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.

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

Science.gov (United States)

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

2018-06-01

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

Neurosecurity: security and privacy for neural devices.

Science.gov (United States)

Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

2009-07-01

An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

A review of organic and inorganic biomaterials for neural interfaces.

Science.gov (United States)

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

2014-03-26

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

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

Science.gov (United States)

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

2013-01-01

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

Neurophysiology and neural engineering: a review.

Science.gov (United States)

Prochazka, Arthur

2017-08-01

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

Development of regenerative peripheral nerve interfaces for motor control of neuroprosthetic devices

Science.gov (United States)

Kemp, Stephen W. P.; Urbanchek, Melanie G.; Irwin, Zachary T.; Chestek, Cynthia A.; Cederna, Paul S.

2017-05-01

Traumatic peripheral nerve injuries suffered during amputation commonly results in debilitating neuropathic pain in the affected limb. Modern prosthetic technologies allow for intuitive, simultaneous control of multiple degrees of freedom. However, these state-of-the-art devices require separate, independent control signals for each degree of freedom, which is currently not possible. As a result, amputees reject up to 75% of myoelectric devices preferring instead to use body-powered artificial limbs which offer subtle sensory feedback. Without meaningful and intuitive sensory feedback, even the most advanced myoelectric prostheses remain insensate, burdensome, and are associated with enormous cognitive demand and mental fatigue. The ideal prosthetic device is one which is capable of providing intuitive somatosensory feedback essential for interaction with the environment. Critical to the design of such a bioprosthetic device is the development of a reliable biologic interface between human and machine. This ideal patient-prosthetic interface allows for transmission of both afferent somatosensory information and efferent motor signals for a closed-loop feedback system of neural control. Our lab has developed the Regenerative Peripheral Nerve Interface (RPNI) as a biologic nerve interface designed for stable integration of a prosthetic device with transected peripheral nerves in a residual limb. The RPNI is constructed by surgically implanting the distal end of a transected peripheral nerve into an autogenous muscle graft. Animal experiments in our lab have shown recording of motor signals from RPNI's implanted into both rodents and monkeys. Here, we achieve high amplitude EMG signals with a high signal to noise (SNR) ratio.

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

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

Science.gov (United States)

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

2014-12-01

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

Tactile Feedback for Above-Device Gesture Interfaces

OpenAIRE

Freeman, Euan; Brewster, Stephen; Lantz, Vuokko

2014-01-01

Above-device gesture interfaces let people interact in the space above mobile devices using hand and finger movements. For example, users could gesture over a mobile phone or wearable without having to use the touchscreen. We look at how above-device interfaces can also give feedback in the space over the device. Recent haptic and wearable technologies give new ways to provide tactile feedback while gesturing, letting touchless gesture interfaces give touch feedback. In this paper we take a f...

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

A User Interface Toolkit for a Small Screen Device.

OpenAIRE

UOTILA, ALEKSI

2000-01-01

The appearance of different kinds of networked mobile devices and network appliances creates special requirements for user interfaces that are not met by existing widget based user interface creation toolkits. This thesis studies the problem domain of user interface creation toolkits for portable network connected devices. The portable nature of these devices places great restrictions on the user interface capabilities. One main characteristic of the devices is that they have small screens co...

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

NARCIS (Netherlands)

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

2009-01-01

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

Poled-glass devices: Influence of surfaces and interfaces

DEFF Research Database (Denmark)

Fage-Pedersen, Jacob; Jacobsen, Rune Shim; Kristensen, Martin

2007-01-01

Devices in periodically poled glass must have a large periodic variation of the built-in field. We show that the periodic variation can be severely degraded by charge dynamics taking place at the external (glass–air) interface or at internal (glass–glass) interfaces if the interfaces have...... the device, one can reveal the existence of imperfect interfaces by use of electric field induced second-harmonic generation....

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

Science.gov (United States)

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

2018-02-01

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

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.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

The conversational interface talking to smart devices

CERN Document Server

McTear, Michael; Griol, David

2016-01-01

This book provides a comprehensive introduction to the conversational interface, which is becoming the main mode of interaction with virtual personal assistants, smart devices, various types of wearables, and social robots. The book consists of four parts: Part I presents the background to conversational interfaces, examining past and present work on spoken language interaction with computers; Part II covers the various technologies that are required to build a conversational interface along with practical chapters and exercises using open source tools; Part III looks at interactions with smart devices, wearables, and robots, and then goes on to discusses the role of emotion and personality in the conversational interface; Part IV examines methods for evaluating conversational interfaces and discusses future directions. · Presents a comprehensive overview of the various technologies that underlie conversational user interfaces; · Combines descriptions of conversational user interface technologies with a gui...

Natural user interfaces for multitouch devices

OpenAIRE

Bukovinski, Matej

2010-01-01

This thesis presents a new class of user interfaces, which a commonly referred to as natural user interfaces. It discusses their main characteristics, evolution and advantages over currently dominant graphical user interfaces. Special attention is devoted to the subgroup of natural user interfaces for multitouch devices. Multitouch technology is firstly presented from a technical point of view and afterwards also in practice in form of a comparative study of six popular multitouch platfo...

A multi-purpose brain-computer interface output device.

Science.gov (United States)

Thompson, David E; Huggins, Jane E

2011-10-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.

A Multi-purpose Brain-Computer Interface Output Device

Science.gov (United States)

Thompson, David E; Huggins, Jane E

2012-01-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as standalone communication and control systems, rather than as interfaces to existing systems built for these purposes. While an individual communication and control system may be powerful or flexible, no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCIs could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e. without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems. PMID:22208120

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

Science.gov (United States)

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

2018-05-01

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

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

Science.gov (United States)

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

2017-08-01

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

Chemically sensitive interfaces on SAW devices

Energy Technology Data Exchange (ETDEWEB)

Ricco, A.J.; Martin, S.J. [Sandia National Labs., Albuquerque, NM (United States); Crooks, R.M.; Xu, Chuanjing [Texas A and M Univ., College Station, TX (United States); Allred, R.E. [Adherent Technologies, Inc., Albuquerque, NM (United States)

1993-11-01

Using surface acoustic wave (SAW) devices, three approaches to the effective use of chemically sensitive interfaces that are not highly chemically selective have been examined: (1) molecular identification from time-resolved permeation transients; (2) using multifrequency SAW devices to determine the frequency dependence of analyte/film interactions; (3) use of an array of SAW devices bearing diverse chemically sensitive interfaces to produce a distinct response pattern for each analyte. In addition to their well-known sensitivity to mass changes (0.0035 monolayer of N{sub 2} can be measured), SAW devices respond to the mechanical and electronic properties of thin films, enhancing response information content but making a thorough understanding of the perturbation critical. Simultaneous measurement of changes in frequency and attenuation, which can provide the information necessary to determine the type of perturbation, are used as part of the above discrimination schemes.

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

Science.gov (United States)

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

2007-01-01

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

Network device interface for digitally interfacing data channels to a controller via a network

Science.gov (United States)

Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

2009-01-01

A communications system and method are provided for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. Network device interfaces associated with different data channels can coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

Energy level alignment at interfaces in organic photovoltaic devices

International Nuclear Information System (INIS)

Opitz, Andreas; Frisch, Johannes; Schlesinger, Raphael; Wilke, Andreas; Koch, Norbert

2013-01-01

Highlights: ► Energy level alignment is crucial for organic solar cell efficiency. ► Photoelectron spectroscopy can reliably determine energy levels of organic material interfaces. ► Care must be taken to avoid even subtle sample damage. -- Abstract: The alignment of energy levels at interfaces in organic photovoltaic devices is crucial for their energy conversion efficiency. Photoelectron spectroscopy (PES) is a well-established and widely used technique for determining the electronic structure of materials; at the same time PES measurements of conjugated organic materials often pose significant challenges, such as obtaining sufficiently defined sample structures and radiation-induced damage of the organic layers. Here we report how these challenges can be tackled to unravel the energy levels at interfaces in organic photovoltaic devices, i.e., electrode/organic and organic/organic interfaces. The electronic structure across entire photovoltaic multilayer devices can thus be reconciled. Finally, general considerations for correlating the electronic structure and the photovoltaic performance of devices will be discussed

Biofabrication to build the biology-device interface

International Nuclear Information System (INIS)

Liu Yi; Kim, Eunkyoung; Culver, James N; Bentley, William E; Payne, Gregory F; Ghodssi, Reza; Rubloff, Gary W

2010-01-01

The last century witnessed spectacular advances in both microelectronics and biotechnology yet there was little synergy between the two. A challenge to their integration is that biological and electronic systems are constructed using divergent fabrication paradigms. Biology fabricates bottom-up with labile components, while microelectronic devices are fabricated top-down using methods that are 'bio-incompatible'. Biofabrication-the use of biological materials and mechanisms for construction-offers the opportunity to span these fabrication paradigms by providing convergent approaches for building the bio-device interface. Integral to biofabrication are stimuli-responsive materials (e.g. film-forming polysaccharides) that allow directed assembly under near physiological conditions in response to device-imposed signals. Biomolecular engineering, through recombinant technology, allows biological components to be endowed with information for assembly (e.g. encoded in a protein's amino acid sequence). Finally, self-assembly and enzymatic assembly provide the mechanisms for construction over a hierarchy of length scales. Here, we review recent advances in the use of biofabrication to build the bio-device interface. We anticipate that the biofabrication toolbox will expand over the next decade as more researchers enlist the unique construction capabilities of biology. Further, we look forward to observing the application of this toolbox to create devices that can better diagnose disease, detect pathogens and discover drugs. Finally, we expect that biofabrication will enable the effective interfacing of biology with electronics to create implantable devices for personalized and regenerative medicine. (topical review)

The Necessary Death of the Block Device Interface

DEFF Research Database (Denmark)

Bjørling, Matias; Bonnet, Philippe; Bouganim, Luc

2013-01-01

that this option amounts to building on quicksand. We illustrate our point by debunking some popular myths about flash devices and by pointing out mistakes in the papers we have published throughout the years. The second option is to abandon the simple abstraction of the block device interface and reconsider how...... device interface. This is the mainstream option both in industry and academia. This leaves the storage and OS communities with the responsibility to deal with the complexity introduced by SSDs in the hope that they will provide us with a robust, yet simple, performance model. In this paper, we show...

Flexible neural interfaces with integrated stiffening shank

Energy Technology Data Exchange (ETDEWEB)

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

2017-10-17

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

Flexible neural interfaces with integrated stiffening shank

Science.gov (United States)

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

2016-07-26

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

THE EVOLUTION OF INTERFACE DESIGN ON MOBILE DEVICES

Directory of Open Access Journals (Sweden)

Atila Isik

2016-09-01

Full Text Available The most typical characteristic of 21. century is the light velocity changing of technology. Dizzily developments in the field of human-robot interaction, mobile communication and education as affect all the fields of life also have an impact on art and design area. Striking increase of interest had been seen for mobile devices which are product of developing technology as tablet and mobile phone. While this increase happening at the present time user interfaces at mobile world and user practices developed as much as dominating design tendency. And became primary impotance for visual designers. This study aims utulizing future options for designers and the oppurtunities and reach a conclusions with examining evolution field of interface design spesific to mobile devices. From Skemorfizm to ordinary design and Google’s recent transition of material design principles were discussed together in this study. And this workout deal with interface design logic of users for working with new computational arificial facts devices as wearable smart objects and devices which able to connect to internet.

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

Science.gov (United States)

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

2015-01-01

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

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.

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

Science.gov (United States)

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

2014-02-01

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

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

Science.gov (United States)

Guo, Liang

2016-01-01

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

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

Science.gov (United States)

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

2011-02-01

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

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

Science.gov (United States)

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

2015-10-01

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

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.

Modality-Specific Axonal Regeneration: Towards selective regenerative neural interfaces

Directory of Open Access Journals (Sweden)

Parisa eLotfi

2011-10-01

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

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

Science.gov (United States)

Zhang, Yisi; Langford, Bruce; Kozhevnikov, Alexay

2011-10-30

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

The importance to reveal buried interfaces in the semiconductor heterostructure devices

International Nuclear Information System (INIS)

Takeda, Yoshikazu; Tabuchi, Masao

2007-01-01

Even though several in-situ monitoring techniques exist and are quite useful to understand the growth processes in MBE or MOVPE, we also need a technique to reveal the buried interfaces along which carriers are transported and recombine to emit light. The interface is modified during the capping (overgrowth) and also during the device fabrication processes after growth. We need to correlate the interface structures in the devices and the device performances. The only technique we have at present is the X-ray CTR scattering measurements. We discuss the limits of the in-situ monitoring and the necessity to reveal the buried interfaces non-destructively, either in-situ or ex-situ

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

Directory of Open Access Journals (Sweden)

Yujuan Zhao

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

INTLIB-6, Graphic Device Interface Library for ENDF/B Processing Codes

International Nuclear Information System (INIS)

Dunford, L.

