Based Approaches for Guiding Neural Regeneration.

    Science.gov (United States)

    Shah, Shreyas; Solanki, Aniruddh; Lee, Ki-Bum

    2016-01-19

    The mammalian brain is a phenomenal piece of "organic machinery" that has fascinated scientists and clinicians for centuries. The intricate network of tens of billions of neurons dispersed in a mixture of chemical and biochemical constituents gives rise to thoughts, feelings, memories, and life as we know it. In turn, subtle imbalances or damage to this system can cause severe complications in physical, motor, psychological, and cognitive function. Moreover, the inevitable loss of nerve tissue caused by degenerative diseases and traumatic injuries is particularly devastating because of the limited regenerative capabilities of the central nervous system (i.e., the brain and spinal cord). Among current approaches, stem-cell-based regenerative medicine has shown the greatest promise toward repairing and regenerating destroyed neural tissue. However, establishing controlled and reliable methodologies to guide stem cell differentiation into specialized neural cells of interest (e.g., neurons and oligodendrocytes) has been a prevailing challenge in the field. In this Account, we summarize the nanotechnology-based approaches our group has recently developed to guide stem-cell-based neural regeneration. We focus on three overarching strategies that were adopted to selectively control this process. First, soluble microenvironmental factors play a critical role in directing the fate of stem cells. Multiple factors have been developed in the form of small-molecule drugs, biochemical analogues, and DNA/RNA-based vectors to direct neural differentiation. However, the delivery of these factors with high transfection efficiency and minimal cytotoxicity has been challenging, especially to sensitive cell lines such as stem cells. In our first approach, we designed nanoparticle-based systems for the efficient delivery of such soluble factors to control neural differentiation. Our nanoparticles, comprising either organic or inorganic elements, were biocompatible and offered

  16. Neural interface of mirror therapy in chronic stroke patients: A functional magnetic resonance imaging study

    Directory of Open Access Journals (Sweden)

    Ashu Bhasin

    2012-01-01

    Full Text Available Background: Recovery in stroke is mediated by neural plasticity. Neuro-restorative therapies improve recovery after stroke by promoting repair and function. Mirror neuron system (MNS has been studied widely in humans in stroke and phantom sensations. Materials and Methods: Study subjects included 20 patients with chronic stroke and 10 healthy controls. Patients had clinical disease-severity scores, functional magnetic resonance imaging (fMRI and diffuse tensor imaging (DTI at baseline, 8 and at 24 weeks. Block design with alternate baseline and activation cycles was used with a total of 90 whole brain echo planar imaging (EPI measurements (timed repetition (TR = 4520 ms, timed echo (TE = 44 ms, slices = 31, slice thickness = 4 mm, EPI factor 127, matrix = 128 × 128, FOV = 230 mm. Whole brain T1-weighted images were acquired using 3D sequence (MPRage with 120 contiguous slices of 1.0 mm thickness. The mirror therapy was aimed via laptop system integrated with web camera, mirroring the movement of the unaffected hand. This therapy was administered for 5 days in a week for 60-90 min for 8 weeks. Results: All the patients showed statistical significant improvement in Fugl Meyer and modified Barthel Index (P < 0.05 whereas the change in Medical Research Council (MRC power grade was not significant post-therapy (8 weeks. There was an increase in the laterality index (LI of ipsilesional BA 4 and BA 6 at 8 weeks exhibiting recruitment and focusing principles of neural plasticity. Conclusions: Mirror therapy simulated the "action-observation" hypothesis exhibiting recovery in patients with chronic stroke. Therapy induced cortical reorganization was also observed from our study.

  17. Touch-based Brain Computer Interfaces: State of the art

    NARCIS (Netherlands)

    Erp, J.B.F. van; Brouwer, A.M.

    2014-01-01

    Brain Computer Interfaces (BCIs) rely on the user's brain activity to control equipment or computer devices. Many BCIs are based on imagined movement (called active BCIs) or the fact that brain patterns differ in reaction to relevant or attended stimuli in comparison to irrelevant or unattended stim

  18. A closed-loop compressive-sensing-based neural recording system

    Science.gov (United States)

    Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S.; Hsiao, Steven; Tran, Trac D.; Yazicioglu, Firat; Etienne-Cummings, Ralph

    2015-06-01

    Objective. This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. Approach. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Main results. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm2/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Significance. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

  19. Neural Network Based Popularity Prediction For IPTV System

    Directory of Open Access Journals (Sweden)

    Jun Li

    2012-12-01

    Full Text Available Internet protocol television (IPTV, being an emerging Internet application, plays an important and indispensable role in our daily life. In order to maximize user experience and on the same time to minimize service cost, we must take into pay attention to how to reduce the storage and transport costs. A lot of previous work has been done before to do this. There is a challenging problem in this: how to predict the popularities of videos as accurate as possible. To solve the problem, this paper presents a Neural Network model for the popularity prediction of the programs in the IPTV system. And we use the actual historical logs to validate our method. The historical logs are divided to two parts, one is used to train the neural network by extract input/output vectors, and the other part is used to verify the model. The experimental results from our validation show the Neural Network based method can gain better accuracy than the comparative method.

  20. Entropy based comparison of neural networks for classification

    Energy Technology Data Exchange (ETDEWEB)

    Draghici, S. [Wayne State Univ., Detroit, MI (United States). Vision and Neural Networks Lab.; Beiu, V. [Los Alamos National Lab., NM (United States)

    1997-04-01

    In recent years, multilayer feedforward neural networks (NN) have been shown to be very effective tools in many different applications. A natural and essential step in continuing the diffusion of these tools in day by day use is their hardware implementation which is by far the most cost effective solution for large scale use. When the hardware implementation is contemplated, the issue of the size of the NN becomes crucial because the size is directly proportional with the cost of the implementation. In this light, any theoretical results which establish bounds on the size of a NN for a given problem is extremely important. In the same context, a particularly interesting case is that of the neural networks using limited integer weights. These networks are particularly suitable for hardware implementation because they need less space for storing the weights and the fixed point, limited precision arithmetic has much cheaper implementations in comparison with its floating point counterpart. This paper presents an entropy based analysis which completes, unifies and correlates results partially presented in [Beiu, 1996, 1997a] and [Draghici, 1997]. Tight bounds for real and integer weight neural networks are calculated.

  1. Beam pattern evaluation for cyclotron operations based on neural networks

    International Nuclear Information System (INIS)

    A beam pattern evaluation method using neural network has been developed to assist non-expert cyclotron operators. While an expert operator can easily tell beam accelerating conditions by the beam pattern measured by a scanned beam probe, it is not easy for non-expert operators to evaluate the pattern. The followings are the summarized procedure of the proposed method. First, the features of the beam patterns, which correspond to the view points of the experts, are extracted using Gabor expansion. A neural network algorithm is applied to calculate the Gabor expansion. Next, the number of the extracted features is reduced by averaging the features of high frequency ranges in five partial zones. The idea of this process is based on the fact that the operators do not pay attention to the details of the high frequency components of the patterns. Finally, the pattern evaluation process by the expert operators is learned by the back-propagation algorithm on a multi-layered feed forward neural network. Parallel processing architecture of the feature extraction network, and the learning capability of the non-linear clustering network are very useful for the evaluation model of beam patterns. (author)

  2. Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Classification

    OpenAIRE

    Rajendra Palange,; Nishikant Pachpute

    2015-01-01

    This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accu...

  3. Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based on M-Matrix

    OpenAIRE

    Wuneng Zhou; Xueqing Yang; Jun Yang; Jun Zhou

    2015-01-01

    The problem of stochastic synchronization of neutral-type neural networks with multidelays based on M-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional and M-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switc...

  4. Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks

    Institute of Scientific and Technical Information of China (English)

    费翔; 何小燕; 罗军舟; 吴介一; 顾冠群

    2000-01-01

    Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.

  5. A Neural Network Based Collision Detection Engine for Multi-Arm Robotic Systems

    OpenAIRE

    Rana, A. S.; Zalzala, A.M.S.

    1996-01-01

    A neural ntwork is proposed for collision detection among multiple robotic arms sharing a common workspace. The structure of the neural network is a hybrid between Guassian Radial Basis Function (RBF) neural networks and Multi-layer perceptron back-propagation (BP) neural networks. This network is used to generate potential fields in the configuration space of the robotic arms. A path planning algorithm based on heuristics is presented. It is shown that this algorithm works better than the c...

  6. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    Science.gov (United States)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  7. A Nanoscale Interface Promoting Molecular and Functional Differentiation of Neural Cells.

    Science.gov (United States)

    Posati, Tamara; Pistone, Assunta; Saracino, Emanuela; Formaggio, Francesco; Mola, Maria Grazia; Troni, Elisabetta; Sagnella, Anna; Nocchetti, Morena; Barbalinardo, Marianna; Valle, Francesco; Bonetti, Simone; Caprini, Marco; Nicchia, Grazia Paola; Zamboni, Roberto; Muccini, Michele; Benfenati, Valentina

    2016-01-01

    Potassium channels and aquaporins expressed by astrocytes are key players in the maintenance of cerebral homeostasis and in brain pathophysiologies. One major challenge in the study of astrocyte membrane channels in vitro, is that their expression pattern does not resemble the one observed in vivo. Nanostructured interfaces represent a significant resource to control the cellular behaviour and functionalities at micro and nanoscale as well as to generate novel and more reliable models to study astrocytes in vitro. However, the potential of nanotechnologies in the manipulation of astrocytes ion channels and aquaporins has never been previously reported. Hydrotalcite-like compounds (HTlc) are layered materials with increasing potential as biocompatible nanoscale interface. Here, we evaluate the effect of the interaction of HTlc nanoparticles films with primary rat neocortical astrocytes. We show that HTlc films are biocompatible and do not promote gliotic reaction, while favouring astrocytes differentiation by induction of F-actin fibre alignment and vinculin polarization. Western Blot, Immunofluorescence and patch-clamp revealed that differentiation was accompanied by molecular and functional up-regulation of both inward rectifying potassium channel Kir 4.1 and aquaporin 4, AQP4. The reported results pave the way to engineering novel in vitro models to study astrocytes in a in vivo like condition. PMID:27503424

  8. Robot Animals Based on Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Yang Xia; Lei Lei; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    The study of robot animals based on brain-computer interface (BCI) technology is an important field in robots and neuroscience at present.In this paper,the development status at home and abroad of the motion control of robot based on BCI and principle of robot animals are introduced,then a new animals' behavior control method by photostimulation is presented.At last,the application prospect is provided.

  9. Content Based Image Retrieval : Classification Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Shereena V.B

    2014-11-01

    Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.

  10. Content Based Image Retrieval : Classification Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Shereena V.B

    2014-10-01

    Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.

  11. Neural Network Based Montioring and Control of Fluidized Bed.

    Energy Technology Data Exchange (ETDEWEB)

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  12. Neutron spectrometry and dosimetry based on a new approach called Genetic Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)

  13. 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.

  14. Star pattern recognition method based on neural network

    Institute of Scientific and Technical Information of China (English)

    LI Chunyan; LI Ke; ZHANG Longyun; JIN Shengzhen; ZU Jifeng

    2003-01-01

    Star sensor is an avionics instrument used to provide the absolute 3-axis attitude of a spacecraft by utilizing star observations. The key function is to recognize the observed stars by comparing them with the reference catalogue. Autonomous star pattern recognition requires that similar patterns can be distinguished from each other with a small training set. Therefore, a new method based on neural network technology is proposed and a recognition system containing parallel backpropagation (BP) multi-subnets is designed. The simulation results show that the method performs much better than traditional algorithms and the proposed system can achieve both higher recognition accuracy and faster recognition speed.

  15. Pulse frequency classification based on BP neural network

    Institute of Scientific and Technical Information of China (English)

    WANG Rui; WANG Xu; YANG Dan; FU Rong

    2006-01-01

    In Traditional Chinese Medicine (TCM), it is an important parameter of the clinic disease diagnosis to analysis the pulse frequency. This article accords to pulse eight major essentials to identify pulse type of the pulse frequency classification based on back-propagation neural networks (BPNN). The pulse frequency classification includes slow pulse, moderate pulse, rapid pulse etc. By feature parameter of the pulse frequency analysis research and establish to identify system of pulse frequency features. The pulse signal from detecting system extracts period, frequency etc feature parameter to compare with standard feature value of pulse type. The result shows that identify-rate attains 92.5% above.

  16. Numerical Analysis of Modeling Based on Improved Elman Neural Network

    OpenAIRE

    Shao Jie; Wang Li; Zhao WeiSong; Zhong YaQin; Reza Malekian

    2014-01-01

    A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power...

  17. 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.

  18. XML-based analysis interface for particle physics data analysis

    International Nuclear Information System (INIS)

    The letter emphasizes on an XML-based interface and its framework for particle physics data analysis. The interface uses a concise XML syntax to describe, in data analysis, the basic tasks: event-selection, kinematic fitting, particle identification, etc. and a basic processing logic: the next step goes on if and only if this step succeeds. The framework can perform an analysis without compiling by loading the XML-interface file, setting p in run-time and running dynamically. An analysis coding in XML instead of C++, easy-to-understood arid use, effectively reduces the work load, and enables users to carry out their analyses quickly. The framework has been developed on the BESⅢ offline software system (BOSS) with the object-oriented C++ programming. These functions, required by the regular tasks and the basic processing logic, are implemented with both standard modules or inherited from the modules in BOSS. The interface and its framework have been tested to perform physics analysis. (authors)

  19. 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.

  20. STUDY ON THE COAL-ROCK INTER-FACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

    Institute of Scientific and Technical Information of China (English)

    Ren Fang; Yang Zhaojian; Xiong Shibo

    2003-01-01

    The coal-rock interface recognition method based on multi-sensor data fusion technique is put forward because of the localization of single type sensor recognition method. The measuring theory based on multi-sensor data fusion technique is analyzed, and hereby the test platform of recognition system is manufactured. The advantage of data fusion with the fuzzy neural network (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carried out. The experiments show that in various conditions the method can always acquire a much higher recognition rate than normal ones.