1999-01-01

1 - Description of program or function: The graphic subroutine libraries DISSPLA and GRALIB (USCD1211) generally produce output which is independent of the output graphic device. A set of device dependent interface routines is required to translate the device independent output to the form required for each graphic device available. The interface library INTLIB provides interface routines for the following output formats: TETRONIX - LN03 PLUS, - video display terminal; POSTSCRIPT - LN03 PLUS with PostScript, - LaserJet III in PostScript mode, - video display terminal; REGIS - VT240 and VT1200; HPGL - LaserJet III in HPGL mode; FR80 - COMP80 film, fiche and hard copy

Neural Networks through Shared Maps in Mobile Devices

Directory of Open Access Journals (Sweden)

William Raveane

2014-12-01

Full Text Available We introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is then inferred from the neural network output through energy minimization to reach a more precize localization than what traditional maximum value class comparisons yield. These results are apt for applying this process in a mobile device for real time image recognition.

Improving Neural Recording Technology at the Nanoscale

Science.gov (United States)

Ferguson, John Eric

Neural recording electrodes are widely used to study normal brain function (e.g., learning, memory, and sensation) and abnormal brain function (e.g., epilepsy, addiction, and depression) and to interface with the nervous system for neuroprosthetics. With a deep understanding of the electrode interface at the nanoscale and the use of novel nanofabrication processes, neural recording electrodes can be designed that surpass previous limits and enable new applications. In this thesis, I will discuss three projects. In the first project, we created an ultralow-impedance electrode coating by controlling the nanoscale texture of electrode surfaces. In the second project, we developed a novel nanowire electrode for long-term intracellular recordings. In the third project, we created a means of wirelessly communicating with ultra-miniature, implantable neural recording devices. The techniques developed for these projects offer significant improvements in the quality of neural recordings. They can also open the door to new types of experiments and medical devices, which can lead to a better understanding of the brain and can enable novel and improved tools for clinical applications.

iSpike: a spiking neural interface for the iCub robot

International Nuclear Information System (INIS)

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

2012-01-01

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

Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

Science.gov (United States)

Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

The Shape of Things to Come: The Military Benefits of the Brain-Computer Interface in 2040

Science.gov (United States)

2015-04-01

capability. Bidirectional interfaces with the ability to influence specific neural groups will not only revolutionize health-care, but transform how the...both read and stimulate neural activity. Unidirectional BCIs are useful; however, it is the bidirectional device that opens the potential for the...connected intra-cranially distributed networks that communicate with thousands of ‘ neural reading & stimulating’ devices that could be safely inserted

High speed digital interfacing for a neural data acquisition system

Directory of Open Access Journals (Sweden)

Bahr Andreas

2016-09-01

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

Flexible microelectrode array for interfacing with the surface of neural ganglia

Science.gov (United States)

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

2018-06-01

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

A neural network device for on-line particle identification in cosmic ray experiments

International Nuclear Information System (INIS)

Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.

2004-01-01

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

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

Science.gov (United States)

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

2015-04-01

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

The Operation of a Domestic Interface Device for the HANARO Control Rod

International Nuclear Information System (INIS)

Choi, Young San; Kim, Sang Jin; Lee, Jung Hee; Kim, Hyung Kyoo

2010-01-01

The interface device for the HANARO control rod which was supplied by a foreign company put difficulties on reactor operation due to the obsolescence of the products and lukewarm technical support from the manufacturer. The development of the interface device based on domestic technology has been completed in order to solve the problems in this issue and to ensure safe and reliable reactor operation. This paper describes the development process of the domestic interface device conducted which was over 5 years, the field test results, and the reactor operation application results

Optimizing the Usability of Brain-Computer Interfaces.

Science.gov (United States)

Zhang, Yin; Chase, Steve M

2018-03-22

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

OpenAIRE

Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

2012-01-01

Paralysis following spinal cord injury, brainstemstroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface ...

Fractal Interfaces for Stimulating and Recording Neural Implants

Science.gov (United States)

Watterson, William James

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

Neural control of finger movement via intracortical brain-machine interface

Science.gov (United States)

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

2017-12-01

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

Fostering learners' interaction with content: A learner-centered mobile device interface

Science.gov (United States)

Abdous, M.

2015-12-01

With the ever-increasing omnipresence of mobile devices in student life, leveraging smart devices to foster students' interaction with course content is critical. Following a learner-centered design iterative approach, we designed a mobile interface that may enable learners to access and interact with online course content efficiently and intuitively. Our design process leveraged recent technologies, such as bootstrap, Google's Material Design, HTML5, and JavaScript to design an intuitive, efficient, and portable mobile interface with a variety of built-in features, including context sensitive bookmarking, searching, progress tracking, captioning, and transcript display. The mobile interface also offers students the ability to ask context-related questions and to complete self-checks as they watch audio/video presentations. Our design process involved ongoing iterative feedback from learners, allowing us to refine and tweak the interface to provide learners with a unified experience across platforms and devices. The innovative combination of technologies built around well-structured and well-designed content seems to provide an effective learning experience to mobile learners. Early feedback indicates a high level of satisfaction with the interface's efficiency, intuitiveness, and robustness from both students and faculty.

Video Game Device Haptic Interface for Robotic Arc Welding

Energy Technology Data Exchange (ETDEWEB)

Corrie I. Nichol; Milos Manic

2009-05-01

Recent advances in technology for video games have made a broad array of haptic feedback devices available at low cost. This paper presents a bi-manual haptic system to enable an operator to weld remotely using the a commercially available haptic feedback video game device for the user interface. The system showed good performance in initial tests, demonstrating the utility of low cost input devices for remote haptic operations.

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

Science.gov (United States)

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

2018-01-17

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

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke.

Science.gov (United States)

McConnell, Alistair C; Moioli, Renan C; Brasil, Fabricio L; Vallejo, Marta; Corne, David W; Vargas, Patricia A; Stokes, Adam A

2017-06-28

To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation. The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke. A total of 110 commercial and non-commercial hand and wrist devices, spanning the 2 major core designs: end-effector and exoskeleton are reviewed. The growing body of evidence on the efficacy and relevance of incorporating brain-machine interfaces in stroke rehabilitation is summarized. The challenges involved in integrating robotic rehabilitation into the healthcare system are discussed. This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.

Electric-field-controlled interface dipole modulation for Si-based memory devices.

Science.gov (United States)

Miyata, Noriyuki

2018-05-31

Various nonvolatile memory devices have been investigated to replace Si-based flash memories or emulate synaptic plasticity for next-generation neuromorphic computing. A crucial criterion to achieve low-cost high-density memory chips is material compatibility with conventional Si technologies. In this paper, we propose and demonstrate a new memory concept, interface dipole modulation (IDM) memory. IDM can be integrated as a Si field-effect transistor (FET) based memory device. The first demonstration of this concept employed a HfO 2 /Si MOS capacitor where the interface monolayer (ML) TiO 2 functions as a dipole modulator. However, this configuration is unsuitable for Si-FET-based devices due to its large interface state density (D it ). Consequently, we propose, a multi-stacked amorphous HfO 2 /1-ML TiO 2 /SiO 2 IDM structure to realize a low D it and a wide memory window. Herein we describe the quasi-static and pulse response characteristics of multi-stacked IDM MOS capacitors and demonstrate flash-type and analog memory operations of an IDM FET device.

Surface and interface analysis of photovoltaic devices

International Nuclear Information System (INIS)

Kazmerski, L.L.

1983-01-01

Interface chemistry can control the performance and operational lifetime of solar cells, especially thin-film, polycrystalline devices. The composition and elemental integrity of device surfaces, internal junctions, layer and defect interfces can be related to and dominate the electroptical characteristics of the materials/ devices. This paper examines the compositional properties of external and internal surfaces in polycrystaline solar cells, utilizing high-resolution, complementary surface analysis techniques. The electronic properties of these same regions are evaluated using microelectrical characterization methods. Cell performance, in turn, is explained in terms of these relation-ships. Specifically, two solar cell types are used as examples: (1) the polycrystalline Si homojunction and (2) the (Cd Zn)S/CuInSe 2 heterojunction. Throughout these investigations of photovoltaic devices, the limitations and strengths of the surface and electrical microanalyses techniques are emphasized and discussed. (Author) [pt

Biomechatronics in medical rehabilitation biomodelling, interface, and control

CERN Document Server

Xie, Shane (S Q )

2017-01-01

This book focuses on the key technologies in developing biomechatronic systems for medical rehabilitation purposes. It includes a detailed analysis of biosignal processing, biomechanics modelling, neural and muscular interfaces, artificial actuators, robot-assisted training, clinical setup/implementation and rehabilitation robot control. Encompassing highly multidisciplinary themes in the engineering and medical fields, it presents researchers’ insights into the emerging technologies and developments that are being utilized in biomechatronics for medical purposes. Presenting a detailed analysis of five key areas in rehabilitation robotics: (i) biosignal processing; (ii) biomechanics modelling; (iii) neural and muscular interfaces; (iv) artificial actuators and devices; and (v) the use of neurological and muscular interfaces in rehabilitation robots control, the book describes the design of biomechatronic systems, the methods and control systems used and the implementation and testing in order to show how th...

INTERFACE DEVICE FOR NONDESTRUCTIVE TESTING OF RESIDUAL SURFACE STRESSES

Directory of Open Access Journals (Sweden)

Gennady A. Perepelkin

2016-01-01

Full Text Available The paper considers the organization of connection of a personal computer with a device for nondestructive testing of residual surface stresses. The device works is based on the phenomenon of diffraction of ionizing radiation from the crystal lattice near the surface of the crystallites. Proposed software interface to the organization for each type of user: the device developers, administrators, users. Some aspects of the organization of communication microcontroller to a PC via USB-port

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.

Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

Directory of Open Access Journals (Sweden)

Sun-Il Chang

2018-01-01

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

Shortening User Interface Design Iterations through Realtime Visualisation of Design Actions on the Target Device

OpenAIRE

MESKENS, Jan; LUYTEN, Kris; CONINX, Karin

2009-01-01

In current mobile user interface design tools, it is time consuming to export a design to the target device. This makes it hard for designers to iterate over the user interfaces they are creating. We propose Gummy-live, a GUI builder for mobile devices allowing designers to test and observe immediately on the target device each step they take in the GUI builder. This way, designers are stimulated to iteratively test and refine user interface prototypes in order to take the target device charac...

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

Czech Academy of Sciences Publication Activity Database

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

2016-01-01

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

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Directory of Open Access Journals (Sweden)

Tayfun Gokmen

2017-10-01

Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

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

Science.gov (United States)

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

2016-07-18

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

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

Directory of Open Access Journals (Sweden)

Miriam eZacksenhouse

2015-05-01

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

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

Science.gov (United States)

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

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

Investigation of Interface Charges at the Heterojunction Discontinuity in HBT Devices

DEFF Research Database (Denmark)

Fuente, Jesús Grajal de al; Krozer, Viktor

2002-01-01

-doped layers are basic tools for interface engineering. An accurate modelling of heterointerfaces which includes thermionic-field emission, surface charges, and surface dipoles allows to analyse the electrical performance of some modern devices based on band gap and interface engineering. It is demonstrated...

Building Interfaces: Mechanisms, fabrication, and applications at the biotic/abiotic interface for silk fibroin based bioelectronic and biooptical devices

Science.gov (United States)

Brenckle, Mark

Recent efforts in bioelectronics and biooptics have led to a shift in the materials and form factors used to make medical devices, including high performance, implantable, and wearable sensors. In this context, biopolymer-based devices must be processed to interface the soft, curvilinear biological world with the rigid, inorganic world of traditional electronics and optics. This poses new material-specific fabrication challenges in designing such devices, which in turn requires further understanding of the fundamental physical behaviors of the materials in question. As a biopolymer, silk fibroin protein has remarkable promise in this space, due to its bioresorbability, mechanical strength, optical clarity, ability to be reshaped on the micro- and nano-scale, and ability to stabilize labile compounds. Application of this material to devices at the biotic/abiotic interface will require the development of fabrication techniques for nano-patterning, lithography, multilayer adhesion, and transfer printing in silk materials. In this work, we address this need through fundamental study of the thermal and diffusional properties of silk protein as it relates to these fabrication strategies. We then leverage these properties to fabricate devices well suited to the biotic/abiotic interface in three areas: shelf-ready sensing, implantable transient electronics, and wearable biosensing. These example devices will illustrate the advantages of silk in this class of bioelectronic and biooptical devices, from fundamentals through application, and contribute to a silk platform for the development of future devices that combine biology with high technology.

High-Density Stretchable Electrode Grids for Chronic Neural Recording.

Science.gov (United States)

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F; Buzsáki, György; Vörös, János

2018-04-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. © 2018 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Engineering the Brain: Ethical Issues and the Introduction of Neural Devices.

Science.gov (United States)

Klein, Eran; Brown, Tim; Sample, Matthew; Truitt, Anjali R; Goering, Sara

2015-01-01

Neural devices now under development stand to interact with and alter the human brain in ways that may challenge standard notions of identity, normality, authority, responsibility, privacy and justice.

Physical interface dynamics alter how robotic exosuits augment human movement: implications for optimizing wearable assistive devices.

Science.gov (United States)

Yandell, Matthew B; Quinlivan, Brendan T; Popov, Dmitry; Walsh, Conor; Zelik, Karl E

2017-05-18

Wearable assistive devices have demonstrated the potential to improve mobility outcomes for individuals with disabilities, and to augment healthy human performance; however, these benefits depend on how effectively power is transmitted from the device to the human user. Quantifying and understanding this power transmission is challenging due to complex human-device interface dynamics that occur as biological tissues and physical interface materials deform and displace under load, absorbing and returning power. Here we introduce a new methodology for quickly estimating interface power dynamics during movement tasks using common motion capture and force measurements, and then apply this method to quantify how a soft robotic ankle exosuit interacts with and transfers power to the human body during walking. We partition exosuit end-effector power (i.e., power output from the device) into power that augments ankle plantarflexion (termed augmentation power) vs. power that goes into deformation and motion of interface materials and underlying soft tissues (termed interface power). We provide empirical evidence of how human-exosuit interfaces absorb and return energy, reshaping exosuit-to-human power flow and resulting in three key consequences: (i) During exosuit loading (as applied forces increased), about 55% of exosuit end-effector power was absorbed into the interfaces. (ii) However, during subsequent exosuit unloading (as applied forces decreased) most of the absorbed interface power was returned viscoelastically. Consequently, the majority (about 75%) of exosuit end-effector work over each stride contributed to augmenting ankle plantarflexion. (iii) Ankle augmentation power (and work) was delayed relative to exosuit end-effector power, due to these interface energy absorption and return dynamics. Our findings elucidate the complexities of human-exosuit interface dynamics during transmission of power from assistive devices to the human body, and provide insight into

Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2010-10-01

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

Interface requirements in nuclear medicine devices and systems

International Nuclear Information System (INIS)

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

1982-01-01

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

Control Board Digital Interface Input Devices – Touchscreen, Trackpad, or Mouse?

Energy Technology Data Exchange (ETDEWEB)

Thomas A. Ulrich; Ronald L. Boring; Roger Lew

2015-08-01

The authors collaborated with a power utility to evaluate input devices for use in the human system interface (HSI) for a new digital Turbine Control System (TCS) at a nuclear power plant (NPP) undergoing a TCS upgrade. A standalone dynamic software simulation of the new digital TCS and a mobile kiosk were developed to conduct an input device study to evaluate operator preference and input device effectiveness. The TCS software presented the anticipated HSI for the TCS and mimicked (i.e., simulated) the turbine systems’ responses to operator commands. Twenty-four licensed operators from the two nuclear power units participated in the study. Three input devices were tested: a trackpad, mouse, and touchscreen. The subjective feedback from the survey indicates the operators preferred the touchscreen interface. The operators subjectively rated the touchscreen as the fastest and most comfortable input device given the range of tasks they performed during the study, but also noted a lack of accuracy for selecting small targets. The empirical data suggest the mouse input device provides the most consistent performance for screen navigation and manipulating on screen controls. The trackpad input device was both empirically and subjectively found to be the least effective and least desired input device.

Recent Advances in Neural Recording Microsystems

Directory of Open Access Journals (Sweden)

Benoit Gosselin

2011-04-01

Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

Understanding and Design of Polymer Device Interfaces

Energy Technology Data Exchange (ETDEWEB)

Kahn, Antoine [Princeton Univ., NJ (United States)

2015-10-26

The research performed under grant DE-FG02-04ER46165 between May 2008 and April 2011 focused on the understanding and control of interfaces of organic semiconductors in general, and polymer interfaces more specifically. This work was a joined effort by three experimentalists and a theoretician. Emphasis was placed on the determination of the electronic structure of these interfaces, i.e. the relative energy position of molecular levels across these interfaces. From these electronic structures depend the injection, extraction and transport of charge carriers into, from and across, respectively, all (opto)electronic devices made of these semiconductors. A significant fraction of our work focused on ways to modify and optimize interfaces, for example via chemical doping of the semiconductors to reduce interface energy barriers or via deposition of ultra-thin work function-reducing polymer or self-assembled monolayers of dipolar molecules. Another significant fraction of our work was devoted to exploring alternate and unconventional interface formation methods, in particular the soft-contact lamination of both metal contacts and polymer overlayers on top of polymer films. These methods allowed us to better understand the impact of hot metal atom evaporation on a soft organic surface, as well as the key mechanisms that control the energetics of polymer/polymer heterojunctions. Finally, a significant fraction of the research was directed to understanding the electronic structure of buried polymer heterojunctions, in particular within donor/acceptor blends of interest in organic photovoltaic applications. The work supported by this grant resulted in 17 publications in some of the best peer-reviewed journals of the field, as well as numerous presentations at US and international conferences.

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

Science.gov (United States)

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

2018-04-01

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

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

Science.gov (United States)

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

2014-02-01

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

Evolvable synthetic neural system

Science.gov (United States)

Curtis, Steven A. (Inventor)

2009-01-01

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

Double network bacterial cellulose hydrogel to build a biology-device interface

Science.gov (United States)

Shi, Zhijun; Li, Ying; Chen, Xiuli; Han, Hongwei; Yang, Guang

2013-12-01

Establishing a biology-device interface might enable the interaction between microelectronics and biotechnology. In this study, electroactive hydrogels have been produced using bacterial cellulose (BC) and conducting polymer (CP) deposited on the BC hydrogel surface to cover the BC fibers. The structures of these composites thus have double networks, one of which is a layer of electroactive hydrogels combined with BC and CP. The electroconductivity provides the composites with capabilities for voltage and current response, and the BC hydrogel layer provides good biocompatibility, biodegradability, bioadhesion and mass transport properties. Such a system might allow selective biological functions such as molecular recognition and specific catalysis and also for probing the detailed genetic and molecular mechanisms of life. A BC-CP composite hydrogel could then lead to a biology-device interface. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) are used here to study the composite hydrogels' electroactive property. BC-PAni and BC-PPy respond to voltage changes. This provides a mechanism to amplify electrochemical signals for analysis or detection. BC hydrogels were found to be able to support the growth, spreading and migration of human normal skin fibroblasts without causing any cytotoxic effect on the cells in the cell culture. These double network BC-CP hydrogels are biphasic Janus hydrogels which integrate electroactivity with biocompatibility, and might provide a biology-device interface to produce implantable devices for personalized and regenerative medicine.

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

Directory of Open Access Journals (Sweden)

Elif Ç. Seymour

2014-02-01

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

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.

Tissue-electronics interfaces: from implantable devices to engineered tissues

Science.gov (United States)

Feiner, Ron; Dvir, Tal

2018-01-01

Biomedical electronic devices are interfaced with the human body to extract precise medical data and to interfere with tissue function by providing electrical stimuli. In this Review, we outline physiologically and pathologically relevant tissue properties and processes that are important for designing implantable electronic devices. We summarize design principles for flexible and stretchable electronics that adapt to the mechanics of soft tissues, such as those including conducting polymers, liquid metal alloys, metallic buckling and meandering architectures. We further discuss technologies for inserting devices into the body in a minimally invasive manner and for eliminating them without further intervention. Finally, we introduce the concept of integrating electronic devices with biomaterials and cells, and we envision how such technologies may lead to the development of bionic organs for regenerative medicine.

A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields.

Directory of Open Access Journals (Sweden)

Alessandro Vato

Full Text Available We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

Effect of interface of electronics devices constructed with different materials to X-ray

International Nuclear Information System (INIS)

Mu Weibing; Chen Panxun

2003-01-01

The behavior of X-ray nearby interface which is constructed with different materials is introduced in this paper. And the affect to electronics devices of this behavior is analyzed, the affect factors of four interfaces are calculated by Monte-Carlo method

Neural interface methods and apparatus to provide artificial sensory capabilities to a subject

Energy Technology Data Exchange (ETDEWEB)

Buerger, Stephen P.; Olsson, III, Roy H.; Wojciechowski, Kenneth E.; Novick, David K.; Kholwadwala, Deepesh K.

2017-01-24

Embodiments of neural interfaces according to the present invention comprise sensor modules for sensing environmental attributes beyond the natural sensory capability of a subject, and communicating the attributes wirelessly to an external (ex-vivo) portable module attached to the subject. The ex-vivo module encodes and communicates the attributes via a transcutaneous inductively coupled link to an internal (in-vivo) module implanted within the subject. The in-vivo module converts the attribute information into electrical neural stimuli that are delivered to a peripheral nerve bundle within the subject, via an implanted electrode. Methods and apparatus according to the invention incorporate implantable batteries to power the in-vivo module allowing for transcutaneous bidirectional communication of low voltage (e.g. on the order of 5 volts) encoded signals as stimuli commands and neural responses, in a robust, low-error rate, communication channel with minimal effects to the subjects' skin.

Characterizing Graphene-modified Electrodes for Interfacing with Arduino®-based Devices.

Science.gov (United States)

Arris, Farrah Aida; Ithnin, Mohamad Hafiz; Salim, Wan Wardatul Amani Wan

2016-08-01

Portable low-cost platform and sensing systems for identification and quantitative measurement are in high demand for various environmental monitoring applications, especially in field work. Quantifying parameters in the field requires both minimal sample handling and a device capable of performing measurements with high sensitivity and stability. Furthermore, the one-device-fits-all concept is useful for continuous monitoring of multiple parameters. Miniaturization of devices can be achieved by introducing graphene as part of the transducer in an electrochemical sensor. In this project, we characterize graphene deposition methods on glassy-carbon electrodes (GCEs) with the goal of interfacing with an Arduino-based user-friendly microcontroller. We found that a galvanostatic electrochemical method yields the highest peak current of 10 mA, promising a highly sensitive electrochemical sensor. An Atlas Scientific™ printed circuit board (PCB) was connected to an Arduino® microcontroller using a multi-circuit connection that can be interfaced with graphene-based electrochemical sensors for environmental monitoring.

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

Directory of Open Access Journals (Sweden)

Huixia Zhao

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

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.

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

Invasive Intraneural Interfaces: Foreign Body Reaction Issues

Science.gov (United States)

Lotti, Fiorenza; Ranieri, Federico; Vadalà, Gianluca; Zollo, Loredana; Di Pino, Giovanni

2017-01-01

Intraneural interfaces are stimulation/registration devices designed to couple the peripheral nervous system (PNS) with the environment. Over the last years, their use has increased in a wide range of applications, such as the control of a new generation of neural-interfaced prostheses. At present, the success of this technology is limited by an electrical impedance increase, due to an inflammatory response called foreign body reaction (FBR), which leads to the formation of a fibrotic tissue around the interface, eventually causing an inefficient transduction of the electrical signal. Based on recent developments in biomaterials and inflammatory/fibrotic pathologies, we explore and select the biological solutions that might be adopted in the neural interfaces FBR context: modifications of the interface surface, such as organic and synthetic coatings; the use of specific drugs or molecular biology tools to target the microenvironment around the interface; the development of bio-engineered-scaffold to reduce immune response and promote interface-tissue integration. By linking what we believe are the major crucial steps of the FBR process with related solutions, we point out the main issues that future research has to focus on: biocompatibility without losing signal conduction properties, good reproducible in vitro/in vivo models, drugs exhaustion and undesired side effects. The underlined pros and cons of proposed solutions show clearly the importance of a better understanding of all the molecular and cellular pathways involved and the need of a multi-target action based on a bio-engineered combination approach. PMID:28932181

Development the interface system of master-slave manipulator and external input device on the graphic simulator

International Nuclear Information System (INIS)

Song, T. J.; Lee, J. Y.; Kim, S. H.; Yoon, J. S.

2002-01-01

The master-slave manipulator is the generally used as remote handling device in the hot cell, in which the high level radioactive materials such as spent fuels are handled. To analyze the motion of remote handling device and to simulate the remote handling operation task in the hot cell, the 3D graphic simulator which has been installed the master-slave manipulator is established. Also the interface program of external input device with 6 DOF(degree of Freedom) is developed and connected to graphic simulator with LLTI(Low Level Tele-operation Interface) which provides a uniquely optimized, high speed, bidirectional communication interface to one or more of system and processes

im4Things: An Ontology-Based Natural Language Interface for Controlling Devices in the Internet of Things

KAUST Repository

Noguera-Arnaldos, José Á ngel; Paredes-Valverde, Mario André s; Salas-Zá rate, Marí a Pilar; Rodriguez-Garcia, Miguel Angel; Valencia-Garcí a, Rafael; Ochoa, José Luis

2017-01-01

. However, the IoT also introduces new challenges, some of which arise from the large range of devices currently available and the heterogeneous interfaces provided for their control. The control and management of this variety of devices and interfaces

Charge transfer through amino groups-small molecules interface improving the performance of electroluminescent devices

Science.gov (United States)

Havare, Ali Kemal; Can, Mustafa; Tozlu, Cem; Kus, Mahmut; Okur, Salih; Demic, Şerafettin; Demirak, Kadir; Kurt, Mustafa; Icli, Sıddık

2016-05-01

A carboxylic group functioned charge transporting was synthesized and self-assembled on an indium tin oxide (ITO) anode. A typical electroluminescent device [modified ITO/TPD (50 nm)/Alq3 (60 nm)/LiF (2 nm)/(120 nm)] was fabricated to investigate the effect of the amino groups-small molecules interface on the characteristics of the device. The increase in the surface work function of ITO is expected to facilitate the hole injection from the ITO anode to the Hole Transport Layer (HTL) in electroluminescence. The modified electroluminescent device could endure a higher current and showed a much higher luminance than the nonmodified one. For the produced electroluminescent devices, the I-V characteristics, optical characterization and quantum yields were performed. The external quantum efficiency of the modified electroluminescent device is improved as the result of the presence of the amino groups-small molecules interface.