  1. A WEARABLE NEURAL INTERFACE FOR REAL TIME TRANSLATION OF SPANISH DEAF SIGN LANGUAGE TO VOICE AND WRITING

    Directory of Open Access Journals (Sweden)

    H. Hidalgo-Silva

    2005-12-01

    Full Text Available This paper describes a work related to the design and implementation of a communication tool for persons withspeech and hearing disabilities. This tool provides to the user a Human-Computer interface capable of the captureand recognition of gestures belonging to the Mexican Spanish Sign Alphabet. To capture the manual expressions, adata-glove constructed to sense the position of fifteen articulations of one of the user’s hand is described. Alocation system that detects the position and movements of the hand with respect to the user’s body is alsoconstructed. The data-glove and location system signals are processed by a pair of programmable automatons. Theautomaton’s outputs are sent to a personal computer that realizes the gesture recognition and interpretation tasks.Artificial neural network techniques are utilized to implement the mappings of the space of information generatedby the instruments to the interpretation space, where the representation of the gestures are found. Once a gestureis captured and interpreted, it is presented in written form through a screen mounted in the clothes of the user,and in verbal form by a speaker.

  2. Switchable Thermal Interfaces Based on Discrete Liquid Droplets

    OpenAIRE

    Yongho Sungtaek Ju; Gilhwan Cha; Yanbing Jia

    2012-01-01

    We present a switchable thermal interface based on an array of discrete liquid droplets initially confined on hydrophilic islands on a substrate. The droplets undergo reversible morphological transition into a continuous liquid film when they are mechanically compressed by an opposing substrate to create low-thermal resistance heat conduction path. We investigate a criterion for reversible switching in terms of hydrophilic pattern size and liquid volume. The dependence of the liquid morpholog...

  3. fNIRS-based brain-computer interfaces: a review

    OpenAIRE

    Noman eNaseer; Keum-Shik eHong

    2015-01-01

    A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The mos...

  4. A thermal logic device based on fluid-solid interfaces

    OpenAIRE

    Murad, Sohail; Puri, Ishwar K.

    2013-01-01

    Thermal rectification requires that thermal conductivity not be a separable function of position and temperature. Investigators have considered inhomogeneous solids to design thermal rectifiers but manipulations of solid lattices are energy intensive. We propose a thermal logic device based on asymmetric solid-fluid resistances that couples two fluid reservoirs separated by solid-fluid interfaces. It is the thermal analog of a three terminal transistor, the hot reservoir being the emitter, th...

  5. Neural Networks

    International Nuclear Information System (INIS)

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

  6. 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.

  7. Implementation of neural networks using quantum well based excitonic devices

    International Nuclear Information System (INIS)

    Implementation is a key bottleneck for tapping the vast potential of neural networks. In this paper the authors examine experimentally and theoretically two devices based on III-V technology, which are critical in the implementation of the Hopfield model as well as other neural type networks for associative memories. The devices are based on Stark effect of excitonic transitions. P-1 (multiquantum wells)-n structures using GaAs/AlGaAs provide a controller-modulator device which has integrating-thresholding properties required of neurons. The p-i-n structures also provide programmable modulators which can serve as a synaptic mask. Using Monte Carlo techniques they examine an all-optical architecture to implement the Hopfield network. No external feedback-thresholding circuitry is required in this implementation due to special design of the controller-modulator device. Speed and stability issues of this architecture are also addressed. The computer simulation results provide valuable insight into how the controller-modulator device should be improved for better network implementation. It is also important to note that the basic technology now exists for such an implementation

  8. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  9. Speech Recognition Method Based on Multilayer Chaotic Neural Network

    Institute of Scientific and Technical Information of China (English)

    REN Xiaolin; HU Guangrui

    2001-01-01

    In this paper,speech recognitionusing neural networks is investigated.Especially,chaotic dynamics is introduced to neurons,and a mul-tilayer chaotic neural network (MLCNN) architectureis built.A learning algorithm is also derived to trainthe weights of the network.We apply the MLCNNto speech recognition and compare the performanceof the network with those of recurrent neural net-work (RNN) and time-delay neural network (TDNN).Experimental results show that the MLCNN methodoutperforms the other neural networks methods withrespect to average recognition rate.

  10. Neural Network Based Parking via Google Map Guidance

    Directory of Open Access Journals (Sweden)

    A.Saranya

    2015-02-01

    Full Text Available Intelligent transportation systems (ITS focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.

  11. Neural network based feed-forward high density associative memory

    Science.gov (United States)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  12. Control of GMA Butt Joint Welding Based on Neural Networks

    DEFF Research Database (Denmark)

    Christensen, Kim Hardam; Sørensen, Torben

    2004-01-01

    This paper presents results from an experimentally based research on Gas Metal Arc Welding (GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality in...... the challenging field of butt joint welding with full penetration under stochastically changing boundary conditions, e.g. major gap width variations. GMAW experiments performed on mild-steel plates (3 mm of thickness), show that high quality welds with uniform back-bead geometry are achievable for gap...... width variations from 0.5 mm to 2.3 mm - scanned 10 mm in front of the electrode location. In this research, the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a static multi-layer feed-forward network. The Levenberg-Marquardt algorithm, for...

  13. Neural Network Based Model for Predicting Housing Market Performance

    Institute of Scientific and Technical Information of China (English)

    Ahmed Khalafallah

    2008-01-01

    The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.

  14. Neural Network based Vehicle Classification for Intelligent Traffic Control

    Directory of Open Access Journals (Sweden)

    Saeid Fazli

    2012-06-01

    Full Text Available Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve controlling and urban management and increase confidence index in roads and highways. The goal of thisarticle is vehicles classification base on neural networks. In this research, it has been used a immovable camera which is located in nearly close height of the road surface to detect and classify the vehicles. The algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic situations by using some techniques included image processing and remove background of the images and performing edge detection and morphology operations. In the second phase, vehicles near the camera areselected and the specific features are processed and extracted. These features apply to the neural networks as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the algorithm and its highly functional level.

  15. Comparison Of Power Quality Disturbances Classification Based On Neural Network

    Directory of Open Access Journals (Sweden)

    Nway Nway Kyaw Win

    2015-07-01

    Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

  16. Towards emotion modeling based on gaze dynamics in generic interfaces

    DEFF Research Database (Denmark)

    Vester-Christensen, Martin; Leimberg, Denis; Ersbøll, Bjarne Kjær;

    2005-01-01

    Gaze detection can be a useful ingredient in generic human computer interfaces if current technical barriers are overcome. We discuss the feasibility of concurrent posture and eye-tracking in the context of single (low cost) camera imagery. The ingredients in the approach are posture and eye regi...... extraction based on active appearance modeling and eye tracking using a new fast and robust heuristic. The eye tracker is shown to perform well for low resolution image segments, hence, making it feasible to estimate gaze using a single generic camera.......Gaze detection can be a useful ingredient in generic human computer interfaces if current technical barriers are overcome. We discuss the feasibility of concurrent posture and eye-tracking in the context of single (low cost) camera imagery. The ingredients in the approach are posture and eye region...

  17. Switchable Thermal Interfaces Based on Discrete Liquid Droplets

    Directory of Open Access Journals (Sweden)

    Yongho Sungtaek Ju

    2012-01-01

    Full Text Available We present a switchable thermal interface based on an array of discrete liquid droplets initially confined on hydrophilic islands on a substrate. The droplets undergo reversible morphological transition into a continuous liquid film when they are mechanically compressed by an opposing substrate to create low-thermal resistance heat conduction path. We investigate a criterion for reversible switching in terms of hydrophilic pattern size and liquid volume. The dependence of the liquid morphology and rupture distance on the diameter and areal fraction of hydrophilic islands, liquid volumes, as well as loading pressure is also characterized both theoretically and experimentally. The thermal resistance in the on-state is experimentally characterized for ionic liquids, which are promising for practical applications due to their negligible vapor pressure. A life testing setup is constructed to evaluate the reliability of the interface under continued switching conditions at relatively high switching frequencies.

  18. ATCA-based ATLAS FTK input interface system

    International Nuclear Information System (INIS)

    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 η-φ 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

  19. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  20. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  1. Neurally and ocularly informed graph-based models for searching 3D environments

    Science.gov (United States)

    Jangraw, David C.; Wang, Jun; Lance, Brent J.; Chang, Shih-Fu; Sajda, Paul

    2014-08-01

    Objective. As we move through an environment, we are constantly making assessments, judgments and decisions about the things we encounter. Some are acted upon immediately, but many more become mental notes or fleeting impressions—our implicit ‘labeling’ of the world. In this paper, we use physiological correlates of this labeling to construct a hybrid brain-computer interface (hBCI) system for efficient navigation of a 3D environment. Approach. First, we record electroencephalographic (EEG), saccadic and pupillary data from subjects as they move through a small part of a 3D virtual city under free-viewing conditions. Using machine learning, we integrate the neural and ocular signals evoked by the objects they encounter to infer which ones are of subjective interest to them. These inferred labels are propagated through a large computer vision graph of objects in the city, using semi-supervised learning to identify other, unseen objects that are visually similar to the labeled ones. Finally, the system plots an efficient route to help the subjects visit the ‘similar’ objects it identifies. Main results. We show that by exploiting the subjects’ implicit labeling to find objects of interest instead of exploring naively, the median search precision is increased from 25% to 97%, and the median subject need only travel 40% of the distance to see 84% of the objects of interest. We also find that the neural and ocular signals contribute in a complementary fashion to the classifiers’ inference of subjects’ implicit labeling. Significance. In summary, we show that neural and ocular signals reflecting subjective assessment of objects in a 3D environment can be used to inform a graph-based learning model of that environment, resulting in an hBCI system that improves navigation and information delivery specific to the user’s interests.

  2. NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

    International Nuclear Information System (INIS)

    In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252Cf, 241AmBe and 239PuBe neutron sources measured with a Bonner spheres system

  3. NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac (Mexico); Vega-Carrillo, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac., Mexico. and Unidad Academica de Estudios Nucleares. C. Cip (Mexico)

    2013-07-03

    In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.

  4. A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks

    Science.gov (United States)

    Rai, Man Mohan

    2001-01-01

    An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.

  5. Sub-pixel mapping method based on BP neural network

    Institute of Scientific and Technical Information of China (English)

    LI Jiao; WANG Li-guo; ZHANG Ye; GU Yan-feng

    2009-01-01

    A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel. The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information. Then the sub-pixel scaled target could be predicted by the trained model. In order to improve the performance of BP network, BP learning algorithm with momentum was employed. The experiments were conducted both on synthetic images and on hyperspectral imagery (HSI). The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.

  6. Image restoration techniques based on fuzzy neural networks

    Institute of Scientific and Technical Information of China (English)

    刘普寅; 李洪兴

    2002-01-01

    By establishing some suitable partitions of input and output spaces, a novel fuzzy neuralnetwork (FNN) which is called selection type FNN is developed. Such a system is a multilayerfeedforward neural network, which can be a universal approximator with maximum norm. Based ona family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN isconstructed. As a result, the fusion of selection type FNN and inference type FNN results in a novelfilter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithmfor structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed onimage and preserve fine image structure, simultaneously. Some examples are simulated to confirmthe advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzymean (AWFM) filter and so on, in suppression of noises and preservation of image structure.

  7. Edge detection of noisy images based on cellular neural networks

    Science.gov (United States)

    Li, Huaqing; Liao, Xiaofeng; Li, Chuandong; Huang, Hongyu; Li, Chaojie

    2011-09-01

    This paper studies a technique employing both cellular neural networks (CNNs) and linear matrix inequality (LMI) for edge detection of noisy images. Our main work focuses on training templates of noise reduction and edge detection CNNs. Based on the Lyapunov stability theorem, we derive a criterion for global asymptotical stability of a unique equilibrium of the noise reduction CNN. Then we design an approach to train edge detection templates, and this approach can detect the edge precisely and efficiently, i.e., by only one iteration. Finally, we illustrate performance of the proposed methodology from the aspect of peak signal to noise ratio (PSNR) through computer simulations. Moreover, some comparisons are also given to prove that our method outperforms classical operators in gray image edge detection.

  8. Neurally based measurement and evaluation of environmental noise

    CERN Document Server

    Soeta, Yoshiharu

    2015-01-01

    This book deals with methods of measurement and evaluation of environmental noise based on an auditory neural and brain-oriented model. The model consists of the autocorrelation function (ACF) and the interaural cross-correlation function (IACF) mechanisms for signals arriving at the two ear entrances. Even when the sound pressure level of a noise is only about 35 dBA, people may feel annoyed due to the aspects of sound quality. These aspects can be formulated by the factors extracted from the ACF and IACF. Several examples of measuring environmental noise—from outdoor noise such as that of aircraft, traffic, and trains, and indoor noise such as caused by floor impact, toilets, and air-conditioning—are demonstrated. According to the noise measurement and evaluation, applications for sound design are discussed. This book provides an excellent resource for students, researchers, and practitioners in a wide range of fields, such as the automotive, railway, and electronics industries, and soundscape, architec...

  9. Biocompatible benzocyclobutene-based intracortical neural implant with surface modification

    Science.gov (United States)

    Lee, Keekeun; Massia, Stephen; He, Jiping

    2005-11-01

    This paper presents the fabrication of a benzocyclobutene (BCB) polymer-based intracortical neural implant for reliable and stable long-term implant function. BCB polymer has many attractive features for chronic implant application: flexibility, biocompatibility, low moisture uptake, low dielectric constant and easy surface modification. A 2 µm thick silicon backbone layer was attached underneath a flexible BCB electrode to improve mechanical stiffness. No insertion trauma was observed during penetrating into the dura of a rat. In vitro cytotoxicity tests of the completed BCB electrode revealed no toxic effects on cultured cells. The modified BCB surface with a dextran coating showed a significant reduction in 3T3 cell adhesion and spreading, indicating that this coating has the potential for lowering protein adsorption, minimizing inflammatory cell adhesion and glial scar formation in vivo, and thereby enhancing long-term implant performance.