Evaluating and improving the performance of thin film force sensors within body and device interfaces.

Science.gov (United States)

Likitlersuang, Jirapat; Leineweber, Matthew J; Andrysek, Jan

2017-10-01

Thin film force sensors are commonly used within biomechanical systems, and at the interface of the human body and medical and non-medical devices. However, limited information is available about their performance in such applications. The aims of this study were to evaluate and determine ways to improve the performance of thin film (FlexiForce) sensors at the body/device interface. Using a custom apparatus designed to load the sensors under simulated body/device conditions, two aspects were explored relating to sensor calibration and application. The findings revealed accuracy errors of 23.3±17.6% for force measurements at the body/device interface with conventional techniques of sensor calibration and application. Applying a thin rigid disc between the sensor and human body and calibrating the sensor using compliant surfaces was found to substantially reduce measurement errors to 2.9±2.0%. The use of alternative calibration and application procedures is recommended to gain acceptable measurement performance from thin film force sensors in body/device applications. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Wireless sEMG-Based Body-Machine Interface for Assistive Technology Devices.

Science.gov (United States)

Fall, Cheikh Latyr; Gagnon-Turcotte, Gabriel; Dube, Jean-Francois; Gagne, Jean Simon; Delisle, Yanick; Campeau-Lecours, Alexandre; Gosselin, Clement; Gosselin, Benoit

2017-07-01

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

A novel asynchronous access method with binary interfaces

Directory of Open Access Journals (Sweden)

Torres-Solis Jorge

2008-10-01

Full Text Available Abstract Background Traditionally synchronous access strategies require users to comply with one or more time constraints in order to communicate intent with a binary human-machine interface (e.g., mechanical, gestural or neural switches. Asynchronous access methods are preferable, but have not been used with binary interfaces in the control of devices that require more than two commands to be successfully operated. Methods We present the mathematical development and evaluation of a novel asynchronous access method that may be used to translate sporadic activations of binary interfaces into distinct outcomes for the control of devices requiring an arbitrary number of commands to be controlled. With this method, users are required to activate their interfaces only when the device under control behaves erroneously. Then, a recursive algorithm, incorporating contextual assumptions relevant to all possible outcomes, is used to obtain an informed estimate of user intention. We evaluate this method by simulating a control task requiring a series of target commands to be tracked by a model user. Results When compared to a random selection, the proposed asynchronous access method offers a significant reduction in the number of interface activations required from the user. Conclusion This novel access method offers a variety of advantages over traditionally synchronous access strategies and may be adapted to a wide variety of contexts, with primary relevance to applications involving direct object manipulation.

Automatic Speech Recognition from Neural Signals: A Focused Review

Directory of Open Access Journals (Sweden)

Christian Herff

2016-09-01

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

Effects of interface electric field on the magnetoresistance in spin devices

Energy Technology Data Exchange (ETDEWEB)

Tanamoto, T., E-mail: tetsufumi.tanamoto@toshiba.co.jp; Ishikawa, M.; Inokuchi, T.; Sugiyama, H.; Saito, Y. [Advanced LSI Technology Laboratory Corporate Research and Development Center, Toshiba Corporation 1, Komukai Toshiba-cho, Saiwai-ku, Kawasaki 212-8582 (Japan)

2014-04-28

An extension of the standard spin diffusion theory is presented by using a quantum diffusion theory via a density-gradient (DG) term that is suitable for describing interface quantum tunneling phenomena. The magnetoresistance (MR) ratio is greatly modified by the DG term through an interface electric field. We have also carried out spin injection and detection measurements using four-terminal Si devices. The local measurement shows that the MR ratio changes depending on the current direction. We show that the change of the MR ratio depending on the current direction comes from the DG term regarding the asymmetry of the two interface electronic structures.

Development of a Math Input Interface with Flick Operation for Mobile Devices

Science.gov (United States)

Nakamura, Yasuyuki; Nakahara, Takahiro

2016-01-01

Developing online test environments for e-learning for mobile devices will be useful to increase drill practice opportunities. In order to provide a drill practice environment for calculus using an online math test system, such as STACK, we develop a flickable math input interface that can be easily used on mobile devices. The number of taps…

Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

OpenAIRE

Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

2017-01-01

In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

A Web Service and Interface for Remote Electronic Device Characterization

Science.gov (United States)

Dutta, S.; Prakash, S.; Estrada, D.; Pop, E.

2011-01-01

A lightweight Web Service and a Web site interface have been developed, which enable remote measurements of electronic devices as a "virtual laboratory" for undergraduate engineering classes. Using standard browsers without additional plugins (such as Internet Explorer, Firefox, or even Safari on an iPhone), remote users can control a Keithley…

NeuroMEMS: Neural Probe Microtechnologies

Directory of Open Access Journals (Sweden)

Sam Musallam

2008-10-01

Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

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

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Chojnowski, K.; FrÄ czek, J.

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

A multimodal interface device for online board games designed for sight-impaired people.

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Caporusso, Nicholas; Mkrtchyan, Lusine; Badia, Leonardo

2010-03-01

Online games between remote opponents playing over computer networks are becoming a common activity of everyday life. However, computer interfaces for board games are usually based on the visual channel. For example, they require players to check their moves on a video display and interact by using pointing devices such as a mouse. Hence, they are not suitable for visually impaired people. The present paper discusses a multipurpose system that allows especially blind and deafblind people playing chess or other board games over a network, therefore reducing their disability barrier. We describe and benchmark a prototype of a special interactive haptic device for online gaming providing a dual tactile feedback. The novel interface of this proposed device is able to guarantee not only a better game experience for everyone but also an improved quality of life for sight-impaired people.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

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Ziyang He

2018-04-01

Full Text Available By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

Science.gov (United States)

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices.

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Cheruku, Ramalingaswamy; Edla, Damodar Reddy; Kuppili, Venkatanareshbabu; Dharavath, Ramesh; Beechu, Nareshkumar Reddy

2017-08-01

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

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

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

2015-07-01

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

Improved head direction command classification using an optimised Bayesian neural network.

Science.gov (United States)

Nguyen, Son T; Nguyen, Hung T; Taylor, Philip B; Middleton, James

2006-01-01

Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.

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

Science.gov (United States)

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

2010-09-01

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

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

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Christopher Bergmeir

2012-01-01

Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

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

Science.gov (United States)

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

2015-05-01

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

Radiation-induced interface state generation in MOS devices with reoxidised nitrided SiO2 gate dielectrics

International Nuclear Information System (INIS)

Lo, G.Q.; Shih, D.K.; Ting, W.; Kwong, D.L.

1989-01-01

In this letter, the radiation-induced interface state generation ΔD it in MOS devices with reoxidised nitrided gate oxides has been studied. The reoxidised nitrided oxides were fabricated by rapid thermal reoxidation (RTO) of rapidly thermal nitrided (RTN) SiO 2 . The devices were irradiated by exposure to X-rays at doses of 0.5-5.0 Mrad (Si). It is found that the RTO process improves the radiation hardness of RTN oxides in terms of interface state generation. The enhanced interface ''hardness'' of reoxidised nitrided oxides is attributed to the strainless interfacial oxide regrowth or reduction of hydrogen concentration during RTO of RTN oxides. (author)

Wireless microsensor network solutions for neurological implantable devices

Science.gov (United States)

Abraham, Jose K.; Whitchurch, Ashwin; Varadan, Vijay K.

2005-05-01

The design and development of wireless mocrosensor network systems for the treatment of many degenerative as well as traumatic neurological disorders is presented in this paper. Due to the advances in micro and nano sensors and wireless systems, the biomedical sensors have the potential to revolutionize many areas in healthcare systems. The integration of nanodevices with neurons that are in communication with smart microsensor systems has great potential in the treatment of many neurodegenerative brain disorders. It is well established that patients suffering from either Parkinson"s disease (PD) or Epilepsy have benefited from the advantages of implantable devices in the neural pathways of the brain to alter the undesired signals thus restoring proper function. In addition, implantable devices have successfully blocked pain signals and controlled various pelvic muscles in patients with urinary and fecal incontinence. Even though the existing technology has made a tremendous impact on controlling the deleterious effects of disease, it is still in its infancy. This paper presents solutions of many problems of today's implantable and neural-electronic interface devices by combining nanowires and microelectronics with BioMEMS and applying them at cellular level for the development of a total wireless feedback control system. The only device that will actually be implanted in this research is the electrodes. All necessary controllers will be housed in accessories that are outside the body that communicate with the implanted electrodes through tiny inductively-coupled antennas. A Parkinson disease patient can just wear a hat-system close to the implantable neural probe so that the patient is free to move around, while the sensors continually monitor, record, transmit all vital information to health care specialist. In the event of a problem, the system provides an early warning to the patient while they are still mobile thus providing them the opportunity to react and

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.

An easy-to-use microfluidic interconnection system to create quick and reversibly interfaced simple microfluidic devices

DEFF Research Database (Denmark)

Pfreundt, Andrea; Andersen, Karsten Brandt; Dimaki, Maria

2015-01-01

The presented microfluidic interconnection system provides an alternative for the individual interfacing of simple microfluidic devices fabricated in polymers such as polymethylmethacrylate, polycarbonate and cyclic olefin polymer. A modification of the device inlet enables the direct attachment...... pressures above 250 psi and therefore supports applications with high flow rates or highly viscous fluids. The ease of incorporation, configuration, fabrication and use make this interconnection system ideal for the rapid prototyping of simple microfluidic devices or other integrated systems that require...... microfluidic interfaces. It provides a valuable addition to the toolbox of individual and small arrays of connectors suitable for micromachined or template-based injection molded devices since it does not require protruding, threaded or glued modifications on the inlet and avoids bulky and expensive fittings....

A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.

Science.gov (United States)

Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

2013-01-01

Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology. Copyright © 2013 Elsevier Inc. All rights reserved.

Brain Computer Interface-Controlling Devices Utilizing The Alpha Brain Waves

Directory of Open Access Journals (Sweden)

Rohan Hundia

2015-01-01

Full Text Available Abstract This paper describes the development and testing of an interface system whereby one can control external devices by voluntarily controlling alpha waves that is through eye movement. Such a system may be used for the control of prosthetics robotic arms and external devices like wheelchairs using the alpha brain waves and the Mu rhythm. The response generated through the movement of the eye detecting and controlling the amplitude of the alpha brain waves is interfaced and processed to control Robotic systems and smart home control. In order to measure the response of alpha waves over different lobes of the brain initially I measured these signals over 32 regions using silver chloride plated electrodes. By the opening and the closure of the eyes and the movement in the up-down left-right directions and processing these movements measuring them over the occipital region I was able to differentiate the amplitude of the alpha waves generated due to these several movements. In the First session testing period subjects were asked to close and open their eyes and they were able to control limited movements of a Robot and a prosthetic arm. In the Second 2session the movement of the eyes was also considered left-right up-down along with the opening and closure during this time span they were able to control more dimensions of the robot several devices at the same time using different eye movements.

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

Science.gov (United States)

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

2016-01-01

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

The chemistry of cyborgs--interfacing technical devices with organisms.

Science.gov (United States)

Giselbrecht, Stefan; Rapp, Bastian E; Niemeyer, Christof M

2013-12-23

The term "cyborg" refers to a cybernetic organism, which characterizes the chimera of a living organism and a machine. Owing to the widespread application of intracorporeal medical devices, cyborgs are no longer exclusively a subject of science fiction novels, but technically they already exist in our society. In this review, we briefly summarize the development of modern prosthetics and the evolution of brain-machine interfaces, and discuss the latest technical developments of implantable devices, in particular, biocompatible integrated electronics and microfluidics used for communication and control of living organisms. Recent examples of animal cyborgs and their relevance to fundamental and applied biomedical research and bioethics in this novel and exciting field at the crossroads of chemistry, biomedicine, and the engineering sciences are presented. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Directory of Open Access Journals (Sweden)

Stefano ePanzeri

2016-04-01

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

A review on mechanical considerations for chronically-implanted neural probes

Science.gov (United States)

Lecomte, Aziliz; Descamps, Emeline; Bergaud, Christian

2018-06-01

This review intends to present a comprehensive analysis of the mechanical considerations for chronically-implanted neural probes. Failure of neural electrical recordings or stimulation over time has shown to arise from foreign body reaction and device material stability. It seems that devices that match most closely with the mechanical properties of the brain would be more likely to reduce the mechanical stress at the probe/tissue interface, thus improving body acceptance. The use of low Young’s modulus polymers instead of hard substrates is one way to enhance this mechanical mimetism, though compliance can be achieved through a variety of means. The reduction of probe width and thickness in comparison to a designated length, the use of soft hydrogel coatings and the release in device tethering to the skull, can also improve device compliance. Paradoxically, the more compliant the device, the more likely it will fail during the insertion process in the brain. Strategies have multiplied this past decade to offer partial or temporary stiffness to the device to overcome this buckling effect. A detailed description of the probe insertion mechanisms is provided to analyze potential sources of implantation failure and the need for a mechanically-enhancing structure. This leads us to present an overview of the strategies that have been put in place over the last ten years to overcome buckling issues. Particularly, great emphasis is put on bioresorbable polymers and their assessment for neural applications. Finally, a discussion is provided on some of the key features for the design of mechanically-reliable, polymer-based next generation of chronic neuroprosthetic devices.

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

Science.gov (United States)

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

2016-02-01

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

Nanowire electrodes for high-density stimulation and measurement of neural circuits

Directory of Open Access Journals (Sweden)

Jacob T. Robinson

2013-03-01

Full Text Available Brain-machine interfaces (BMIs that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.

Architecture and prototype of human-machine interface with mobile robotic device

International Nuclear Information System (INIS)

Dyumin, A.A.; Sorokoumov, P.S.; Chepin, E.V.; Urvanov, G.A.

2013-01-01

The possibility of controlling mobile robotic (MRD) device is analyzed and a prototype control system is described. It is established that, for controlling MRD, it is expedient to use a brain-computer interface. A system of interpretation of information obtained from the operator brain has been developed and used in the proposed prototype control system [ru

A bidirectional brain-machine interface featuring a neuromorphic hardware decoder

Directory of Open Access Journals (Sweden)

Fabio Boi

2016-12-01

Full Text Available Bidirectional brain-machine interfaces (BMIs establish a two-way direct communication link4 between the brain and the external world. A decoder translates recorded neural activity into motor5 commands and an encoder delivers sensory information collected from the environment directly6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky7 external devices. However, the clinical support of patients with severe motor and sensory deficits8 requires compact, low-power, and fully implantable systems that can decode neural signals to9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits.12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn13 to decode neural signals recorded from the brain into motor outputs controlling the movements14 of an external device. The modularity of the BMI allowed us to tune the individual components15 of the setup without modifying the whole system. In this paper we present the features of16 this modular BMI, and describe how we configured the network of spiking neuron circuits to17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is21 mature enough for the development of BMI modules that are sufficiently low-power and compact,22 while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

Science.gov (United States)

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

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.

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

Directory of Open Access Journals (Sweden)

Pyungwoo Yeon

2016-09-01

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

Comparison of Mono-, Bi-, and Tripolar Configurations for Stimulation and Recording With an Interfascicular Interface.

Science.gov (United States)

Nielsen, Thomas N; Sevcencu, Cristian; Struijk, Johannes J

2014-01-01

Previous studies have indicated that electrodes placed between fascicles can provide nerve recruitment with high topological selectivity if the areas of interest in the nerve are separated with passive elements. In this study, we investigated if this separation of fascicles also can provide topologically selective nerve recordings and compared the performance of mono-, bi-, and tripolar configurations for stimulation and recording with an intra-neural interface. The interface was implanted in the sciatic nerve of 10 rabbits and achieved a median selectivity of Ŝ=0.98-0.99 for all stimulation configurations, while recording selectivity configurations was in the range of Ŝ=0.70-0.80 with the monopolar configuration providing the lowest and the average reference configuration the highest recording selectivity. Interfascicular electrodes could provide an interesting addition to the bulk of peripheral nerve interfaces available for neural prosthetic devices. The separation of the nerve into chambers by the passive elements of the electrode could ensure a higher selectivity than comparable cuff electrodes and the intra-neural location could provide an option of targeting mainly central fascicles. Further studies are, however, still required to develop biocompatible electrodes and test their stability and safety in chronic experiments.

An introduction to neural networks surgery, a field of neuromodulation which is based on advances in neural networks science and digitised brain imaging.

Science.gov (United States)

Sakas, D E; Panourias, I G; Simpson, B A

2007-01-01

Operative Neuromodulation is the field of altering electrically or chemically the signal transmission in the nervous system by implanted devices in order to excite, inhibit or tune the activities of neurons or neural networks and produce therapeutic effects. The present article reviews relevant literature on procedures or devices applied either in contact with the cerebral cortex or cranial nerves or in deep sites inside the brain in order to treat various refractory neurological conditions such as: a) chronic pain (facial, somatic, deafferentation, phantom limb), b) movement disorders (Parkinson's disease, dystonia, Tourette syndrome), c) epilepsy, d) psychiatric disease, e) hearing deficits, and f) visual loss. These data indicate that in operative neuromodulation, a new field emerges that is based on neural networks research and on advances in digitised stereometric brain imaging which allow precise localisation of cerebral neural networks and their relay stations; this field can be described as Neural networks surgery because it aims to act extrinsically or intrinsically on neural networks and to alter therapeutically the neural signal transmission with the use of implantable electrical or electronic devices. The authors also review neurotechnology literature relevant to neuroengineering, nanotechnologies, brain computer interfaces, hybrid cultured probes, neuromimetics, neuroinformatics, neurocomputation, and computational neuromodulation; the latter field is dedicated to the study of the biophysical and mathematical characteristics of electrochemical neuromodulation. The article also brings forward particularly interesting lines of research such as the carbon nanofibers electrode arrays for simultaneous electrochemical recording and stimulation, closed-loop systems for responsive neuromodulation, and the intracortical electrodes for restoring hearing or vision. The present review of cerebral neuromodulatory procedures highlights the transition from the

VAPOR SAMPLING DEVICE FOR INTERFACE WITH MICROTOX ASSAY FOR SCREENING TOXIC INDUSTRIAL CHEMICALS

Science.gov (United States)

A time-integrated sampling system interfaced with a toxicity-based assay is reported for monitoring volatile toxic industrial chemicals (TICs). Semipermeable membrane devices (SPMDs) using dimethyl sulfoxide (DMSO) as the fill solvent accumulated each of 17 TICs from the vapor...

Resolving Overlimiting Current Mechanisms in Microchannel-Nanochannel Interface Devices

Science.gov (United States)

Yossifon, Gilad; Leibowitz, Neta; Liel, Uri; Schiffbauer, Jarrod; Park, Sinwook

2015-11-01

We present results demonstrating the space charge-mediated transition between classical, diffusion-limited current and surface-conduction dominant over-limiting currents in a shallow micro-nanochannel device. The extended space charge layer develops at the depleted micro-nanochannel entrance at high current and is correlated with a distinctive maximum in the dc resistance. Experimental results for a shallow surface-conduction dominated system are compared with theoretical models, allowing estimates of the effective surface charge at high voltage to be obtained. Further, we extend the study to microchannels of moderate to large depths where the role of various electro-convection mechanisms becomes dominant. In particular, electro-osmotic of the second kind and electro-osmotic instability (EOI) which competes each other at geometrically heterogeneous (e.g. undulated nanoslot interface, array of nanoslots) nanoslot devices. Also, these effects are also shown to be strongly modulated by the non-ideal permselectivity of the nanochannel.

Design principle for efficient charge separation at the donor-acceptor interface for high performance organic solar cell device

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Nie, Wanyi; Gupta, Gautam; Crone, Brian; Wang, Hsing-Lin; Mohite, Aditya; MPA-11 Material synthesis and integrated device Team; MPA-chemistry Team

2014-03-01

The performance of donor (D) /acceptor (A) structure based organic electronic devices, such as solar cell, light emitting devices etc., relays on the charge transfer process at the interface dramatically. In organic solar cell, the photo-induced electron-hole pair is tightly bonded and will form a charge transfer (CT) state at the D/A interface after dissociation. There is a large chance for them to recombine through CT state and thus is a major loss that limit the overall performance. Here, we report three different strategies that allow us to completely suppress the exciplex (or charge transfer state) recombination between any D/A system. We observe that the photocurrent increases by 300% and the power conversion efficiency increases by 4-5 times simply by inserting a spacer layer in the form of an a) insulator b) Oliogomer or using a c) heavy atom at the donor-acceptor interface in a P3HT/C60 bilayer device. By using those different functional mono layers, we successfully suppressed the exciplex recombination in evidence of increased photocurrent and open circuit voltage. Moreover, these strategies are applicable universally to any donor-acceptor interface. And we demonstrated such strategies in a bulk-heterojunction device which improved the power conversion efficiency from 3.5% up to 4.6%.

Controllable Spatial Configuration on Cathode Interface for Enhanced Photovoltaic Performance and Device Stability.

Science.gov (United States)

Li, Jiangsheng; Duan, Chenghao; Wang, Ning; Zhao, Chengjie; Han, Wei; Jiang, Li; Wang, Jizheng; Zhao, Yingjie; Huang, Changshui; Jiu, Tonggang

2018-05-08

The molecular structure of cathode interface modification materials can affect the surface morphology of the active layer and key electron transfer processes occurring at the interface of polymer solar cells in inverted structures mostly due to the change of molecular configuration. To investigate the effects of spatial configuration of the cathode interfacial modification layer on polymer solar cells device performances, we introduced two novel organic ionic salts (linear NS2 and three-dimensional (3D) NS4) combined with the ZnO film to fabricate highly efficient inverted solar cells. Both organic ionic salts successfully decreased the surface traps of the ZnO film and made its work function more compatible. Especially NS4 in three-dimensional configuration increased the electron mobility and extraction efficiency of the interfacial film, leading to a significant improvement of device performance. Power conversion efficiency (PCE) of 10.09% based on NS4 was achieved. Moreover, 3D interfacial modification could retain about 92% of its initial PCE over 160 days. It is proposed that 3D interfacial modification retards the element penetration-induced degradation without impeding the electron transfer from the active layer to the ZnO film, which significantly improves device stability. This indicates that inserting three-dimensional organic ionic salt is an efficient strategy to enhance device performance.

A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.

Directory of Open Access Journals (Sweden)

Monzurul Alam

Full Text Available A brain-machine interface (BMI is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.

Autotuning of PID controller by means of human machine interface device

Directory of Open Access Journals (Sweden)

Michał Awtoniuk

2017-06-01

Full Text Available More and more control systems are based on industry microprocessors like PLC controllers (Programmable Logic Controller. The most commonly used control algorithm is PID (Proportional-Integral-Derivative algorithm. Autotuning procedure is not available in every PLC. These controllers are typically used in cooperation with HMI (Human Machine Interface devices. In the study two procedures of autotuning of the PID controller were implemented in the HMI device: step method and relay method. Six tuning rules for step methods and one for relay method were chosen. The autotuning procedures on simulated controlled object and PLC controller without build-in autotuning were tested. The object of control was first order system plus time delay.

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

Science.gov (United States)

Ereifej, Evon S.

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

Chitosan to Connect Biology to Electronics: Fabricating the Bio-Device Interface and Communicating Across This Interface

Directory of Open Access Journals (Sweden)

Eunkyoung Kim

2014-12-01

Full Text Available Individually, advances in microelectronics and biology transformed the way we live our lives. However, there remain few examples in which biology and electronics have been interfaced to create synergistic capabilities. We believe there are two major challenges to the integration of biological components into microelectronic systems: (i assembly of the biological components at an electrode address, and (ii communication between the assembled biological components and the underlying electrode. Chitosan possesses a unique combination of properties to meet these challenges and serve as an effective bio-device interface material. For assembly, chitosan’s pH-responsive film-forming properties allow it to “recognize” electrode-imposed signals and respond by self-assembling as a stable hydrogel film through a cathodic electrodeposition mechanism. A separate anodic electrodeposition mechanism was recently reported and this also allows chitosan hydrogel films to be assembled at an electrode address. Protein-based biofunctionality can be conferred to electrodeposited films through a variety of physical, chemical and biological methods. For communication, we are investigating redox-active catechol-modified chitosan films as an interface to bridge redox-based communication between biology and an electrode. Despite significant progress over the last decade, many questions still remain which warrants even deeper study of chitosan’s structure, properties, and functions.