  10. Intelligent control based on fuzzy logic and neural net theory

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  11. Neural Network Based Color Recognition for Bobbin Sorting Machine

    Directory of Open Access Journals (Sweden)

    Mu Zhang

    2013-07-01

    Full Text Available Winding is a key process in the manufacturing process of textile industry. The normal and effective operation of winding process plays a very important role on the textiles’ quality and economic effects. At present, a large proportion of bobbins which collected from winder still have yarn left over. The bobbin recycling is severely limited and quick running of winder is seriously restricted, the invention of the the automatic bobbin sorting machine has solved this problem. The ability to distinguish bobbin which has yarn left over from the rest and the classification accuracy of color are the two important performance indicators for bobbin sorting machine. According to the development and application of the color recognition technology and the artificial intelligence method, this study proposes a novel color recognition method that based on BP neural networks. The result shows that the accuracy of color recognition reaches 98%.  

  12. Functional 3D Neural Mini-Tissues from Printed Gel-Based Bioink and Human Neural Stem Cells.

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Lozano, Rodrigo; Chen, Yu; Kapsa, Robert M; Zhou, Qi; Wallace, Gordon G; Crook, Jeremy M

    2016-06-01

    Direct-write printing of stem cells within biomaterials presents an opportunity to engineer tissue for in vitro modeling and regenerative medicine. Here, a first example of constructing neural tissue by printing human neural stem cells that are differentiated in situ to functional neurons and supporting neuroglia is reported. The supporting biomaterial incorporates a novel clinically relevant polysaccharide-based bioink comprising alginate, carboxymethyl-chitosan, and agarose. The printed bioink rapidly gels by stable cross-linking to form a porous 3D scaffold encapsulating stem cells for in situ expansion and differentiation. Differentiated neurons form synaptic contacts, establish networks, are spontaneously active, show a bicuculline-induced increased calcium response, and are predominantly gamma-aminobutyric acid expressing. The 3D tissues will facilitate investigation of human neural development, function, and disease, and may be adaptable for engineering other 3D tissues from different stem cell types. PMID:27028356

  13. Neural model of finite state automaton based on hysteresis microensembles

    International Nuclear Information System (INIS)

    The artificial neural network approach for the implementation of a deterministic finite state automaton has been considered. The previously proposed model of the micro-ensemble has been used as a building block for the automaton. It has been shown that hysteresis dynamics of such a model provides the memorize property needed for a neural network implementing the automaton. In addition, a method for the transformation of any deterministic finite state automaton into a functionally equivalent neural network has been proposed

  14. Sensor Temperature Compensation Technique Simulation Based on BP Neural Network

    OpenAIRE

    Xiangwu Wei

    2013-01-01

    Innovatively, neural network function programming in the BPNN (BP neural network) tool boxes from MATLAB are applied, and data processing is done about CYJ-101 pressure sensor, and the problem of the sensor temperature compensation is solved. The paper has made the pressure sensors major sensors and temperature sensor assistant sensors, input the voltage signal from the two sensors into the established BP neural network model, and done the simulation under the NN Toolbox environment of MATLAB...

  15. Methods of Forecasting Based on Artificial Neural Networks

    OpenAIRE

    Stepčenko, A; Borisovs, A

    2014-01-01

    This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neur...

  16. Optimization of Component Based Software Engineering Model Using Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar

    2014-10-01

    Full Text Available The goal of Component Based Software Engineering (CBSE is to deliver high quality, more reliable and more maintainable software systems in a shorter time and within limited budget by reusing and combining existing quality components. A high quality system can be achieved by using quality components, framework and integration process that plays a significant role. So, techniques and methods used for quality assurance and assessment of a component based system is different from those of the traditional software engineering methodology. In this paper, we are presenting a model for optimizing Chidamber and Kemerer (CK metric values of component-based software. A deep analysis of a series of CK metrics of the software components design patterns is done and metric values are drawn from them. By using unsupervised neural network- Self Organizing Map, we have proposed a model that provides an optimized model for Software Component engineering model based on reusability that depends on CK metric values. Average, standard deviated and optimized values for the CK metric are compared and evaluated to show the optimized reusability of component based model.

  17. Term Structure of Interest Rates Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.

  18. 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.

  19. OPTIMAL PWM BASED ON REAL—TIME SOLUTION WITH NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    ShenZhongting; YanYangguang

    2002-01-01

    A novel concept of neural network based control in pulse-width modulation(PWM)voltage source inverters is presented.On the one hand,the optimal switching an-gles are obtained in real time by the neural network based controller;on the other hand,the output voltage is ad-justed to fit the expected value by neural network when input voltage or loads change.The structure of neural network is simple and easy to be realized by DSP hard-ware system.No large memory used for the existing opti-mal PWM schemes is required in the system.Theoreticalanlysis of the proposed so-called sparse neural network is provided,and the stability of the system is proved.Un-der the control of neural network the error of output volt-age descends sharply,and the system outputs ac voltage with high precision.

  20. Surfaces and interfaces in polymer-based electronics

    Science.gov (United States)

    Fahlman, M.; Salaneck, W. R.

    2002-03-01

    Research on electronics applications such as light-emitting devices for flat-panel displays, transistors, sensors and even solid state lasers based on conducting polymers is presently under way and in some cases has reached the stage of prototype production. The mechanisms for charge injection and conduction in these materials are being studied, as are the physics of luminescence and its quenching. Lately, research into controlling film morphology through self-organizing techniques also has gained interest. Though the present interest in conducting polymers mainly concerns the pristine semiconducting state, doped conducting polymers are also studied for potential use in many applications. In this paper, we present an overview of some of the central issues in surface and interface science in the field, as well as provide our view on what may lie ahead in the future. Specifically, the importance of metal/polymer, polymer/metal and polymer/polymer interfaces is addressed. We illustrate these using polymer-based light-emitting devices, though the same type of issues appear in other polymer-based applications such as transistors and solar cells.

  1. Method of Information Security Risk Assessment Based on Cloud Neural Network for the Internet of Things

    OpenAIRE

    Xiaoli Dong

    2015-01-01

    Aiming at the randomness and fuzziness of information security risk assessment factors of Internet of Things, cloud neural network information security risk assessment model was proposed, based on combination of cloud model and neural network and dynamic fusion of heterogeneous security factors. Focus on the research of normal cloud neural network evaluation method and judgment of global and multivalued dependencies characteristics between safety evaluation indicators and risk levels...

  2. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    International Nuclear Information System (INIS)

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  3. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac (Mexico); Vega-Carrillo, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac., Mexico. and Unidad Academica de Estudios Nucleares. C. Cip (Mexico)

    2013-07-03

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in

  4. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    Science.gov (United States)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-01

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  5. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  6. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  7. Fault Localization Analysis Based on Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Wei Zheng

    2016-01-01

    Full Text Available With software’s increasing scale and complexity, software failure is inevitable. To date, although many kinds of software fault localization methods have been proposed and have had respective achievements, they also have limitations. In particular, for fault localization techniques based on machine learning, the models available in literatures are all shallow architecture algorithms. Having shortcomings like the restricted ability to express complex functions under limited amount of sample data and restricted generalization ability for intricate problems, the faults cannot be analyzed accurately via those methods. To that end, we propose a fault localization method based on deep neural network (DNN. This approach is capable of achieving the complex function approximation and attaining distributed representation for input data by learning a deep nonlinear network structure. It also shows a strong capability of learning representation from a small sized training dataset. Our DNN-based model is trained utilizing the coverage data and the results of test cases as input and we further locate the faults by testing the trained model using the virtual test suite. This paper conducts experiments on the Siemens suite and Space program. The results demonstrate that our DNN-based fault localization technique outperforms other fault localization methods like BPNN, Tarantula, and so forth.

  8. Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

    Directory of Open Access Journals (Sweden)

    Amitabha Das

    2005-03-01

    Full Text Available We present an artificial neural-network- (NN- based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS operating in a wide temperature range of 0 to 250° C. Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS error of only ±1.0% over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU- based implementation scheme is also provided.

  9. Expert System Based on Data Mining and Neural Networks

    Institute of Scientific and Technical Information of China (English)

    NI Zhi-wei; JIA Rui-yu

    2001-01-01

    On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural expert system to find some unknown useful knowledge and get some satisfactory results.

  10. 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

  11. Thermoelastic steam turbine rotor control based on neural network

    Science.gov (United States)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  12. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  13. Chinese word sense disambiguation based on neural networks

    Institute of Scientific and Technical Information of China (English)

    LIU Ting; LU Zhi-mao; LANG Jun; LI Sheng

    2005-01-01

    The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to ( - M, + N). The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of M and N affect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90. 31% ,and 89. 62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.

  14. INTERPRETATION TRAINED NEURAL NETWORKS BASED ON GENETIC ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Safa S. Ibrahim

    2013-01-01

    Full Text Available In this paper, constructive learning is used to train the neural networks. The results of neural networks are obtained but its result is not in comprehensible form or in a black box form. Our goal is to use an important and desirable model to identify sets of input variable which results in a desired output value. The nature of this model can help to find an optimal set of difficult input variables. Accuracy. Genetic algorithms are used as an interpretation of achieving neural network inversion. On the other hand the inversion of neural network enables to find one or more input patterns which satisfy a specific output. The input patterns obtained from the genetic algorithm can be used for building neural network system explanation facilities.

  15. Stem cell-based therapy in neural repair.

    Science.gov (United States)

    Hsu, Yi-Chao; Chen, Su-Liang; Wang, Dan-Yen; Chiu, Ing-Ming

    2013-01-01

    Cell-based therapy could aid in alleviating symptoms or even reversing the progression of neurodegenerative diseases and nerve injuries. Fibroblast growth factor 1 (FGF1) has been shown to maintain the survival of neurons and induce neurite outgrowth. Accumulating evidence suggests that combination of FGF1 and cell-based therapy is promising for future therapeutic application. Neural stem cells (NSCs), with the characteristics of self-renewal and multipotency, can be isolated from embryonic stem cells, embryonic ectoderm, and developing or adult brain tissues. For NSC clinical application, several critical problems remain to be resolved: (1) the source of NSCs should be personalized; (2) the isolation methods and protocols of human NSCs should be standardized; (3) the clinical efficacy of NSC transplants must be evaluated in more adequate animal models; and (4) the mechanism of intrinsic brain repair needs to be better characterized. In addition, the ideal imaging technique for tracking NSCs would be safe and yield high temporal and spatial resolution, good sensitivity and specificity. Here, we discuss recent progress and future development of cell-based therapy, such as NSCs, induced pluripotent stem cells, and induced neurons, in neurodegenerative diseases and peripheral nerve injuries. PMID:23806879

  16. An Efficient Sentence-based Sentiment Analysis for Expressive Text-to-speech using Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    B. Sudhakar

    2014-07-01

    Full Text Available In recent years, speech processing has become an active research area in the field of signal processing due to the usage of automated systems for spoken language interface. In developed countries, the customer service with automated system in speech synthesis has been the recent trend. The existing automated speech synthesis systems have certain problems during the real time implementation such as lack of naturalness in output speech, lack of emotions and so on. In this study, the novel Text to Speech system is introduced along with the sentiment analysis in Tamil language. The input text is first classified into the positive, negative and neutral based on the emotions in the sentence then the text is converted into speech with emotions during TTS conversion. Existing approaches used neural network based classifiers for classification. But, neural networks have certain drawbacks in real time training. So, this research study uses Fuzzy Neural Network (FNN to classify the sentence based on the emotions. The text to speech with sentiment analysis effective scheme which is evaluated using Doordarshan news Tamil dataset. The proposed scheme is implemented using MATLAB. This TTS system has several social applications, especially in railway stations where the announcements can be made through expressive speech.

  17. Intercurrent fault diagnosis of nuclear power plants based on hybrid artificial neural network

    International Nuclear Information System (INIS)

    Based on the analysis of the structure of ART-2 and parallel BP neural network, a hybrid artificial neural network is proposed aiming at the intercurrent faults diagnosis of nuclear power plants. Firstly the ART-2 net is used to identify the single fault, then the parallel BP net is used to distinguish intercurrent faults from new fault. The simulation shows that, the hybrid artificial neural network resolves the problem of single neural network in distinguishing intercurrent faults from new fault, and can diagnose the intercurrent fault and new fault efficiently. (authors)

  18. Study on the Robot Robust Adaptive Control Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    温淑焕; 王洪瑞; 吴丽艳

    2003-01-01

    Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.

  19. Voltage harmonic elimination with RLC based interface smoothing filter

    Science.gov (United States)

    Chandrasekaran, K.; Ramachandaramurthy, V. K.

    2015-04-01

    A method is proposed for designing a Dynamic Voltage Restorer (DVR) with RLC interface smoothing filter. The RLC filter connected between the IGBT based Voltage Source Inverter (VSI) is attempted to eliminate voltage harmonics in the busbar voltage and switching harmonics from VSI by producing a PWM controlled harmonic voltage. In this method, the DVR or series active filter produces PWM voltage that cancels the existing harmonic voltage due to any harmonic voltage source. The proposed method is valid for any distorted busbar voltage. The operating VSI handles no active power but only harmonic power. The DVR is able to suppress the lower order switching harmonics generated by the IGBT based VSI. Good dynamic and transient results obtained. The Total Harmonic Distortion (THD) is minimized to zero at the sensitive load end. Digital simulations are carried out using PSCAD/EMTDC to validate the performance of RLC filter. Simulated results are presented.

  20. Research on Deep Web Query Interface Clustering Based on Hadoop

    OpenAIRE

    Baohua Qiang; Rui Zhang; Yufeng Wang; Qian He; Wei Li; Sai Wang

    2014-01-01

    How to cluster different query interfaces effectively is one of the most core issues when generating integrated query interface on Deep Web integration domain. However, with the rapid development of Internet technology, the number of Deep Web query interface shows an explosive growth trend. For this reason, the traditional stand-alone Deep Web query interface clustering approaches encounter bottlenecks in terms of time complexity and space complexity. After further study of the Hadoop distrib...