A bio-inspired memory device based on interfacing Physarum polycephalum with an organic semiconductor

Energy Technology Data Exchange (ETDEWEB)

Romeo, Agostino; Dimonte, Alice; Tarabella, Giuseppe; D’Angelo, Pasquale, E-mail: dangelo@imem.cnr.it, E-mail: iannotta@imem.cnr.it; Erokhin, Victor; Iannotta, Salvatore, E-mail: dangelo@imem.cnr.it, E-mail: iannotta@imem.cnr.it [IMEM-CNR, Institute of Materials for Electronics and Magnetism-National Research Council, Parma 43124 (Italy)

2015-01-01

The development of devices able to detect and record ion fluxes is a crucial point in order to understand the mechanisms that regulate communication and life of organisms. Here, we take advantage of the combined electronic and ionic conduction properties of a conducting polymer to develop a hybrid organic/living device with a three-terminal configuration, using the Physarum polycephalum Cell (PPC) slime mould as a living bio-electrolyte. An over-oxidation process induces a conductivity switch in the polymer, due to the ionic flux taking place at the PPC/polymer interface. This behaviour endows a current-depending memory effect to the device.

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke

OpenAIRE

McConnell, Alistair C; Moioli, Renan C; Brasil, Fabricio L; Vallejo, Marta; Corne, David W; Vargas, Patricia A; Stokes, Adam A

2017-01-01

OBJECTIVE: To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation.METHODS: The development progression of ro...

Non-invasive neural stimulation

Science.gov (United States)

Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

2017-05-01

Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

Effects of oxide traps, interface traps, and ''border traps'' on metal-oxide-semiconductor devices

International Nuclear Information System (INIS)

Fleetwood, D.M.; Winokur, P.S.; Reber, R.A. Jr.; Meisenheimer, T.L.; Schwank, J.R.; Shaneyfelt, M.R.; Riewe, L.C.

1993-01-01

We have identified several features of the 1/f noise and radiation response of metal-oxide-semiconductor (MOS) devices that are difficult to explain with standard defect models. To address this issue, and in response to ambiguities in the literature, we have developed a revised nomenclature for defects in MOS devices that clearly distinguishes the language used to describe the physical location of defects from that used to describe their electrical response. In this nomenclature, ''oxide traps'' are simply defects in the SiO 2 layer of the MOS structure, and ''interface traps'' are defects at the Si/SiO 2 interface. Nothing is presumed about how either type of defect communicates with the underlying Si. Electrically, ''fixed states'' are defined as trap levels that do not communicate with the Si on the time scale of the measurements, but ''switching states'' can exchange charge with the Si. Fixed states presumably are oxide traps in most types of measurements, but switching states can either be interface traps or near-interfacial oxide traps that can communicate with the Si, i.e., ''border traps'' [D. M. Fleetwood, IEEE Trans. Nucl. Sci. NS-39, 269 (1992)]. The effective density of border traps depends on the time scale and bias conditions of the measurements. We show the revised nomenclature can provide focus to discussions of the buildup and annealing of radiation-induced charge in non-radiation-hardened MOS transistors, and to changes in the 1/f noise of MOS devices through irradiation and elevated-temperature annealing

Design and manufacturing challenges of optogenetic neural interfaces: a review

Science.gov (United States)

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

2017-08-01

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

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

Science.gov (United States)

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

2017-04-01

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

A bio-inspired memory device based on interfacing Physarum polycephalum with an organic semiconductor

Directory of Open Access Journals (Sweden)

Agostino Romeo

2015-01-01

Full Text Available The development of devices able to detect and record ion fluxes is a crucial point in order to understand the mechanisms that regulate communication and life of organisms. Here, we take advantage of the combined electronic and ionic conduction properties of a conducting polymer to develop a hybrid organic/living device with a three-terminal configuration, using the Physarum polycephalum Cell (PPC slime mould as a living bio-electrolyte. An over-oxidation process induces a conductivity switch in the polymer, due to the ionic flux taking place at the PPC/polymer interface. This behaviour endows a current-depending memory effect to the device.

Electrical and optical properties of diketopyrrolopyrrole-based copolymer interfaces in thin film devices.

Science.gov (United States)

Adil, Danish; Kanimozhi, Catherine; Ukah, Ndubuisi; Paudel, Keshab; Patil, Satish; Guha, Suchi

2011-05-01

Two donor-acceptor diketopyrrolopyrrole (DPP)-based copolymers (PDPP-BBT and TDPP-BBT) have been synthesized for their application in organic devices such as metal-insulator semiconductor (MIS) diodes and field-effect transistors (FETs). The semiconductor-dielectric interface was characterized by capacitance-voltage and conductance-voltage methods. These measurements yield an interface trap density of 4.2 × 10(12) eV⁻¹ cm⁻² in TDPP-BBT and 3.5 × 10¹² eV⁻¹ cm⁻² in PDPP-BBT at the flat-band voltage. The FETs based on these spincoated DPP copolymers display p-channel behavior with hole mobilities of the order 10⁻³ cm²/(Vs). Light scattering studies from PDPP-BBT FETs show almost no change in the Raman spectrum after the devices are allowed to operate at a gate voltage, indicating that the FETs suffer minimal damage due to the metal-polymer contact or the application of an electric field. As a comparison Raman intensity profile from the channel-Au contact layer in pentacene FETs are presented, which show a distinct change before and after biasing.

Upper limb functional electrical stimulation devices and their man-machine interfaces.

Science.gov (United States)

Venugopalan, L; Taylor, P N; Cobb, J E; Swain, I D

2015-01-01

Functional Electrical Stimulation (FES) is a technique that uses electricity to activate the nerves of a muscle that is paralysed due to hemiplegia, multiple sclerosis, Parkinson's disease or spinal cord injury (SCI). FES has been widely used to restore upper limb functions in people with hemiplegia and C5-C7 tetraplegia and has improved their ability to perform their activities of daily living (ADL). At the time of writing, a detailed literature review of the existing upper limb FES devices and their man-machine interfaces (MMI) showed that only the NESS H200 was commercially available. However, the rigid arm splint doesn't fit everyone and prevents the use of a tenodesis grip. Hence, a robust and versatile upper limb FES device that can be used by a wider group of people is required.

Low Defect Density Substrates and High-Quality Epi-Substrate Interfaces for ABCS Devices and Progress Toward Phonon-Mediated THz Lasers

National Research Council Canada - National Science Library

Goodhue, William; Bliss, David; Krishnaswami, Kannan; Vangala, Shivashankar; Li, Jin; Zhu, Beihong

2005-01-01

... has been developing technology for producing low defect density substrates and high-quality epi-substrate interfaces for ABCS device applications as well as developing fabrication and device concepts...

Keeping Disability in Mind: A Case Study in Implantable Brain-Computer Interface Research.

Science.gov (United States)

Sullivan, Laura Specker; Klein, Eran; Brown, Tim; Sample, Matthew; Pham, Michelle; Tubig, Paul; Folland, Raney; Truitt, Anjali; Goering, Sara

2018-04-01

Brain-Computer Interface (BCI) research is an interdisciplinary area of study within Neural Engineering. Recent interest in end-user perspectives has led to an intersection with user-centered design (UCD). The goal of user-centered design is to reduce the translational gap between researchers and potential end users. However, while qualitative studies have been conducted with end users of BCI technology, little is known about individual BCI researchers' experience with and attitudes towards UCD. Given the scientific, financial, and ethical imperatives of UCD, we sought to gain a better understanding of practical and principled considerations for researchers who engage with end users. We conducted a qualitative interview case study with neural engineering researchers at a center dedicated to the creation of BCIs. Our analysis generated five themes common across interviews. The thematic analysis shows that participants identify multiple beneficiaries of their work, including other researchers, clinicians working with devices, device end users, and families and caregivers of device users. Participants value experience with device end users, and personal experience is the most meaningful type of interaction. They welcome (or even encourage) end-user input, but are skeptical of limited focus groups and case studies. They also recognize a tension between creating sophisticated devices and developing technology that will meet user needs. Finally, interviewees espouse functional, assistive goals for their technology, but describe uncertainty in what degree of function is "good enough" for individual end users. Based on these results, we offer preliminary recommendations for conducting future UCD studies in BCI and neural engineering.

Improvement of design of a surgical interface using an eye tracking device.

Science.gov (United States)

Erol Barkana, Duygun; Açık, Alper; Duru, Dilek Goksel; Duru, Adil Deniz

2014-05-07

Surgical interfaces are used for helping surgeons in interpretation and quantification of the patient information, and for the presentation of an integrated workflow where all available data are combined to enable optimal treatments. Human factors research provides a systematic approach to design user interfaces with safety, accuracy, satisfaction and comfort. One of the human factors research called user-centered design approach is used to develop a surgical interface for kidney tumor cryoablation. An eye tracking device is used to obtain the best configuration of the developed surgical interface. Surgical interface for kidney tumor cryoablation has been developed considering the four phases of user-centered design approach, which are analysis, design, implementation and deployment. Possible configurations of the surgical interface, which comprise various combinations of menu-based command controls, visual display of multi-modal medical images, 2D and 3D models of the surgical environment, graphical or tabulated information, visual alerts, etc., has been developed. Experiments of a simulated cryoablation of a tumor task have been performed with surgeons to evaluate the proposed surgical interface. Fixation durations and number of fixations at informative regions of the surgical interface have been analyzed, and these data are used to modify the surgical interface. Eye movement data has shown that participants concentrated their attention on informative regions more when the number of displayed Computer Tomography (CT) images has been reduced. Additionally, the time required to complete the kidney tumor cryoablation task by the participants had been decreased with the reduced number of CT images. Furthermore, the fixation durations obtained after the revision of the surgical interface are very close to what is observed in visual search and natural scene perception studies suggesting more efficient and comfortable interaction with the surgical interface. The

Efficient universal computing architectures for decoding neural activity.

Directory of Open Access Journals (Sweden)

Benjamin I Rapoport

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

Spectromicroscopic Insights into the Morphology and Interfaces of Operational Organic Electronic Devices

OpenAIRE

Du, Xiaoyan

2017-01-01

Organic electronics, e.g., organic field-effect transistors (OFETs), organic solar cells (OSCs) and organic light-emitting diodes (OLEDs), have attracted strong interest in both academia and industry during the last decades due to their unique capabilities offered by organic semiconductors. The micro-/nano-structures in active layers and the interface engineering in organic electronics are extremely important for desired device functionalities. In this thesis, the structure-function relations...

Rapid Prototyping Human Interfaces Using Stretchable Strain Sensor

Directory of Open Access Journals (Sweden)

Tokiya Yamaji

2017-01-01

Full Text Available In the modern society with a variety of information electronic devices, human interfaces increase their importance in a boundary of a human and a device. In general, the human is required to get used to the device. Even if the device is designed as a universal device or a high-usability device, the device is not suitable for all users. The usability of the device depends on the individual user. Therefore, personalized and customized human interfaces are effective for the user. To create customized interfaces, we propose rapid prototyping human interfaces using stretchable strain sensors. The human interfaces comprise parts formed by a three-dimensional printer and the four strain sensors. The three-dimensional printer easily makes customized human interfaces. The outputs of the interface are calculated based on the sensor’s lengths. Experiments evaluate three human interfaces: a sheet-shaped interface, a sliding lever interface, and a tilting lever interface. We confirm that the three human interfaces obtain input operations with a high accuracy.

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

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

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

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

Science.gov (United States)

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

2010-01-01

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

DLTS Analysis and Interface Engineering of Solution Route Fabricated Zirconia Based MIS Devices Using Plasma Treatment

Science.gov (United States)

Kumar, Arvind; Mondal, Sandip; Koteswara Rao, K. S. R.

2018-02-01

In this work, we have fabricated low-temperature sol-gel spin-coated and oxygen (O2) plasma treated ZrO2 thin film-based metal-insulator-semiconductor devices. To understand the impact of plasma treatment on the Si/ZrO2 interface, deep level transient spectroscopy measurements were performed. It is reported that the interface state density ( D it) comes down to 7.1 × 1010 eV-1 cm-2 from 4 × 1011 eV-1 cm-2, after plasma treatment. The reduction in D it is around five times and can be attributed to the passivation of oxygen vacancies near the Si/ZrO2 interface, as they try to relocate near the interface. The energy level position ( E T) of interfacial traps is estimated to be 0.36 eV below the conduction band edge. The untreated ZrO2 film displayed poor leakage behavior due to the presence of several traps within the film and at the interface; O2 plasma treated films show improved leakage current density as they have been reduced from 5.4 × 10-8 A/cm2 to 1.98 × 10-9 A/cm2 for gate injection mode and 6.4 × 10-8 A/cm2 to 6.3 × 10-10 A/cm2 for substrate injection mode at 1 V. Hence, we suggest that plasma treatment might be useful in future device fabrication technology.

Design Program in Graphic User Interface Environment for Automobile ER Devices

Science.gov (United States)

Lim, S. C.; Park, J. S.; Sohn, J. W.; Choi, S. B.