  1. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  2. Electromyogram-based neural network control of transhumeral prostheses

    Directory of Open Access Journals (Sweden)

    Christopher L. Pulliam, MS

    2011-07-01

    Full Text Available Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.

  3. MR-based imaging of neural stem cells

    International Nuclear Information System (INIS)

    The efficacy of therapies based on neural stem cells (NSC) has been demonstrated in preclinical models of several central nervous system (CNS) diseases. Before any potential human application of such promising therapies can be envisaged, there are some important issues that need to be solved. The most relevant one is the requirement for a noninvasive technique capable of monitoring NSC delivery, homing to target sites and trafficking. Knowledge of the location and temporospatial migration of either transplanted or genetically modified NSC is of the utmost importance in analyzing mechanisms of correction and cell distribution. Further, such a technique may represent a crucial step toward clinical application of NSC-based approaches in humans, for both designing successful protocols and monitoring their outcome. Among the diverse imaging approaches available for noninvasive cell tracking, such as nuclear medicine techniques, fluorescence and bioluminescence, magnetic resonance imaging (MRI) has unique advantages. Its high temporospatial resolution, high sensitivity and specificity render MRI one of the most promising imaging modalities available, since it allows dynamic visualization of migration of transplanted cells in animal models and patients during clinically useful time periods. Different cellular and molecular labeling approaches for MRI depiction of NSC are described and discussed in this review, as well as the most relevant issues to be considered in optimizing molecular imaging techniques for clinical application. (orig.)

  4. Batch Process Modelling and Optimal Control Based on Neural Network Models

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2005-01-01

    This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

  5. Research on the Prediction of VNN Neural Network Traffic Flow Model Based on Chaotic Algorithm

    Directory of Open Access Journals (Sweden)

    Yin Lisheng

    2013-06-01

    Full Text Available This paperresearches on the prediction of traffic flow chaotic time series based on VNNTF neural network. First, the traffic flow time series chaotic feature is extracted by chaos theory. Pretreatment for traffic flow time series and the VNNTP neural networks model was build by this. Second, principles of neural network learning algorithm VNNTF is described. Based on chaotic learning algorithm, the neural network traffic Volterra learning algorithm isdesigned for fast learning algorithm. Last, a single-step prediction of traffic flow chaotic time series is researched by VNNTF network model based on chaotic algorithm. The results showed that the VNNTF network model predictive performance is better than the Volterra prediction filter and the BP neural network   by the simulation results and root-mean-square value.

  6. Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.

  7. Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network

    Institute of Scientific and Technical Information of China (English)

    WU Xiao-guang; SHI Zhong-kun

    2006-01-01

    The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.

  8. Neural network predicts sequence of TP53 gene based on DNA chip

    DEFF Research Database (Denmark)

    Spicker, J.S.; Wikman, F.; Lu, M.L.;

    2002-01-01

    We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and...

  9. Non-fragile H∞ synchronization of memristor-based neural networks using passivity theory.

    Science.gov (United States)

    Mathiyalagan, K; Anbuvithya, R; Sakthivel, R; Park, Ju H; Prakash, P

    2016-02-01

    In this paper, we formulate and investigate the mixed H∞ and passivity based synchronization criteria for memristor-based recurrent neural networks with time-varying delays. Some sufficient conditions are obtained to guarantee the synchronization of the considered neural network based on the master-slave concept, differential inclusions theory and Lyapunov-Krasovskii stability theory. Also, the memristive neural network is considered with two different types of memductance functions and two types of gain variations. The results for non-fragile observer-based synchronization are derived in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed criterion is demonstrated through numerical examples. PMID:26655373

  10. Simulation Model of Magnetic Levitation Based on NARX Neural Networks

    Directory of Open Access Journals (Sweden)

    Dragan Antić

    2013-04-01

    Full Text Available In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.

  11. Detecting Nasal Vowels in Speech Interfaces Based on Surface Electromyography.

    Directory of Open Access Journals (Sweden)

    João Freitas

    Full Text Available Nasality is a very important characteristic of several languages, European Portuguese being one of them. This paper addresses the challenge of nasality detection in surface electromyography (EMG based speech interfaces. We explore the existence of useful information about the velum movement and also assess if muscles deeper down in the face and neck region can be measured using surface electrodes, and the best electrode location to do so. The procedure we adopted uses Real-Time Magnetic Resonance Imaging (RT-MRI, collected from a set of speakers, providing a method to interpret EMG data. By ensuring compatible data recording conditions, and proper time alignment between the EMG and the RT-MRI data, we are able to accurately estimate the time when the velum moves and the type of movement when a nasal vowel occurs. The combination of these two sources revealed interesting and distinct characteristics in the EMG signal when a nasal vowel is uttered, which motivated a classification experiment. Overall results of this experiment provide evidence that it is possible to detect velum movement using sensors positioned below the ear, between mastoid process and the mandible, in the upper neck region. In a frame-based classification scenario, error rates as low as 32.5% for all speakers and 23.4% for the best speaker have been achieved, for nasal vowel detection. This outcome stands as an encouraging result, fostering the grounds for deeper exploration of the proposed approach as a promising route to the development of an EMG-based speech interface for languages with strong nasal characteristics.

  12. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  13. Recognition of a Life Distribution Based on a Neural Network

    Institute of Scientific and Technical Information of China (English)

    GAO Shang

    2004-01-01

    In general, we describe three different methods to select an appropriate distribution form:bistogram, probability plots, and hypothesis test. The life distribution is recognized by a neural network method. The relationship among life distribution with life data is described through threshold and weight of neural networks. The method is convenient to use. An example is presented to validate this method, and the results are satisfactory.

  14. BRAIN TUMOR CLASSIFICATION USING NEURAL NETWORK BASED METHODS

    OpenAIRE

    Kalyani A. Bhawar*, Prof. Nitin K. Bhil

    2016-01-01

    MRI (Magnetic resonance Imaging) brain neoplasm pictures Classification may be a troublesome tasks due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of 3 stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the options connected with tomography pictures victimization d...

  15. Food Safety Evaluation System Construction Based on Artificial Neural Network

    OpenAIRE

    Jian Wang; Zhenmin Tang; Xianli Jin

    2015-01-01

    This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.

  16. Food Safety Evaluation System Construction Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jian Wang

    2015-05-01

    Full Text Available This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.

  17. Thermal analysis of charring materials based on pyrolysis interface model

    Directory of Open Access Journals (Sweden)

    Huang Hai-Ming

    2014-01-01

    Full Text Available Charring thermal protection systems have been used to protect hypersonic vehicles from high heat loads. The pyrolysis of charring materials is a complicated physical and chemical phenomenon. Based on the pyrolysis interface model, a simulating approach for charring ablation has been designed in order to obtain one dimensional transient thermal behavior of homogeneous charring materials in reentry capsules. As the numerical results indicate, the pyrolysis rate and the surface temperature under a given heat flux rise abruptly in the beginning, then reach a plateau, but the temperature at the bottom rises very slowly to prevent the structural materials from being heated seriously. Pyrolysis mechanism can play an important role in thermal protection systems subjected to serious aerodynamic heat.

  18. A microcontroller-based interface circuit for lossy capacitive sensors

    International Nuclear Information System (INIS)

    This paper introduces and analyses a low-cost microcontroller-based interface circuit for lossy capacitive sensors, i.e. sensors whose parasitic conductance (Gx) is not negligible. Such a circuit relies on a previous circuit also proposed by the authors, in which the sensor is directly connected to a microcontroller without using either a signal conditioner or an analogue-to-digital converter in the signal path. The novel circuit uses the same hardware, but it performs an additional measurement and executes a new calibration technique. As a result, the sensitivity of the circuit to Gx decreases significantly (a factor higher than ten), but not completely due to the input capacitances of the port pins of the microcontroller. Experimental results show a relative error in the capacitance measurement below 1% for Gx x) shows the effectiveness of the circuit

  19. APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

  20. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. PMID:25913233

  1. 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.

  2. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    Science.gov (United States)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  3. Route Selection Problem Based on Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    N. Kojic

    2013-12-01

    Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.

  4. Illicit material detector based on gas sensors and neural networks

    Science.gov (United States)

    Grimaldi, Vincent; Politano, Jean-Luc

    1997-02-01

    In accordance with its missions, le Centre de Recherches et d'Etudes de la Logistique de la Police Nationale francaise (CREL) has been conducting research for the past few years targeted at detecting drugs and explosives. We have focused our approach of the underlying physical and chemical detection principles on solid state gas sensors, in the hope of developing a hand-held drugs and explosives detector. The CREL and Laboratory and Scientific Services Directorate are research partners for this project. Using generic hydrocarbon, industrially available, metal oxide sensors as illicit material detectors, requires usage precautions. Indeed, neither the product's concentrations, nor even the products themselves, belong to the intended usage specifications. Therefore, the CREL is currently investigating two major research topics: controlling the sensor's environment: with environmental control we improve the detection of small product concentration; determining detection thresholds: both drugs and explosives disseminate low gas concentration. We are attempting to quantify the minimal concentration which triggers detection. In the long run, we foresee a computer-based tool likely to detect a target gas in a noisy atmosphere. A neural network is the suitable tool for interpreting the response of heterogeneous sensor matrix. This information processing structure, alongside with proper sensor environment control, will lessen the repercussions of common MOS sensor sensitivity characteristic dispersion.

  5. Neural Online Filtering Based on Preprocessed Calorimeter Data

    CERN Document Server

    Torres, R C; The ATLAS collaboration; Simas Filho, E F; De Seixas, J M

    2009-01-01

    Among LHC detectors, ATLAS aims at coping with such high event rate by designing a three-level online triggering system. The first level trigger output will be ~75 kHz. This level will mark the regions where relevant events were found. The second level will validate LVL1 decision by looking only at the approved data using full granularity. At the level two output, the event rate will be reduced to ~2 kHz. Finally, the third level will look at full event information and a rate of ~200 Hz events is expected to be approved, and stored in persistent media for further offline analysis. Many interesting events decay into electrons, which have to be identified from the huge background noise (jets). This work proposes a high-efficient LVL2 electron / jet discrimination system based on neural networks fed from preprocessed calorimeter information. The feature extraction part of the proposed system performs a ring structure of data description. A set of concentric rings centered at the highest energy cell is generated ...

  6. Pattern recognition for electroencephalographic signals based on continuous neural networks.

    Science.gov (United States)

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2016-07-01

    This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results. PMID:27131469

  7. Neural network-based QSAR and insecticide discovery: spinetoram.

    Science.gov (United States)

    Sparks, Thomas C; Crouse, Gary D; Dripps, James E; Anzeveno, Peter; Martynow, Jacek; Deamicis, Carl V; Gifford, James

    2008-01-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry. PMID:18344004

  8. Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process

    Energy Technology Data Exchange (ETDEWEB)

    Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)

    2005-10-01

    In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)

  9. EMP response modeling of TVS based on the recurrent neural network

    Directory of Open Access Journals (Sweden)

    Zhiqiang JI

    2015-04-01

    Full Text Available Due to the larger workload in the implementation process and the poor consistence between the test results and actual situation problems when using the transmission line pulse (TLP testing methods, a modeling method based on the recurrent neural network is proposed for EMP response forecast. Based on the TLP testing system, two categories of EMP are increased, which are the machine model ESD EMP and human metal model ESD EMP. Elman neural network, Jordan neural network and their combination namely Elman-Jordan neural network are established for response modeling of NUP2105L transient voltage suppressor (TVS forecasting the response under different EMP. The simulation results show that the recurrent neural network has satisfying modeling effects and high computation efficiency.

  10. Direct interfaces for smart skins based on FPGAs

    Science.gov (United States)

    Oballe-Peinado, Óscar; Castellanos-Ramos, Julián; Hidalgo-López, José A.; Vidal-Verdú, Fernando

    2009-05-01

    Many artificial skins for robotics are based on piezoresistive films that cover an array of electrodes. Local preprocessing is a must in these systems to reduce errors and interferences and cope with the large amount of data provided by the sensor. This paper presents circuitry based on an FPGA to implement the interface to the artificial skin. The approach consists of a direct connection. The analog to digital conversion procedure is simple. It consists of measuring the discharging time of a capacitor through the resistance we want to read. This first proposed approach needs isolated tactels, so the raw sensor has to be fabricated in this way. If the tactile array is large, the strategy is not feasible. For instance, up to 288 pins are required to implement the interface with an array of 16x16 tactels. The proposal of this work for this case is to replace passive integrators by active ones. The result is a circuitry that allows the cancellation of interferences due to parasitic resistors and the sharing of the addressing tracks. Moreover, the FPGA allows the processing of data from the tactile sensor at a very high rate. This is because the high number of I/O pins of the device allows the conversion of many channels (in our case one per column) in parallel. The internal processing of the tactile image can also be done in parallel. This means we could be able to respond to very high demanding tasks in terms of dynamic requirements, like slippage detection. This also means we can run complex algorithms at real time, so a smart, programmable and powerful sensor is obtained.

  11. Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

    This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training...... samples from the system, after which linearized state space models are extracted from the neural network in a number of operating points according to a simple and computationally cheap scheme. Robust observer-based controllers can then be designed in each of these operating points,and gain scheduling...

  12. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    International Nuclear Information System (INIS)

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved

  13. Research on a Neural Network Approach Based Diagnosis Expert System of Crack Control in Massive Concrete

    Institute of Scientific and Technical Information of China (English)

    HAN Liu-xin; WANG Huan-chen; ZHANG Xian-hui

    2001-01-01

    A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic structure, building of knowledge base and the implementation of neural network applied model. The realizing method of neural network based clustering algorithm in the knowledge base and selfstudy is analyzed emphatically and stimulated by means of the computer. From the above study, some important conclusions have been drawn and some new viewpoints have been suggested.

  14. A novel compensation-based recurrent fuzzy neural network and its learning algorithm

    Institute of Scientific and Technical Information of China (English)

    WU Bo; WU Ke; LU JianHong

    2009-01-01

    Based on detailed atudy on aeveral kinds of fuzzy neural networks, we propose a novel compensation. based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure Identification of the CRFNN In order to confirm the fuzzy rules and their correlaUve parameters effectively. Furthermore, we Improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.

  15. Synchronization of Memristor-Based Coupling Recurrent Neural Networks With Time-Varying Delays and Impulses.

    Science.gov (United States)

    Zhang, Wei; Li, Chuandong; Huang, Tingwen; He, Xing

    2015-12-01

    Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results. PMID:26054076

  16. Neural network-based H∞ filtering for nonlinear systems with time-delays

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.

  17. 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

  18. ECG Signal Recognition based on Wavelet Transform Using Neural and Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    H. M. Abdul-Ridha

    2008-01-01

    Full Text Available This work presents aneural and fuzzy based ECG signal recognition system based on wavelet transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural network is found using a proposed best basis technique. Using the proposed best bases reduces the dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and the neural network parameters are learned using back propagation algorithm.

  19. Neural Synchronization based Secret Key Exchange over Public Channels: A survey

    OpenAIRE

    Chakraborty, Sandip; Dalal, Jiban; Sarkar, Bikramjit; Mukherjee, Debaprasad

    2015-01-01

    Exchange of secret keys over public channels based on neural synchronization using a variety of learning rules offer an appealing alternative to number theory based cryptography algorithms. Though several forms of attacks are possible on this neural protocol e.g. geometric, genetic and majority attacks, our survey finds that deterministic algorithms that synchronize with the end-point networks have high time complexity, while probabilistic and population-based algorithms have demonstrated abi...

  20. EM-based optimization of microwave circuits using artificial neural networks: the state of the art

    OpenAIRE

    Rayas-Sánchez, José E.

    2004-01-01

    This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments and clusterization. Innovative strategies for ANN-based design exploiting...

  1. ECG Signal Recognition based on Wavelet Transform Using Neural and Fuzzy Logic

    OpenAIRE

    H. M. Abdul-Ridha; Abduladhem Abdulkareem Ali

    2008-01-01

    This work presents aneural and fuzzy based ECG signal recognition system based on wavelet transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural network is found using a proposed best basis technique. Using the proposed best bases reduces the dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and the neural network parameters are learned using back propagation algorithm.

  2. Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.

    Science.gov (United States)

    Li, Ning; Cao, Jinde

    2016-03-01

    This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results. PMID:26462246

  3. Neural network based method for conversion of solar radiation data

    International Nuclear Information System (INIS)

    Highlights: ► Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ► The above network, amongst many such as multilayer perceptron, is the most successful one. ► The present neural network returns a relative mean absolute error value of 9.1%. ► The present model leads to a mean absolute error value of estimate of 14.9 Wh/m2. - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m2. The other statistical values of coefficient of determination and relative mean absolute error also indicate the advantage of

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

    OpenAIRE

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

    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 c...

  5. A case study to estimate costs using Neural Networks and regression based models

    Directory of Open Access Journals (Sweden)

    Nadia Bhuiyan

    2012-07-01

    Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

  6. A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

    Institute of Scientific and Technical Information of China (English)

    ANG Xue-ye

    2007-01-01

    The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given . It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.

  7. Identification Method of Sports Throwing Force Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Rui Su

    2013-07-01

    Full Text Available In order to speed up the defects of the neural network computing and recognition, the essay proposes the information identification method research of sports throwing force based on the fuzzy neural network model. Firstly, I use the information, which is the combination of the wavelet transformation and the fuzzy neural network, to identify the new method combining and make the noise-suppressed processing of information. Then, according to the athlete’s throwing action and the extraction of signal processing characteristics, as well as the analysis of the fuzzy neural network algorithm. Finally, in order to verify the effectiveness of the proposed algorithm, I make analysis for the experimental results, which indicates that using this algorithm can not only have less noise than the traditional algorithm, but also have less number of the neural network computation. Besides, its recognition speed and accuracy is also higher.

  8. Application of a real neural collision avoidance system based on the locust to AGV navigation

    Science.gov (United States)

    Rind, F. C.; Allen, Charles R.

    1992-11-01

    The superb aereal performance of flying insects is achieved with comparatively simple neural machinery. Insects react rapidly to changing visual images. The abilities of insects to perform these computations in real time has already led to a successful prototype autonomous guided vehicle with a sensor and control structure modelled on the fly eye. Increasingly in visual neuroscience it is possible to isolate the critical image cues used by identified neurones to achieve a selective response to a feature or group of features within the changing visual image. In this paper we describe a biological neural network based on the input organization of such an identified motion detecting neurone, which responds selectively to the images of an object approaching on a collision course with the animal. We compare the response of the artificial neural network with the biological neural network in the same colliding stimulus. This approach led to a series of testable predictions about the organization of the biological neural network.

  9. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

  10. REAL-TIME MOTION PLANNING METHOD BASED ON NEURAL NETWORKS FOR MULTIPLE MOBILE ROBOTS

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The motion planning of multiple mobile robots undertaking individual tasks in the same environment is studied. A motion planning method based on neural networks is proposed. By storing fuzzy rules in neural networks the method can fully make use of the association ability and high processing speed of neural networks to make robots avoid collisions with other objects in real time.Compared with rules method,the method can not only avoid building a large and complex rules base but also make robots avoid collisions and conflicts at higher speed and with higher intelligence.

  11. Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yuan Haiying; Chen Guangju; Xie Yongle

    2007-01-01

    Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.

  12. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  13. Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

    International Nuclear Information System (INIS)

    Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.

  14. Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHOULi-Ming; ZHANGYing-Yue; CHENTian-Lun

    2005-01-01

    Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse,the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.

  15. Optimization Design based on BP Neural Network and GA Method

    Directory of Open Access Journals (Sweden)

    Bing Wang

    2013-12-01

    Full Text Available This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.

  16. Musical expertise affects neural bases of letter recognition.

    Science.gov (United States)

    Proverbio, Alice Mado; Manfredi, Mirella; Zani, Alberto; Adorni, Roberta

    2013-02-01

    It is known that early music learning (playing of an instrument) modifies functional brain structure (both white and gray matter) and connectivity, especially callosal transfer, motor control/coordination and auditory processing. We compared visual processing of notes and words in 15 professional musicians and 15 controls by recording their synchronized bioelectrical activity (ERPs) in response to words and notes. We found that musical training in childhood (from age ~8 years) modifies neural mechanisms of word reading, whatever the genetic predisposition, which was unknown. While letter processing was strongly left-lateralized in controls, the fusiform (BA37) and inferior occipital gyri (BA18) were activated in both hemispheres in musicians for both word and music processing. The evidence that the neural mechanism of letter processing differed in musicians and controls (being absolutely bilateral in musicians) suggests that musical expertise modifies the neural mechanisms of letter reading. PMID:23238370

  17. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  18. Neural Network Based Forecasting of Foreign Currency Exchange Rates

    Directory of Open Access Journals (Sweden)

    S. Kumar Chandar

    2014-06-01

    Full Text Available The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other major currencies, Pound Sterling, US Dollar, Euro and Japanese Yen are forecast by the trained neural networks. The neural network was trained by three different learning algorithms using historical data to find the suitable algorithm for prediction. The forecasting performance of the proposed system is evaluated using three statistical metrics and compared. The results presented here demonstrate that significantly close prediction can be made without extensive knowledge of market data.

  19. Ontology Based Queries - Investigating a Natural Language Interface

    NARCIS (Netherlands)

    van der Sluis, Ielka; Hielkema, F.; Mellish, C.; Doherty, G.

    2010-01-01

    In this paper we look at what may be learned from a comparative study examining non-technical users with a background in social science browsing and querying metadata. Four query tasks were carried out with a natural language interface and with an interface that uses a web paradigm with hyperlinks.

  20. Age Based User Interface in Mobile Operating System

    CERN Document Server

    Sharma, Sumit; Singh, Paramjit; Mahajan, Aditya; 10.5121/ijcsea.2012.2215

    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.

  1. Designing and application of SAN extension interface based on CWDM

    Science.gov (United States)

    Qin, Leihua; Yu, Shengsheng; Zhou, Jingli

    2005-11-01

    As Fibre Channel (FC) becomes the protocol of choice within corporate data centers, enterprises are increasingly deploying SANs in their data central. In order to mitigate the risk of losing data and improve the availability of data, more and more enterprises are increasingly adopting storage extension technologies to replicate their business critical data to a secondary site. Transmitting this information over distance requires a carrier grade environment with zero data loss, scalable throughput, low jitter, high security and ability to travel long distance. To address this business requirements, there are three basic architectures for storage extension, they are Storage over Internet Protocol, Storage over Synchronous Optical Network/Synchronous Digital Hierarchy (SONET/SDH) and Storage over Dense Wavelength Division Multiplexing (DWDM). Each approach varies in functionality, complexity, cost, scalability, security, availability , predictable behavior (bandwidth, jitter, latency) and multiple carrier limitations. Compared with these connectiviy technologies,Coarse Wavelength Division Multiplexing (CWDM) is a Simplified, Low Cost and High Performance connectivity solutions for enterprises to deploy their storage extension. In this paper, we design a storage extension connectivity over CWDM and test it's electrical characteristic and random read and write performance of disk array through the CWDM connectivity, testing result show us that the performance of the connectivity over CWDM is acceptable. Furthermore, we propose three kinds of network architecture of SAN extension based on CWDM interface. Finally the credit-Based flow control mechanism of FC, and the relationship between credits and extension distance is analyzed.

  2. Operation and control interfaces based upon distributed agent networks

    International Nuclear Information System (INIS)

    The majority of todays large scale compute clusters and software systems running on them are using operation and control interfaces (OCI) for monitoring and control. The majority of these OCI's are still based upon single node applications, which are limited by the physical system they are running on. In areas where hundred thousand and more statistical values have to be analyzed and taken into account for visualization and decision making this kind of OCI's are no option at all. Furthermore, this kind of OCI's do not empower whole collaborations to control and operate cluster at the same time from around the world. Distributed agent networks (DAN) tend to have the possibility to overcome this limitations. A distributed agent network is per design a multi-node approach. Together with a web based OCI, automatic data propagation and distributed locking algorithms they provide simultaneous operation and control, distributed state tracking and visualization to world wide collaborations. The first compute cluster in the scientific world using this combination of technologies is the ALICE HLT at CERN.

  3. The neural correlates of gist-based true and false recognition

    OpenAIRE

    Gutchess, Angela H.; Schacter, Daniel L.

    2011-01-01

    When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temp...

  4. Neural Machine Learning Approaches: Q-Learning and Complexity Estimation Based Information Processing System

    OpenAIRE

    Chebira, Abdennasser; MELLOUK, Abdelhamid; Madani, Kurosh; Hoceini, Said

    2009-01-01

    Due the complexity of the actual systems based on heterogeneous methods, artificial neural networks approaches can reduce this complexity by modeling the environment as stochastic. Algorithms based on Neural Networks can take into account the dynamics of these environments with no model of dynamics to be given. Main idea of the approaches developed in this chapter is to take advantage from distributed processing and task simplification by dividing an initially complex processing task into a s...

  5. Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays

    OpenAIRE

    Huaiqin Wu; Luying Zhang; Sanbo Ding; Xueqing Guo; Lingling Wang

    2013-01-01

    This paper investigates the complete periodic synchronization of memristor-based neural networks with time-varying delays. Firstly, under the framework of Filippov solutions, by using M-matrix theory and the Mawhin-like coincidence theorem in set-valued analysis, the existence of the periodic solution for the network system is proved. Secondly, complete periodic synchronization is considered for memristor-based neural networks. According to the state-dependent switching feature of the memrist...

  6. Dynamical analysis of memristor-based fractional-order neural networks with time delay

    Science.gov (United States)

    Cui, Xueli; Yu, Yongguang; Wang, Hu; Hu, Wei

    2016-06-01

    In this paper, the memristor-based fractional-order neural networks with time delay are analyzed. Based on the theories of set-value maps, differential inclusions and Filippov’s solution, some sufficient conditions for asymptotic stability of this neural network model are obtained when the external inputs are constants. Besides, uniform stability condition is derived when the external inputs are time-varying, and its attractive interval is estimated. Finally, numerical examples are given to verify our results.

  7. Development of Genetic Algorithm based Neural Network model for parameter estimation of Fast Breeder Reactor Subsystem

    OpenAIRE

    Subhra Rani Patra; R. Jehadeesan; Rajeswari, S.

    2012-01-01

    This work provides the construction of Genetic Algorithm based Neural Network for parameter estimation of Fast Breeder Test Reactor (FBTR) Subsystem. The parameter estimated here is temperature of Intermediate Heat Exchanger of Fast Breeder Test Reactor. Genetic Algorithm based Neural Network is a global search algorithm having less probability of being trapped in local minimum problem as compared to Standard Back Propagation algorithm which is a local search algorithm. The various developmen...

  8. A nonlinear PCA algorithm based on RBF neural networks

    Institute of Scientific and Technical Information of China (English)

    YANG Bin; ZHU Zhong-ying

    2005-01-01

    Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.

  9. Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

    Institute of Scientific and Technical Information of China (English)

    陈瑜; 张起森

    2004-01-01

    Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.

  10. Decoding-Accuracy-Based Sequential Dimensionality Reduction of Spatio-Temporal Neural Activities

    Science.gov (United States)

    Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

    Performance of a brain machine interface (BMI) critically depends on selection of input data because information embedded in the neural activities is highly redundant. In addition, properly selected input data with a reduced dimension leads to improvement of decoding generalization ability and decrease of computational efforts, both of which are significant advantages for the clinical applications. In the present paper, we propose an algorithm of sequential dimensionality reduction (SDR) that effectively extracts motor/sensory related spatio-temporal neural activities. The algorithm gradually reduces input data dimension by dropping neural data spatio-temporally so as not to undermine the decoding accuracy as far as possible. Support vector machine (SVM) was used as the decoder, and tone-induced neural activities in rat auditory cortices were decoded into the test tone frequencies. SDR reduced the input data dimension to a quarter and significantly improved the accuracy of decoding of novel data. Moreover, spatio-temporal neural activity patterns selected by SDR resulted in significantly higher accuracy than high spike rate patterns or conventionally used spatial patterns. These results suggest that the proposed algorithm can improve the generalization ability and decrease the computational effort of decoding.