This work presents a design and analysis program for vehicle devices utilizing an electrorheological (ER) fluid. The program is operated in graphic user interface (GUI) environment and the initial window is consisted of four subprogram modules which are related to ER shock absorber, ER seat damper, ER engine mount, and ER anti-lock brake system (ABS), respectively. In order to execute each module, both material properties and design parameters are to be chosen by the user. Then, the output display window shows the field-dependent performance characteristics to be considered as design criteria. In addition, control performances of the vehicle system equipped with ER devices are displayed in time and frequency domain. In order to demonstrate the effectiveness of the proposed program, ER shock absorber and ER ABS are designed and manufactured and their performance characteristics are evaluated.

Theta phase precession and phase selectivity: a cognitive device description of neural coding

Science.gov (United States)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to

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

NARCIS (Netherlands)

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

2010-01-01

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

Fast and Efficient Radiological Interventions via a Graphical User Interface Commanded Magnetic Resonance Compatible Robotic Device

Science.gov (United States)

Özcan, Alpay; Christoforou, Eftychios; Brown, Daniel; Tsekos, Nikolaos

2011-01-01

The graphical user interface for an MR compatible robotic device has the capability of displaying oblique MR slices in 2D and a 3D virtual environment along with the representation of the robotic arm in order to swiftly complete the intervention. Using the advantages of the MR modality the device saves time and effort, is safer for the medical staff and is more comfortable for the patient. PMID:17946067

Neural network-based sensor signal accelerator.

Energy Technology Data Exchange (ETDEWEB)

Vogt, M. C.

2000-10-16

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

Electronic Processes at Organic−Organic Interfaces: Insight from Modeling and Implications for Opto-electronic Devices †

KAUST Repository

Beljonne, David; Cornil, Jérôme; Muccioli, Luca; Zannoni, Claudio; Brédas, Jean-Luc; Castet, Frédéric

2011-01-01

We report on the recent progress achieved in modeling the electronic processes that take place at interfaces between π-conjugated materials in organic opto-electronic devices. First, we provide a critical overview of the current computational

Single-crystal charge transfer interfaces for efficient photonic devices (Conference Presentation)

Science.gov (United States)

Alves, Helena; Pinto, Rui M.; Maçôas, Ermelinda M. S.; Baleizão, Carlos; Santos, Isabel C.

2016-09-01

Organic semiconductors have unique optical, mechanical and electronic properties that can be combined with customized chemical functionality. In the crystalline form, determinant features for electronic applications such as molecular purity, the charge mobility or the exciton diffusion length, reveal a superior performance when compared with materials in a more disordered form. Combining crystals of two different conjugated materials as even enable a new 2D electronic system. However, the use of organic single crystals in devices is still limited to a few applications, such as field-effect transistors. In 2013, we presented the first system composed of single-crystal charge transfer interfaces presenting photoconductivity behaviour. The system composed of rubrene and TCNQ has a responsivity reaching 1 A/W, corresponding to an external quantum efficiency of nearly 100%. A similar approach, with a hybrid structure of a PCBM film and rubrene single crystal also presents high responsivity and the possibility to extract excitons generated in acceptor materials. This strategy led to an extended action towards the near IR. By adequate material design and structural organisation of perylediimides, we demonstrate that is possible to improve exciton diffusion efficiency. More recently, we have successfully used the concept of charge transfer interfaces in phototransistors. These results open the possibility of using organic single-crystal interfaces in photonic applications.

Authenticated sensor interface device

Energy Technology Data Exchange (ETDEWEB)

Coleman, Jody Rustyn; Poland, Richard W.

2018-05-01

A system and method for the secure storage and transmission of data is provided. A data aggregate device can be configured to receive secure data from a data source, such as a sensor, and encrypt the secure data using a suitable encryption technique, such as a shared private key technique, a public key encryption technique, a Diffie-Hellman key exchange technique, or other suitable encryption technique. The encrypted secure data can be provided from the data aggregate device to different remote devices over a plurality of segregated or isolated data paths. Each of the isolated data paths can include an optoisolator that is configured to provide one-way transmission of the encrypted secure data from the data aggregate device over the isolated data path. External data can be received through a secure data filter which, by validating the external data, allows for key exchange and other various adjustments from an external source.

An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.

Science.gov (United States)

He, Yongtian; Nathan, Kevin; Venkatakrishnan, Anusha; Rovekamp, Roger; Beck, Christopher; Ozdemir, Recep; Francisco, Gerard E; Contreras-Vidal, Jose L

2014-01-01

Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASA's powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals--the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton--is demonstrated.

Brain-computer interfaces increase whole-brain signal to noise.

Science.gov (United States)

Papageorgiou, T Dorina; Lisinski, Jonathan M; McHenry, Monica A; White, Jason P; LaConte, Stephen M

2013-08-13

Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

Motorized CPM/CAM physiotherapy device with sliding-mode Fuzzy Neural Network control loop.

Science.gov (United States)

Ho, Hung-Jung; Chen, Tien-Chi

2009-11-01

Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices promote rehabilitation of damaged joints. This paper presents a computerized CPM/CAM system that obviates the need for mechanical resistance devices such as springs. The system is controlled by a computer which performs sliding-mode Fuzzy Neural Network (FNN) calculations online. CAM-type resistance force is generated by the active performance of an electric motor which is controlled so as to oppose the motion of the patient's leg. A force sensor under the patient's foot on the device pedal provides data for feedback in a sliding-mode FNN control loop built around the motor. Via an active impedance control feedback system, the controller drives the motor to behave similarly to a damped spring by generating and controlling the amplitude and direction of the pedal force in relation to the patient's leg. Experiments demonstrate the high sensitivity and speed of the device. The PC-based feedback nature of the control loop means that sophisticated auto-adaptable CPM/CAM custom-designed physiotherapy becomes possible. The computer base also allows extensive data recording, data analysis and network-connected remote patient monitoring.

Evolvable Neural Software System

Science.gov (United States)

Curtis, Steven A.

2009-01-01

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

sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Tatsiana Malechka

2015-02-01

Full Text Available Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI. In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP-BCI, an Event-related (de-synchronization (ERD/ERS-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

Substrate dependence of energy level alignment at the donor-acceptor interface in organic photovoltaic devices

International Nuclear Information System (INIS)

Zhou, Y.C.; Liu, Z.T.; Tang, J.X.; Lee, C.S.; Lee, S.T.

2009-01-01

The interface energy level alignment between copper phthalocyanine (CuPC) and fullerene (C60), the widely studied donor-acceptor pair in organic photovoltaics (OPVs), on indium-tin oxide (ITO) and Mg substrate was investigated. The CuPC/C60 interface formed on ITO shows a nearly common vacuum level, but a dipole and band bending exist, resulting in a 0.8 eV band offset at the same interface on Mg. This observation indicates that the energy difference between the highest occupied molecular orbital of CuPC and the lowest unoccupied molecular orbital of C60, which dictates the open circuit voltage of the CuPC/C60 OPV, can be tuned by the work function of the substrate. Furthermore, the substrate effect on the energy alignment at the donor/acceptor interface can satisfactorily explain that a device with an anode of a smaller work function can provide a higher open circuit voltage.

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

Science.gov (United States)

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

2012-01-01

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

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

Directory of Open Access Journals (Sweden)

William Taube Navaraj

2017-09-01

Full Text Available This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs based hardware-implementable neural network (HNN approach for tactile data processing in electronic skin (e-skin. The viability of Si nanowires (NWs as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals.

Microfluidic electrochemical device and process for chemical imaging and electrochemical analysis at the electrode-liquid interface in-situ

Science.gov (United States)

Yu, Xiao-Ying; Liu, Bingwen; Yang, Li; Zhu, Zihua; Marshall, Matthew J.

2016-03-01

A microfluidic electrochemical device and process are detailed that provide chemical imaging and electrochemical analysis under vacuum at the surface of the electrode-sample or electrode-liquid interface in-situ. The electrochemical device allows investigation of various surface layers including diffuse layers at selected depths populated with, e.g., adsorbed molecules in which chemical transformation in electrolyte solutions occurs.

Neural Stem Cell Differentiation Using Microfluidic Device-Generated Growth Factor Gradient.

Science.gov (United States)

Kim, Ji Hyeon; Sim, Jiyeon; Kim, Hyun-Jung

2018-04-11

Neural stem cells (NSCs) have the ability to self-renew and differentiate into multiple nervous system cell types. During embryonic development, the concentrations of soluble biological molecules have a critical role in controlling cell proliferation, migration, differentiation and apoptosis. In an effort to find optimal culture conditions for the generation of desired cell types in vitro , we used a microfluidic chip-generated growth factor gradient system. In the current study, NSCs in the microfluidic device remained healthy during the entire period of cell culture, and proliferated and differentiated in response to the concentration gradient of growth factors (epithermal growth factor and basic fibroblast growth factor). We also showed that overexpression of ASCL1 in NSCs increased neuronal differentiation depending on the concentration gradient of growth factors generated in the microfluidic gradient chip. The microfluidic system allowed us to study concentration-dependent effects of growth factors within a single device, while a traditional system requires multiple independent cultures using fixed growth factor concentrations. Our study suggests that the microfluidic gradient-generating chip is a powerful tool for determining the optimal culture conditions.

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

Directory of Open Access Journals (Sweden)

Muthukannan K

2013-11-01

Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

Enhanced thermal stability of RuO2/polyimide interface for flexible device applications

Science.gov (United States)

Music, Denis; Schmidt, Paul; Chang, Keke

2017-09-01

We have studied the thermal stability of RuO2/polyimide (Kapton) interface using experimental and theoretical methods. Based on calorimetric and spectroscopic analyses, this inorganic-organic system does not exhibit any enthalpic peaks as well as all bonds in RuO2 and Kapton are preserved up to 500 °C. In addition, large-scale density functional theory based molecular dynamics, carried out in the same temperature range, validates the electronic structure and points out that numerous Ru-C and a few Ru-O covalent/ionic bonds form across the RuO2/Kapton interface. This indicates strong adhesion, but there is no evidence of Kapton degradation upon thermal excitation. Furthermore, RuO2 does not exhibit any interfacial bonds with N and H in Kapton, providing additional evidence for the thermal stability notion. It is suggested that the RuO2/Kapton interface is stable due to aromatic architecture of Kapton. This enhanced thermal stability renders Kapton an appropriate polymeric substrate for RuO2 containing systems in various applications, especially for flexible microelectronic and energy devices.

Fabrication and interfacing of nanochannel devices for single-molecule studies

International Nuclear Information System (INIS)

Hoang, H T; Berenschot, J W; De Boer, M J; Tas, N R; Haneveld, J; Elwenspoek, M C; Segers-Nolten, I M

2009-01-01

Nanochannel devices have been fabricated using standard micromachining techniques such as optical lithography, deposition and etching. 1D nanochannels with thin glass capping and through-wafer inlet/outlet ports were constructed. 2D nanochannels have been made transparent by oxidation of polysilicon channel wall for optical detection and these fragile channels were successfully connected to macro inlet ports. The interfacing from the macro world to the nanochannels was especially designed for optical observation of filling liquid inside nanochannels using an inverted microscope. Toward single-molecule studies, individual quantum dots were visualized in 150 nm height 1D nanochannels. The potential of 2D nanochannels for single-molecule studies was shown from a filling experiment with a fluorescent dye solution

Interfacing between concrete and steel construction and fusion research devices

International Nuclear Information System (INIS)

Willoughby, E.

1981-01-01

In 1976 Giffels Associates, Inc. an architect/engineer organization, was retained by the United States Department of Energy to provide Title I and Title II design services and Title III construction inspection services for the Tokamak Fusion Test Reactor now being installed at the Princeton Plasma Physics Laboratory in Princeton, New Jersey. Construction of the complex required to house and serve the reactor itself, designed by others, now commencing. During building construction several problems occurred with respect to the interface between the building design, construction and the fusion device (reactor). A brief description of some of these problems and related factors is presented, which may be of benefit to those persons active in continuing fusion research and experimental work

Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities

Science.gov (United States)

Scholl, Clara A.; Hendrickson, Scott M.; Swett, Bruce A.; Fitch, Michael J.; Walter, Erich C.; McLoughlin, Michael P.; Chevillet, Mark A.; Blodgett, David W.; Hwang, Grace M.

2017-05-01

The development of portable non-invasive brain computer interface technologies with higher spatio-temporal resolution has been motivated by the tremendous success seen with implanted devices. This talk will discuss efforts to overcome several major obstacles to viability including approaches that promise to improve spatial and temporal resolution. Optical approaches in particular will be highlighted and the potential benefits of both Blood-Oxygen Level Dependent (BOLD) and Fast Optical Signal (FOS) will be discussed. Early-stage research into the correlations between neural activity and FOS will be explored.