  11. Neural network based visualization of collaborations in a citizen science project

    Science.gov (United States)

    Morais, Alessandra M. M.; Santos, Rafael D. C.; Raddick, M. Jordan

    2014-05-01

    Citizen science projects are those in which volunteers are asked to collaborate in scientific projects, usually by volunteering idle computer time for distributed data processing efforts or by actively labeling or classifying information - shapes of galaxies, whale sounds, historical records are all examples of citizen science projects in which users access a data collecting system to label or classify images and sounds. In order to be successful, a citizen science project must captivate users and keep them interested on the project and on the science behind it, increasing therefore the time the users spend collaborating with the project. Understanding behavior of citizen scientists and their interaction with the data collection systems may help increase the involvement of the users, categorize them accordingly to different parameters, facilitate their collaboration with the systems, design better user interfaces, and allow better planning and deployment of similar projects and systems. Users behavior can be actively monitored or derived from their interaction with the data collection systems. Records of the interactions can be analyzed using visualization techniques to identify patterns and outliers. In this paper we present some results on the visualization of more than 80 million interactions of almost 150 thousand users with the Galaxy Zoo I citizen science project. Visualization of the attributes extracted from their behaviors was done with a clustering neural network (the Self-Organizing Map) and a selection of icon- and pixel-based techniques. These techniques allows the visual identification of groups of similar behavior in several different ways.

  12. 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.

  13. Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

    Science.gov (United States)

    Broderick, Ron

    1997-01-01

    The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network

  14. A neural network based seafloor classification using acoustic backscatter

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.

    This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...

  15. Apple Grade Judgment Based on the Neural Network

    Institute of Scientific and Technical Information of China (English)

    BAO Xiao-an; LUO Zhuo-lin; ZHANG Rui-lin

    2004-01-01

    A processing method on the basis of the technology of computer visual and digital image was introduced. The improved LVQ (learning vector quantization) neural network algorithm applied in the process to identify the grade of apples was proved effective in experiment.

  16. Construction of Neural Networks that Do Not Have Critical Points Based on Hierarchical Structure

    Directory of Open Access Journals (Sweden)

    Tohru Nitta

    2013-10-01

    Full Text Available a critical point is a point at which the derivatives of an error function are all zero. It has been shown in the literature that critical points caused by the hierarchical structure of a real-valued neural network (NN can be local minima or saddle points, although most critical points caused by the hierarchical structure are saddle points in the case of complex-valued neural networks. Several studies have demonstrated that singularity of those kinds has a negative effect on learning dynamics in neural networks. As described in this paper, the decomposition of high-dimensional neural networks into low-dimensional neural networks equivalent to the original neural networks yields neural networks that have no critical point based on the hierarchical structure. Concretely, the following three cases are shown: (a A 2-2-2 real-valued NN is constructed from a 1-1-1 complex-valued NN. (b A 4-4-4 real-valued NN is constructed from a 1-1-1 quaternionic NN. (c A 2-2-2 complex-valued NN is constructed from a 1-1-1 quaternionic NN. Those NNs described above do not suffer from a negative effect by singular points during learning comparatively because they have no critical point based on a hierarchical structure.

  17. Pyroelectric energy harvesting using liquid-based switchable thermal interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Cha, G; Ju, YS

    2013-01-15

    The pyroelectric effect offers an intriguing solid-state approach for harvesting ambient thermal energy to power distributed networks of sensors and actuators that are remotely located or otherwise difficult to access. There have been, however, few device-level demonstrations due to challenges in converting spatial temperature gradients into temporal temperature oscillations necessary for pyroelectric energy harvesting. We demonstrate the feasibility of a device concept that uses liquid-based thermal interfaces for rapid switching of the thermal conductance between a pyroelectric material and a heat source/sink and can thereby deliver high output power density. Using a thin film of a pyroelectric co-polymer together with a macroscale mechanical actuator, we operate pyroelectric thermal energy harvesting cycles at frequencies close to 1 Hz. Film-level power densities as high as 110 mW/cm(3) were achieved, limited by slow heat diffusion across a glass substrate. When combined with a laterally interdigitated electrode array and a MEMS actuator, the present design offers an attractive option for compact high-power density thermal energy harvesters. (C) 2012 Elsevier B.V. All rights reserved.

  18. Interface-based enterprise and software architecture mapping

    Directory of Open Access Journals (Sweden)

    Aziz Ahmad Rais

    2016-04-01

    Full Text Available Information technology (IT becomes more and more complex because of various technologies, methodologies, techniques and practices. Even though the goal of all technologies, methodologies, practices and techniques is to facilitate construction, to simplify, and to increase the reusability of information systems, in practice integrating all these becomes a challenge. This challenge can be met by creating more abstract levels in the information systems in question. Higher-level abstraction simplifies different views of complex problems, but at the same time it generates a knock-on issue regarding how actually to implement such an abstract-level view, and/or how to map it back to the lower levels of abstraction. The goal of this article is to simplify the implementation of enterprise architecture and map it to software architecture using an interface-based analysis technique. In order to achieve this goal, service-oriented architecture (SOA, which is composed of multiple concepts, will be used. The concepts are flexible, so they can be applied in enterprise architecture as well as in software architecture.

  19. 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.

  20. Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS

    International Nuclear Information System (INIS)

    In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically, it is the component of this field parallel to the neuron’s local orientation, the so-called effective electric field, that can initiate neuronal stimulation. Deeper insights on the stimulation mechanisms can be acquired through extensive TMS modelling. Most models study simple representations of neurons with assumed geometries, whereas we embed realistic neural trajectories computed using tractography based on diffusion tensor images. This way of modelling ensures a more accurate spatial distribution of the effective electric field that is in addition patient and case specific. The case study of this paper focuses on the single pulse stimulation of the left primary motor cortex with a standard figure-of-eight coil. Including realistic neural geometry in the model demonstrates the strong and localized variations of the effective electric field between the tracts themselves and along them due to the interplay of factors such as the tract’s position and orientation in relation to the TMS coil, the neural trajectory and its course along the white and grey matter interface. Furthermore, the influence of changes in the coil orientation is studied. Investigating the impact of tissue anisotropy confirms that its contribution is not negligible. Moreover, assuming isotropic tissues lead to errors of the same size as rotating or tilting the coil with 10 degrees. In contrast, the model proves to be less sensitive towards the not well-known tissue conductivity values. (paper)

  1. Passivity of memristor-based BAM neural networks with different memductance and uncertain delays.

    Science.gov (United States)

    Anbuvithya, R; Mathiyalagan, K; Sakthivel, R; Prakash, P

    2016-08-01

    This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results. PMID:27468321

  2. Finite-time synchronization control of a class of memristor-based recurrent neural networks.

    Science.gov (United States)

    Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun

    2015-03-01

    This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method. PMID:25536233

  3. STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Zhang Cuiping; Yang Qingfo

    2003-01-01

    According to advantages of neural network and characteristics of operating procedures of engine, a new strategy is represented on the control of fuel injection and ignition timing of gasoline engine based on improved BP network algorithm. The optimum ignition advance angle and fuel injection pulse band of engine under different speed and load are tested for the samples training network, focusing on the study of the design method and procedure of BP neural network in engine injection and ignition control. The results show that artificial neural network technique can meet the requirement of engine injection and ignition control. The method is feasible for improving power performance, economy and emission performances of gasoline engine.

  4. The Monitoring of Red Tides Based on Modular Neural Networks Using Airborne Hyperspectral Remote Sensing

    Institute of Scientific and Technical Information of China (English)

    JI Guangrong; SUN Jie; ZHAO Wencang; ZHANG Hande

    2006-01-01

    This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction (LRC) is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm (AGA). The experiments showed that this method can monitor red tide rapidly and effectively.

  5. Tea classification based on artificial olfaction using bionic olfactory neural network

    OpenAIRE

    X. L. Yang; Fu, J.; Lou, Z G; L. Y. Wang; Li, G.; Freeman, Walter J III

    2006-01-01

    Based on the research on mechanism of biological olfactory system, we constructed a K-set, which is a novel bionic neural network. Founded on the groundwork of K0, KI and KII sets, the KIII set in the K-set hierarchy simulates the whole olfactory neural system. In contrast to the conventional artificial neural networks, the KIII set operates in nonconvergent 'chaotic' dynamical modes similar to the biological olfactory system. In this paper, an application of electronic nose-brain for tea cla...

  6. Building a Tax Predictive Model Based on the Cloud Neural Network

    Institute of Scientific and Technical Information of China (English)

    田永青; 李志; 朱仲英

    2003-01-01

    Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.

  7. Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi-spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.

  8. Correlation methods of base-level cycle based on wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The authors discussed the method of wavelet neural network (WNN) for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrelation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.

  9. A Framework for Effective User Interface Design for Web-Based Electronic Commerce Applications

    Directory of Open Access Journals (Sweden)

    Justyna Burns

    2001-01-01

    Full Text Available Efficient delivery of relevant product information is increasingly becoming the central basis of competition between firms. The interface design represents the central component for successful information delivery to consumers. However, interface design for web-based information systems is probably more an art than a science at this point in time. Much research is needed to understand properties of an effective interface for electronic commerce. This paper develops a framework identifying the relationship between user factors, the role of the user interface and overall system success for web-based electronic commerce. The paper argues that web-based systems for electronic commerce have some similar properties to decision support systems (DSS and adapts an established DSS framework to the electronic commerce domain. Based on a limited amount of research studying web browser interface design, the framework identifies areas of research needed and outlines possible relationships between consumer characteristics, interface design attributes and measures of overall system success.

  10. SSVEP based EEG Interface for Google Street View Navigation

    OpenAIRE

    Raza, Asim

    2012-01-01

    Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former conne...

  11. Interface design approach for system on chip based on configuration

    OpenAIRE

    Maalej, Issam; Gogniat, Guy; Abid, Mohamed; Philippe, Jean Luc

    2003-01-01

    Communication synthesis is an essential step in hardware/software co-synthesis: many embedded systems use automatic generation of interface for point to point communication or use external supports of communication as standard bus or micro network. In this paper, we address the problem of hardware – software interface design in codesign approach for real-time applications. We refer to the hardware component as hardware accelerator and the software component as processor. In our approach, the ...

  12. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  13. Instantaneous Gradient Based Dual Mode Feed-Forward Neural Network Blind Equalization Algorithm

    Directory of Open Access Journals (Sweden)

    Ying Xiao

    2013-01-01

    Full Text Available To further improve the performance of feed-forward neural network blind equalization based on Constant Modulus Algorithm (CMA cost function, an instantaneous gradient based dual mode between Modified Constant Modulus Algorithm (MCMA and Decision Directed (DD algorithm was proposed. The neural network weights change quantity of the adjacent iterative process is defined as instantaneous gradient. After the network converges, the weights of neural network to achieve a stable energy state and the instantaneous gradient would be zero. Therefore dual mode algorithm can be realized by criterion which set according to the instantaneous gradient. Computer simulation results show that the dual mode feed-forward neural network blind equalization algorithm proposed in this study improves the convergence rate and convergence precision effectively, at the same time, has good restart and tracking ability under channel burst interference condition.

  14. Multiscale approach for bone remodeling simulation based on finite element and neural network computation

    CERN Document Server

    Hambli, Ridha

    2011-01-01

    The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate bone remodelling process. Because whole bone simulation considering the 3D trabecular level is time consuming, the finite element calculation is performed at macroscopic level and a trained neural network are employed as numerical devices for substituting the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at macroscopic scale depending on the morphological organization at the mesoscopic computed by the trained neural network. The digital image-based modeling technique using m-CT and voxel finite element mesh is used to capture 2 mm3 Representative Volume Elements at mesoscale level in a femur head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied str...

  15. The optimum design of the pressure control spring of the relief valve based on neural networks

    Institute of Scientific and Technical Information of China (English)

    FU Xiao-jin

    2006-01-01

    Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.

  16. Results from a MA16-based neural trigger in an experiment looking for beauty

    International Nuclear Information System (INIS)

    Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel's analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 μs for a Fisher discriminant (1-layer net) and of 6 μs for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.)

  17. Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

    Science.gov (United States)

    Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

    2015-12-01

    Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational

  18. Technique of information hiding based on neural network

    Science.gov (United States)

    Xu, Li; Tao, Gu

    2007-04-01

    A neural network algorithm is proposed which can conceal different files effectively such as *.exe, *.com, *.doc, *.txt and self-defined file formats. First, the important contents of the file are coded into a binary array. The total number of 0s and 1s is N. Second, to make the neural network learn the sample space, N pixel values and their closely relevant pixel values are randomly chosen from a color BMP format image and used to train the neural network, thus we get a group of network parameters and its outputs Y1. Each element of Y1 is increased by 0 or 1 according to the zeros and ones from the array, the increased Y1is called Y2. Third, using the transmitted parameters, a receiver can restore the neural network. Network outputs Y3(Y1) can also be obtained by simulating the restored neural network with the seed pixel values. Finally, the encrypted information can be decoded by Y2 and Y3. The acquisition of parameters and Y1 is different when the neural network is trained each time, so the algorithm has the characteristic of a one-time pad, which can enhance the correspondence security. Because the network colligates the chosen pixel values and their closely relevant pixel values, a cryptanalyst can not restore the network parameters and Y3 easily. Without the seed picture and the password, he does not know the encrypted data even if he knows the network parameters and Y2. If he only has the seed picture, he does not know the encrypted contents either, because there is no other information in the picture, which just is used to train the network. Using the same algorithm, the fragile watermark can be embedded into Y1 simultaneously. Besides telling you whether Y2 or network parameters have been tampered with, the fragile watermark could as well, reflecting the distortion status in the spatial domain of the tampered image. Therefore, the proposed method is of significance in practice.

  19. NOISE IDENTIFICATION FOR HYDRAULIC AXIAL PISTON PUMP BASED ON ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.

  20. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Science.gov (United States)

    Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim

    2009-12-01

    An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  1. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  2. Research on Spatial Estimation of Soil Property Based on Improved RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Jianbo Xu

    2013-01-01

    Full Text Available To seek optimal network parameters of Radial Basis Function (RBF Neural Network and improve the accuracy of this method on estimation of soil property space, this study utilizes genetic algorithm to optimize three network parameters of RBF Neural Network including the number of hidden layer nodes, expansion speed and root-mean-square error. Then, based on optimized RBF Neural Network, spatial interpolation is conducted for arable soil property under different sampling scales in the study area. The estimation result is superior to RBF Neural Network method without optimization and geostatistical method in terms of the fitting capacity and interpolation accuracy. Compared with the result of space estimation by RBF Neural Network method without optimization, among the 5 schemes, the forecast errors of RBF Neural Network optimized by genetic algorithm reduce greatly. Mean absolute error (MAE reduces 0.4868 on the average and root-mean-square error (RMSE reduces 1.492 on the average. Therefore, RBF Neural Network method optimized by genetic algorithm can gain the information about regional soil property spatial variation more accurately and provides technical support for arable land quality evaluation, accurate farmland management and rational application of fertilizer.

  3. A case study to estimate costs using Neural Networks and regression based models

    OpenAIRE

    Nadia Bhuiyan; Adil Salam; Fantahun M. Defersha

    2012-01-01

    Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be con...

  4. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    Energy Technology Data Exchange (ETDEWEB)

    Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration

    2015-07-01

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  5. ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

    OpenAIRE

    Eva Volna; Martin Kotyrba; Hashim Habiballa

    2015-01-01

    The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experim...

  6. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    International Nuclear Information System (INIS)

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  7. System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit

    OpenAIRE

    Shi Qiang Liu; Rong Zhu

    2016-01-01

    Errors compensation of micromachined-inertial-measurement-units (MIMU) is essential in practical applications. This paper presents a new compensation method using a neural-network-based identification for MIMU, which capably solves the universal problems of cross-coupling, misalignment, eccentricity, and other deterministic errors existing in a three-dimensional integrated system. Using a neural network to model a complex multivariate and nonlinear coupling system, the errors could be readily...

  8. The Influence of Perceptual and Knowledge-based Familiarity on the Neural Substrates of Face Perception

    OpenAIRE

    J. Cloutier; Kelley, W. M.; Heatherton, T. F.

    2010-01-01

    This study examined the neural substrates of facial familiarity and person-knowledge. Based on current neural models of face perception, it was hypothesized that distinct extended networks of brain regions differentiate the perception of (a) novel faces, (b) novel faces associated with person-knowledge, (c) perceptually familiar faces and (d) familiar faces for which person-knowledge was learned. To test this hypothesis, we conducted an event-related fMRI experiment during which participants ...

  9. Projective synchronization of fractional-order memristor-based neural networks.

    Science.gov (United States)

    Bao, Hai-Bo; Cao, Jin-De

    2015-03-01

    This paper investigates the projective synchronization of fractional-order memristor-based neural networks. Sufficient conditions are derived in the sense of Caputo's fractional derivation and by combining a fractional-order differential inequality. Two numerical examples are given to show the effectiveness of the main results. The results in this paper extend and improve some previous works on the synchronization of fractional-order neural networks. PMID:25463390

  10. Configuration space control of a parallel delta robot with a neural network based inverse kinematics

    OpenAIRE

    Uzunovic, Tarik; Golubovic, Edin; Baran, Eray Abdurrahman; Şabanoviç, Asif; SABANOVIC, Asif

    2013-01-01

    This paper describes configuration space control of a Delta robot with a neural network based kinematics. Mathematical model of the kinematics for parallel Delta robot used for manipulation purposes in microfactory was validated, and experiments showed that this model is not describing “real” kinematics properly. Therefore a new solution for kinematics mapping had to be investigated. Solution was found in neural network utilization, and it was used to model robot's inverse kinematics. It show...

  11. Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

    OpenAIRE

    J. C. Ochoa-Rivera; R. García-Bartual; Andreu, J.

    2002-01-01

    A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generatio...

  12. Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

    OpenAIRE

    J. C. Ochoa-Rivera; R. García-Bartual; Andreu, J.

    2002-01-01

    A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for s...

  13. Indirect model for roughness in rough honing processes based on artificial neural networks

    OpenAIRE

    Sivatte Adroer, Mauricio; Llanas Parra, Francesc Xavier; Buj Corral, Irene; Vivancos Calvet, Joan

    2016-01-01

    In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were test...

  14. Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve

    Directory of Open Access Journals (Sweden)

    D. Novák

    2004-01-01

    Full Text Available A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure. 

  15. Towards a neural network based therapy for hallucinatory disorders.

    Science.gov (United States)

    Peláez, J R

    2000-01-01

    Pattern completion in a neural network model of the thalamus and a biologically plausible model of synaptic plasticity are the key concepts used in this paper for analyzing some cognitive disorders that involve hallucinations of several kinds: visual hallucinations in the Charles Bonnet syndrome and psychedelic drugs consumption, somatic hallucination in phantom limbs, cognitive hallucinations in schizophrenia and even in multiple personality disorders. All these types of hallucinations are proposed to be the result of a pattern completion dynamics performed in thalamic deafferented areas. Effective treatments of some of these disorders involve peripheral stimulation jointly with a central inhibition so that the neural circuits generating the disorders are depressed according to the proposed model of synaptic plasticity. PMID:11156194

  16. Neural network based PWM AC chopper fed induction motor drive

    Directory of Open Access Journals (Sweden)

    Venkatesan Jamuna

    2009-01-01

    Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .

  17. Risk Assessment Algorithms Based On Recursive Neural Networks

    CERN Document Server

    De Lara, Alejandro Chinea Manrique

    2007-01-01

    The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning t...

  18. MODEL OF CASE-BASED NEURAL NETWORK%基于范例的神经网络模型

    Institute of Scientific and Technical Information of China (English)

    艾景军; 李俊生

    2004-01-01

    In order to improve generalization capability of neural networks, a model structure of Case-Based neural networks has been presented. The model blended Case-Based Reasoning method into neural networks and has the ability of incrementally learning. The results demonstrated that the model could observably improve the generalization capability of supervised neural networks. Firstly, paper summarized the advancing front of researching on generalization capability of neural networks.Secondly, the structure of CBNN and its process of working were introduced. Finally, the results of experiments were compared and discussed.

  19. Neural Network Based Forecasting of Foreign Currency Exchange Rates

    OpenAIRE

    S. Kumar Chandar; Sumathi, Dr. M.; Dr S. N. Sivanandam

    2014-01-01

    The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other...

  20. Hyaluronic acid-based scaffold for central neural tissue engineering

    OpenAIRE

    Wang, Xiumei; He, Jin; Wang, Ying; CUI, FU-ZHAI

    2012-01-01

    Central nervous system (CNS) regeneration with central neuronal connections and restoration of synaptic connections has been a long-standing worldwide problem and, to date, no effective clinical therapies are widely accepted for CNS injuries. The limited regenerative capacity of the CNS results from the growth-inhibitory environment that impedes the regrowth of axons. Central neural tissue engineering has attracted extensive attention from multi-disciplinary scientists in recent years, and ma...

  1. Neural Network AE Source Location Based on Extracted Signal Features

    Czech Academy of Sciences Publication Activity Database

    Chlada, Milan; Blaháček, Michal; Převorovský, Zdeněk

    Brno : VUT Brno, 2005 - (Mazal, P.), s. 55-62 ISBN 80-214-2996-8. [NDT in Progress. Praha (CZ), 10.10.2005-12.10.2005] R&D Projects: GA ČR(CZ) GA201/04/2102; GA MPO FT-TA/026 Institutional research plan: CEZ:AV0Z20760514 Keywords : AE source location * neural network s * signal features Subject RIV: BI - Acoustics

  2. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  3. Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

    OpenAIRE

    Sher, Gene I.

    2011-01-01

    Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the applicati...

  4. Optogenetics: illuminating the neural bases of rodent behavior

    OpenAIRE

    Lobo, Mary Kay

    2014-01-01

    T Chase Francis,1 Dipesh Chaudhury,2 Mary Kay Lobo1 1Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA; 2Department of Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates Abstract: In vivo optogenetics has provided researchers with the ability to delve deeper into the neural basis of behavior by driving cell-type specific circuit connections within and between brain regions. The diverse toolbox available for circuit- and...

  5. Dynamic Reputation Based Trust Management Using Neural Network Approach

    OpenAIRE

    Reza Azmi; Mahdieh Hakimi; Zahra Bahmani

    2011-01-01

    Multi-agent systems like Peer-to-Peer (P2P) Networks employ scalable mechanisms that allow anyone to offer content and services to other system users. The open accessibility of these networks makes them vulnerable to malicious users wishing to poison the system. This paper proposed a novel trust and reputation system, using RBF artificial neural network to determine trust level and mitigate the number of unreliable downloads.

  6. Neural bases of selective attention in action video game players

    OpenAIRE

    Bavelier, D.; Achtman, RL; M Mani; Föcker, J

    2011-01-01

    Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention. Yet little is known about the neural mechanisms that mediate such attentional benefits. A review of the aspects of attention enhanced in action game players suggests there are changes in the mechanisms that control attention allocation and its efficiency (Hubert-Wallander et al., 2010). The present study used brain imaging to test this hypothes...

  7. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  8. The neural bases of key competencies of emotional intelligence

    OpenAIRE

    Krueger, Frank; Aron K Barbey; McCabe, Kevin; Strenziok, Maren; Zamboni, Giovanna; Solomon, Jeffrey; Raymont, Vanessa; Grafman, Jordan

    2009-01-01

    Emotional intelligence (EI) refers to a set of competencies that are essential features of human social life. Although the neural substrates of EI are virtually unknown, it is well established that the prefrontal cortex (PFC) plays a crucial role in human social-emotional behavior. We studied a unique sample of combat veterans from the Vietnam Head Injury Study, which is a prospective, long-term follow-up study of veterans with focal penetrating head injuries. We administered the Mayer-Salove...

  9. Artificial Neural Networks for SCADA Data based Load Reconstruction (poster)

    OpenAIRE

    Hofemann, C.; Van Bussel, G.J.W.; Veldkamp, H.

    2011-01-01

    If at least one reference wind turbine is available, which provides sufficient information about the wind turbine loads, the loads acting on the neighbouring wind turbines can be predicted via an artificial neural network (ANN). This research explores the possibilities to apply such a network not only within a wind park but on turbines located at different sites. Following the idea to develop a tool to forecast the particular loads of any wind turbine in the field without the need to install ...

  10. A NOVEL INTRUSION DETECTION MODE BASED ON UNDERSTANDABLE NEURAL NETWORK TREES

    Institute of Scientific and Technical Information of China (English)

    Xu Qinzhen; Yang Luxi; Zhao Qiangfu; He Zhenya

    2006-01-01

    Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network,statistical techniques and expert systems are used to model network packets in the field of intrusion detection.In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is presented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN's capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually "gray boxes" as they can be interpreted easily ifthe number of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset.We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.

  11. Expert music performance: cognitive, neural, and developmental bases.

    Science.gov (United States)

    Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

    2015-01-01

    In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. PMID:25725910

  12. Neural Network Based Lna Design for Mobile Satellite Receiver

    Directory of Open Access Journals (Sweden)

    Abhijeet Upadhya

    2014-08-01

    Full Text Available Paper presents a Neural Network Modelling approach to microwave LNA design. To acknowledge the specifications of the amplifier, Mobile Satellite Systems are analyzed. Scattering parameters of the LNA in the frequency range 0.5 to 18 GHz are calculated using a Multilayer Perceptron Artificial Neural Network model and corresponding smith charts and polar charts are plotted as output to the model. From these plots, the microwave scattering parameter description of the LNA are obtained. Model is efficiently trained using Agilent ATF 331M4 InGaAs/InP Low Noise pHEMT amplifier datasheet and the neural model’s output seem to follow the various device characteristic curves with high regression. Next, Maximum Allowable Gain and Noise figure of the device are modelled and plotted for the same frequency range. Finally, the optimized model is utilized as an interpolator and the resolution of the amplifying capability with noise characteristics are obtained for the L Band of MSS operation.

  13. Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    Hongyu Sun; Yang Xiang; Mingdao Yang

    2011-01-01

    The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment (P < 0.01), which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

  14. Touch interface for markless AR based on Kinect

    Science.gov (United States)

    Hsieh, Ching-Tang; Kuo, Tai-Ku; Wang, Hui-Chun; Wu, Yeh-Kuang; Chang, Liung-Chun

    2014-01-01

    We develop an augmented reality (AR) environment with hidden-marker via touch interface using Kinect device, and then also set up a touch painting game with the AR environment. This environment is similar to that of the touch screen interface which allows user to paint picture on a tabletop with his fingers, and it is designed with depth image information from Kinect device setting up above a tabletop. We incorporate support vector machine (SVM) to classify painted pictures which correspond to the inner data and call out its AR into the tabletop in color images information from Kinect device. Because users can utilize this similar touch interface to control AR, we achieve a marker-less AR and interactive environment.

  15. Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency Of Classification In Handwritten Digit Datasets

    Directory of Open Access Journals (Sweden)

    Neera Saxena

    2011-07-01

    Full Text Available This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance the accuracy of the system, along the experimental results. The method offers lesser computational preprocessing in comparison to other ensemble techniques as it ex-preempts feature extraction process before feeding the data into base classifiers. This is achieved by the basic nature of neo-cognitron, it is a multilayer feed-forward neural network. Ensemble of such base classifiers gives class labels for each pattern that in turn is combined to give the final class label for that pattern. The purpose of this paper is not only to exemplify learning behaviour of neo-cognitron as base classifiers, but also to purport better fashion to combine neural network based ensemble classifiers.

  16. Dissolved oxygen prediction using a possibility theory based fuzzy neural network

    Science.gov (United States)

    Khan, Usman T.; Valeo, Caterina

    2016-06-01

    A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.

  17. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

    Science.gov (United States)

    Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

    2015-11-01

    This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. PMID:26506019

  18. Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

    Institute of Scientific and Technical Information of China (English)

    LI Zhe-min; CUI Li-guo; XU Shi-wei; WENG Ling-yun; DONG Xiao-xia; LI Gan-qiong; YU Hai-peng

    2013-01-01

    This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China. In the process of determining the structure of the chaotic neural network, the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension, and then the number of hidden layer nodes is estimated by trial and error. Finally, this model is applied to predict the retail prices of eggs and compared with ARIMA. The result shows that the chaotic neural network has better nonlinear iftting ability and higher precision in the prediction of weekly retail price of eggs. The empirical result also shows that the chaotic neural network can be widely used in the ifeld of short-term prediction of agricultural prices.

  19. A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor

    Institute of Scientific and Technical Information of China (English)

    Along Yu; Zheng Li

    2006-01-01

    In this paper, we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method,the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line scaling and high precision. The maximum nonlinearity error can be reduced to 0.037% using GNN. However, the maximum nonlinearity error is 0.075% using least square method (LMS).

  20. Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

    Institute of Scientific and Technical Information of China (English)

    Qi Zhidong; Zhu Xinjian; Cao Guangyi

    2006-01-01

    Aiming at on-line controlling of Direct Methanol Fuel Cell (DMFC) stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System (ANFIS) identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative (PID) and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.

  1. An optical nanofiber-based interface for single molecules

    CERN Document Server

    Skoff, Sarah M; Schauffert, Hardy; Rauschenbeutel, Arno

    2016-01-01

    Optical interfaces for quantum emitters are a prerequisite for implementing quantum networks. Here, we couple single molecules to the guided modes of an optical nanofiber. The molecules are embedded within a crystal that provides photostability and due to its inhomogeneous environment, a means to spectrally address single molecules. Single molecules are excited and detected solely via the nanofiber interface without the requirement of additional optical access. In this way, we realize a fully fiber-integrated system that is scalable and may become a versatile constituent for quantum hybrid systems.

  2. Sales Forecasting Based on ERP System through Delphi, fuzzy Clustering and Back-Propagation Neural Networks with adaptive learning rate

    Directory of Open Access Journals (Sweden)

    Attariuas Hicham

    2012-11-01

    Full Text Available In recent years, there has been a strong tendency by companies to use centralized management systems like Enterprise resource planning (ERP. ERP systems offer a comprehensive and simplified process managements and extensive functional coverage. Sales management module is an important element business management of ERP. This paper describes an intelligent hybrid sales forecasting system ERP-FCBPN sales forecast based on architecture of ERP through Delphi, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate (FCBPN. The proposed approach is composed of three stages: (1 Stage of data collection: Data collection will be implemented from the fields (attributes existing at the interfaces (Tables the database of the ERP. Collection of Key factors that influence sales be made using the Delphi method; (2 Stage of Data preprocessing: Winter Exponential Smoothing method will be utilized to take the trend effect into consideration. (3 Stage of learning by FCBPN: We use hybrid sales forecasting system based on Delphi, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate (FCBPN. The data for this study come from an industrial company that manufactures packaging. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising solution for industrial forecasting.

  3. Research of Digital Interface Layout Design based on Eye-tracking

    Directory of Open Access Journals (Sweden)

    Shao Jiang

    2015-01-01

    Full Text Available The aim of this paper is to improve the low service efficiency and unsmooth human-computer interaction caused by currently irrational layouts of digital interfaces for complex systems. Also, three common layout structures for digital interfaces are to be presented and five layout types appropriate for multilevel digital interfaces are to be summarized. Based on the eye tracking technology, an assessment was conducted in advantages and disadvantages of different layout types through subjects’ search efficiency. Based on data and results, this study constructed a matching model which is appropriate for multilevel digital interface layout and verified the fact that the task element is a significant and important aspect of layout design. A scientific experimental model of research on digital interfaces for complex systems is provided. Both data and conclusions of the eye movement experiment provide a reference for layout designs of interfaces for complex systems with different task characteristics.

  4. Interface Agent for Computer-based Tutoring Systems.

    Science.gov (United States)

    Dang, Trang; Ghenniwa, Hamada; Kamel, Mohamed

    1999-01-01

    Proposes an interface agent for intelligent tutoring systems that creates a collaborative learning environment between the learner and the tutoring software. Describes implementation of a prototype using the IBM Agent Builder Environment Toolkit to use with an intelligent tutoring system for algebra and considers benefits in a lifelong learning…

  5. The neural bases underlying social risk perception in purchase decisions.

    Science.gov (United States)

    Yokoyama, Ryoichi; Nozawa, Takayuki; Sugiura, Motoaki; Yomogida, Yukihito; Takeuchi, Hikaru; Akimoto, Yoritaka; Shibuya, Satoru; Kawashima, Ryuta

    2014-05-01

    Social considerations significantly influence daily purchase decisions, and the perception of social risk (i.e., the anticipated disapproval of others) is crucial in dissuading consumers from making purchases. However, the neural basis for consumers' perception of social risk remains undiscovered, and this novel study clarifies the relevant neural processes. A total of 26 volunteers were scanned while they evaluated purchase intention of products (purchase intention task) and their anticipation of others' disapproval for possessing a product (social risk task), using functional magnetic resonance imaging (fMRI). The fMRI data from the purchase intention task was used to identify the brain region associated with perception of social risk during purchase decision making by using subjective social risk ratings for a parametric modulation analysis. Furthermore, we aimed to explore if there was a difference between participants' purchase decisions and their explicit evaluations of social risk, with reference to the neural activity associated with social risk perception. For this, subjective social risk ratings were used for a parametric modulation analysis on fMRI data from the social risk task. Analysis of the purchase intention task revealed a significant positive correlation between ratings of social risk and activity in the anterior insula, an area of the brain that is known as part of the emotion-related network. Analysis of the social risk task revealed a significant positive correlation between ratings of social risk and activity in the temporal parietal junction and the medial prefrontal cortex, which are known as theory-of-mind regions. Our results suggest that the anterior insula processes consumers' social risk implicitly to prompt consumers not to buy socially unacceptable products, whereas ToM-related regions process such risk explicitly in considering the anticipated disapproval of others. These findings may prove helpful in understanding the mental

  6. Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

    Science.gov (United States)

    Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Cassady, K.; Kofman, I. S.; De Dios, Y. E.; Szecsy, D. L.; Riascos-Castaneda, R. F.; Wood, S. J.; Bloomberg, J. J.

    2016-01-01

    We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. Our preliminary findings document brain structural volumetric increases, primarily restricted to sensorimotor regions. Eventual comparison to our bed rest study results will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe as we accumulate more data. In this presentation I will discuss our progress to date with the flight study

  7. Standard Cell-Based Implementation of a Digital Optoelectronic Neural-Network Hardware

    Science.gov (United States)

    Maier, Klaus D.; Beckstein, Clemens; Blickhan, Reinhard; Erhard, Werner

    2001-03-01

    A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.

  8. Neural network based semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear......-displacement trajectories. The proposed neural network controller is therefore trained based on data derived from these desired forcedisplacement curves, where the optimal relation between friction force level and response amplitude is determined explicitly by simply maximizing the damping ratio of the targeted vibration...... mode of the structure. The neural network control is then developed to reproduce the desired force based on damper displacement and velocity as network input, and it is therefore referred to as an amplitude dependent model reference control method. An inverse model of the MR damper is needed to...

  9. Research of Digital Interface Layout Design based on Eye-tracking

    OpenAIRE

    Shao Jiang; Xue Chengqi; Wang Fang; Wang Haiyan; Tang Wencheng; Chen Mo; Kang Mingwu

    2015-01-01

    The aim of this paper is to improve the low service efficiency and unsmooth human-computer interaction caused by currently irrational layouts of digital interfaces for complex systems. Also, three common layout structures for digital interfaces are to be presented and five layout types appropriate for multilevel digital interfaces are to be summarized. Based on the eye tracking technology, an assessment was conducted in advantages and disadvantages of different layout types through subjects’ ...

  10. Glotaran: A Java-Based Graphical User Interface for the R Package TIMP

    OpenAIRE

    Katharine M. Mullen; Ralf Seger; Laptenok, Sergey P; Snellenburg, Joris J.; van Stokkum, Ivo H. M.

    2012-01-01

    In this work the software application called Glotaran is introduced as a Java-based graphical user interface to the R package TIMP, a problem solving environment for fitting superposition models to multi-dimensional data. TIMP uses a command-line user interface for the interaction with data, the specification of models and viewing of analysis results. Instead, Glotaran provides a graphical user interface which features interactive and dynamic data inspection, easier -- assisted by the user in...

  11. A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Wang Lunwen; Zhang Ling

    2007-01-01

    In this letter, Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies, fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore, "Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3 × 107 data points), and knowledge is found effectively.

  12. Neural Network Based Boolean Factor Analysis of Parliament Voting

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Polyakov, P.Y.; Húsek, Dušan; Řezanková, H.

    Heidelberg : Springer, 2006 - (Rizzi, A.; Vichi, M.), s. 861-868 ISBN 3-7908-1708-2. [COMPSTAT 2006. Symposium /17./. Rome (IN), 28.08.2006-01.09.2006] R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Grant ostatní: RFBR(RU) 05-07-90049 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural networks * social networks Subject RIV: BB - Applied Statistics, Operational Research

  13. Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

    Science.gov (United States)

    Hasan, M. A.; Reaz, M. B. I.

    2012-01-01

    The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

  14. Wavelet Neural Network Based Traffic Prediction for Next Generation Network

    Institute of Scientific and Technical Information of China (English)

    Zhao Qigang; Li Qunzhan; He Zhengyou

    2005-01-01

    By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.

  15. Product Assembly Cost Estimation Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.

  16. Las bases neurales y los qualia de la conciencia

    OpenAIRE

    Fernando Maureira; Daniel Serey

    2011-01-01

    Desde el punto de vista científico, el estudio de la conciencia es un fenómeno relativamente nuevo. Esta área de investigación debe seguir tres pasos relacionados con: descubrir los eventos neurales que generan la conciencia, comprobar la correlación entre ambos y desarrollar una teoría. La biología actual aún se encuentra en el primer paso, destacando los aportes de investigadores como Crick, Koch, Edelman, Tonini, Bartels, Zeki, Thompson, Varela, Llinás, etc. Sin embargo, el problema más co...

  17. Electronic structure of hybrid interfaces for polymer-based electronics

    International Nuclear Information System (INIS)

    The fundamentals of the energy level alignment at anode and cathode electrodes in organic electronics are described. We focus on two different models that treat weakly interacting organic/metal (and organic/organic) interfaces: the induced density of interfacial states model and the so-called integer charge transfer model. The two models are compared and evaluated, mainly using photoelectron spectroscopy data of the energy level alignment of conjugated polymers and molecules at various organic/metal and organic/organic interfaces. We show that two different alignment regimes are generally observed: (i) vacuum level alignment, which corresponds to the lack of vacuum level offsets (Schottky-Mott limit) and hence the lack of charge transfer across the interface, and (ii) Fermi level pinning where the resulting work function of an organic/metal and organic/organic bilayer is independent of the substrate work function and an interface dipole is formed due to charge transfer across the interface. We argue that the experimental results are best described by the integer charge transfer model which predicts the vacuum level alignment when the substrate work function is above the positive charge transfer level and below the negative charge transfer level of the conjugated material. The model further predicts Fermi level pinning to the positive (negative) charge transfer level when the substrate work function is below (above) the positive (negative) charge transfer level. The nature of the integer charge transfer levels depend on the materials system: for conjugated large molecules and polymers, the integer charge transfer states are polarons or bipolarons; for small molecules' highest occupied and lowest unoccupied molecular orbitals and for crystalline systems, the relevant levels are the valence and conduction band edges. Finally, limits and further improvements to the integer charge transfer model are discussed as well as the impact on device design. (topical review)

  18. Neural network based daily precipitation generator (NNGEN-P)

    Energy Technology Data Exchange (ETDEWEB)

    Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

    2007-02-15

    Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

  19. Automatic localization of vertebrae based on convolutional neural networks

    Science.gov (United States)

    Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie

    2015-03-01

    Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.

  20. Using Artificial Neural Networks for Energy Regulation Based Variable-speed Electrohydraulic Drive

    Institute of Scientific and Technical Information of China (English)

    XU Ming; JIN Bo; YU Yaxin; SHEN Haikuo; LI Wei

    2010-01-01

    In the energy regulation based varibable-speed electrohydraulic drive system, the supply energy and the demanded energy, which will affect the control performance greatly, are crucial. However, they are hard to be obtained via conventional methods for some reasons. This paper tries to a new route: the definitive numerical values of the supply energy and the demanded energy are not required, except for their relationship which is called energy state. A three-layer back propagation(BP) neural network was built up to act as an energy analysis unit to deduce the energy state. The neural network has three inputs: the reference displacement, the actual displacement of cylinder rod and the system flowrate supply. The output of the neural network is energy state. A Chebyshev type II filter was designed to calculate the cylinder speed for the estimation of system flowrate supply. The training and testing samples of neural network were collected by the system accurate simulation model. After off-line training, the neural network was tested by the testing data. And the testing result demonstrates that the designed neural network was successful. Then, the neural network acts as the energy analysis unit in real-time experiments of cylinder position control, where it works efficiently under square-wave and sine-wave reference displacement. The experimental results validate its feasibility and adaptability. Only a position sensor and some pressure sensors, which are cheap and have quick dynamic response, are necessary for the system control. And the neural network plays the role of identifying the energy state.