In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network

Directory of Open Access Journals (Sweden)

Dileep Kumar

2017-01-01

Full Text Available Traditionally permanent acoustic sensors leak detection techniques have been proven to be very effective in water distribution pipes. However, these methods need long distance deployment and proper position of sensors and cannot be implemented on underground pipelines. An inline-inspection acoustic device is developed which consists of acoustic sensors. The device will travel by the flow of water through the pipes which record all noise events and detect small leaks. However, it records all the noise events regarding background noises, but the time domain noisy acoustic signal cannot manifest complete features such as the leak flow rate which does not distinguish the leak signal and environmental disturbance. This paper presents an algorithm structure with the modularity of wavelet and neural network, which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks. This study validates that the time domain is not evident to the complete features regarding noisy leak signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes. The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise and background noise, and by neural network implementation, the method improves the classification performance of extracted features.

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

Science.gov (United States)

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

2017-01-01

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

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.

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

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Yoko Saito

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

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

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Markus A Wenzel

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

im4Things: An Ontology-Based Natural Language Interface for Controlling Devices in the Internet of Things

KAUST Repository

Noguera-Arnaldos, José Ángel

2017-03-14

The Internet of Things (IoT) offers opportunities for new applications and services that enable users to access and control their working and home environment from local and remote locations, aiming to perform daily life activities in an easy way. However, the IoT also introduces new challenges, some of which arise from the large range of devices currently available and the heterogeneous interfaces provided for their control. The control and management of this variety of devices and interfaces represent a new challenge for non-expert users, instead of making their life easier. Based on this understanding, in this work we present a natural language interface for the IoT, which takes advantage of Semantic Web technologies to allow non-expert users to control their home environment through an instant messaging application in an easy and intuitive way. We conducted several experiments with a group of end users aiming to evaluate the effectiveness of our approach to control home appliances by means of natural language instructions. The evaluation results proved that without the need for technicalities, the user was able to control the home appliances in an efficient way.

Powering biomedical devices

CERN Document Server

Romero, Edwar

2013-01-01

From exoskeletons to neural implants, biomedical devices are no less than life-changing. Compact and constant power sources are necessary to keep these devices running efficiently. Edwar Romero's Powering Biomedical Devices reviews the background, current technologies, and possible future developments of these power sources, examining not only the types of biomedical power sources available (macro, mini, MEMS, and nano), but also what they power (such as prostheses, insulin pumps, and muscular and neural stimulators), and how they work (covering batteries, biofluids, kinetic and ther

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

Science.gov (United States)

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

2017-08-01

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

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.

Topological interface states and effects for next generation of innovative devices

International Nuclear Information System (INIS)

Kantser, Valeriu; Carlig, Sergiu

2013-01-01

Topological insulators (TI) have opened a gateway to search new quantum electronic phase of the condensed matter as well as to pave new platform of modern technology. This stems mainly on their unique surface states that are protected by time-reversal symmetry, show the Dirac cones connecting the inverted conduction and valence bands and exhibit unique spin-momentum locking property. Increasing the surface state contribution in proportion to the bulk of material is critical to investigate the surface states and for future innovative device applications. The way to achieve this is to configure topological insulators into nanostructures, which at the same time in combination with others materials significantly enlarge the variety of new states and phenomena. This article reviews the recent progress made in topological insulator nano heterostructures electronic states investigation. The state of art of different new scenario of engineering topologically interface states in the TI heterostructures are revealed, in particular by using polarization fields and antiferromagnetic ordering. Some of new proposals for innovative electronic devices are discussed. (authors)

A fluidic device for the controlled formation and real-time monitoring of soft membranes self-assembled at liquid interfaces.

Science.gov (United States)

Mendoza-Meinhardt, Arturo; Botto, Lorenzo; Mata, Alvaro

2018-02-13

Membrane materials formed at the interface between two liquids have found applications in a large variety of technologies, from sensors to drug-delivery and catalysis. However, studying the formation of these membranes in real-time presents considerable challenges, owing to the difficulty of prescribing the location and instant of formation of the membrane, the difficulty of observing time-dependent membrane shape and thickness, and the poor reproducibility of results obtained using conventional mixing procedures. Here we report a fluidic device that facilitates characterisation of the time-dependent thickness, morphology and mass transport properties of materials self-assembled at fluid-fluid interfaces. In the proposed device the membrane forms from the controlled coalescence of two liquid menisci in a linear open channel. The linear geometry and controlled mixing of the solutions facilitate real-time visualisation, manipulation and improve reproducibility. Because of its small dimensions, the device can be used in conjunction with standard microscopy methods and reduces the required volumes of potentially expensive reagents. As an example application to tissue engineering, we use the device to characterise interfacial membranes formed by supra-molecular self-assembly of peptide-amphiphiles with either an elastin-like-protein or hyaluronic acid. The device can be adapted to study self-assembling membranes for applications that extend beyond bioengineering.

Ethical considerations on novel neuronal interfaces.

Science.gov (United States)

Keskinbora, Kadircan H; Keskinbora, Kader

2018-04-01

Wireless powered implants, each smaller than a grain of rice, have the potential to scan and stimulate brain cells. Further research may lead to next-generation brain-machine interfaces for controlling prosthetics, exoskeletons, and robots, as well as "electroceuticals" to treat disorders of the brain and body. In conditions that can be particularly alleviated with brain stimulation, the use of such mini devices may pose certain challenges. Health professionals are becoming increasingly more accountable in decision-making processes that have impacts on the life quality of individuals. It is possible to transmit such stimulation using remote control principles. Perhaps, the most important concern regarding the use of these devices termed as "neural dust" is represented by the possibility of controlling affection and other mental functions via waves reaching the brain using more advanced versions of such devices. This will not only violate the respect for authority principle of ethics, but also medical ethics, and may potentially lead to certain incidents of varying vehemence that may be considered illegal. Therefore, a sound knowledge and implementation of ethical principles is becoming a more important issue on the part of healthcare professionals. In both the ethical decision-making process and in ethical conflicts, it may be useful to re-appraise the principles of medical ethics. In this article, the ethical considerations of these devices are discussed.

Spiking Neural Networks with Unsupervised Learning Based on STDP Using Resistive Synaptic Devices and Analog CMOS Neuron Circuit.

Science.gov (United States)

Kwon, Min-Woo; Baek, Myung-Hyun; Hwang, Sungmin; Kim, Sungjun; Park, Byung-Gook

2018-09-01

We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, asymmetric negative and positive pulse generation part, a refractory part, and finally a back-propagation pulse generation part for learning of the synaptic devices. The resistive synaptic devices were fabricated using HfOx switching layer by atomic layer deposition (ALD). The resistive synaptic device had gradual set and reset characteristics and the conductance was adjusted by spike-timing-dependent-plasticity (STDP) learning rule. We carried out circuit simulation of synaptic device and CMOS neuron circuit. And we have developed an unsupervised spiking neural networks (SNNs) for 5 × 5 pattern recognition and classification using the neuron circuit and synaptic devices. The hardware-based SNNs can autonomously and efficiently control the weight updates of the synapses between neurons, without the aid of software calculations.

Hydrogel-Electrospun Fiber Mat Composite Coatings for Neural Prostheses

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Ning eHan

2011-03-01

Full Text Available Achieving stable, long-term performance of implanted neural prosthetic devices has been challenging because of implantation related neuron loss and a foreign body response that results in encapsulating glial scar formation. To improve neuron-prosthesis integration and form chronic, stable interfaces, we investigated the potential of neurotrophin-eluting hydrogel-electrospun fiber mat (EFM composite coatings. In particular, poly(ethylene glycol-poly(ε-caprolactone (PEGPCL hydrogel- poly(ε-caprolactone (PCL EFM composites were applied as coatings for multielectrode arrays (MEAs. Coatings were stable and persisted on electrode surfaces for over 1 month under an agarose gel tissue phantom and over 9 months in a PBS immersion bath. To demonstrate drug release, a neurotrophin, nerve growth factor (NGF, was loaded in the PEGPCL hydrogel layer, and coating cytotoxicity and sustained NGF release were evaluated using a PC12 cell culture model. Quantitative MTT assays showed that these coatings had no significant toxicity toward PC12 cells, and neurite extension at day 7 and 14 confirmed sustained release of NGF at biologically significant concentrations for at least 2 weeks. Our results demonstrate that hydrogel-EFM composite materials can be applied to neural prostheses as a means to improve neuron-electrode proximity and enhance long-term device performance and function.

HCIDL: Human-computer interface description language for multi-target, multimodal, plastic user interfaces

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Lamia Gaouar

2018-06-01

Full Text Available From the human-computer interface perspectives, the challenges to be faced are related to the consideration of new, multiple interactions, and the diversity of devices. The large panel of interactions (touching, shaking, voice dictation, positioning … and the diversification of interaction devices can be seen as a factor of flexibility albeit introducing incidental complexity. Our work is part of the field of user interface description languages. After an analysis of the scientific context of our work, this paper introduces HCIDL, a modelling language staged in a model-driven engineering approach. Among the properties related to human-computer interface, our proposition is intended for modelling multi-target, multimodal, plastic interaction interfaces using user interface description languages. By combining plasticity and multimodality, HCIDL improves usability of user interfaces through adaptive behaviour by providing end-users with an interaction-set adapted to input/output of terminals and, an optimum layout. Keywords: Model driven engineering, Human-computer interface, User interface description languages, Multimodal applications, Plastic user interfaces

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

Science.gov (United States)

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

EDITORIAL: Commercial opportunities for neural engineers

Science.gov (United States)

Cavuoto, James

2008-03-01

Research and academic professionals in neural engineering know well the promise the field offers for advancing our understanding of basic neuroscience and devising new therapies for treating neurological diseases and disorders. But there is also considerable commercial opportunity for new start-up companies in several areas of neural engineering. The neurotechnology industry, which includes firms that manufacture neuromodulation devices, neural prostheses, neurorehabilitation systems, and neurosensing devices, is forecast to grow to grow from 3.6 billion this year to 8.8 billion in 2012, according to a recently published market research study from Neurotech Reports. In recent years, there have been several successful spinoffs of neurotechnology startup firms that originated with research at universities and clinical institutions. In many cases, the academic researchers who invented the new technology or product innovation have stayed on with their startup firms after receiving funding from venture capital firms, or after going public. Among the most successful neurotechnology industry spinoffs in recent years were: Cyberkinetics Inc., Foxborough, MA, a manufacturer of brain-computer interface devices based on research at Brown University. John Donoghue, a professor and chairman of the department of neuroscience at Brown University and executive director of the brain science program at Brown, founded the company in 2001 and remains on board as the chief scientific officer. Synapse Biomedical, Inc., Oberlin, OH, a manufacturer of diaphragm pacing systems, based on research at Case Western Reserve University. Raymond Onders, director of minimal invasive surgery and associate professor at University Hospital Case Medical Center in Cleveland, was the primary researcher. He was helped by J. Thomas Mortimer, professor emeritus of biomedical engineering at Case, and it was Mortimer who came up with the name NeuRx for the device. Onders performed his first surgical implant

Neural prostheses in clinical applications--trends from precision mechanics towards biomedical microsystems in neurological rehabilitation.

Science.gov (United States)

Stieglitz, T; Schuettler, M; Koch, K P

2004-04-01

Neural prostheses partially restore body functions by technical nerve excitation after trauma or neurological diseases. External devices and implants have been developed since the early 1960s for many applications. Several systems have reached nowadays clinical practice: Cochlea implants help the deaf to hear, micturition is induced by bladder stimulators in paralyzed persons and deep brain stimulation helps patients with Parkinson's disease to participate in daily life again. So far, clinical neural prostheses are fabricated with means of precision mechanics. Since microsystem technology opens the opportunity to design and develop complex systems with a high number of electrodes to interface with the nervous systems, the opportunity for selective stimulation and complex implant scenarios seems to be feasible in the near future. The potentials and limitations with regard to biomedical microdevices are introduced and discussed in this paper. Target specifications are derived from existing implants and are discussed on selected applications that has been investigated in experimental research: a micromachined implant to interface a nerve stump with a sieve electrode, cuff electrodes with integrated electronics, and an epiretinal vision prosthesis.

An artifical neural network for detection of simulated dental caries

Energy Technology Data Exchange (ETDEWEB)

Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

2006-08-15

Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

An artifical neural network for detection of simulated dental caries

International Nuclear Information System (INIS)

Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.

2006-01-01

Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

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.

Brain–muscle interface

Indian Academy of Sciences (India)

2011-05-16

May 16, 2011 ... Clipboard: Brain–muscle interface: The next-generation BMI. Radhika Rajan Neeraj Jain ... Keywords. Assistive devices; brain–machine interface; motor cortex; paralysis; spinal cord injury ... Journal of Biosciences | News ...

Syringe-Injectable Electronics with a Plug-and-Play Input/Output Interface.

Science.gov (United States)

Schuhmann, Thomas G; Yao, Jun; Hong, Guosong; Fu, Tian-Ming; Lieber, Charles M

2017-09-13

Syringe-injectable mesh electronics represent a new paradigm for brain science and neural prosthetics by virtue of the stable seamless integration of the electronics with neural tissues, a consequence of the macroporous mesh electronics structure with all size features similar to or less than individual neurons and tissue-like flexibility. These same properties, however, make input/output (I/O) connection to measurement electronics challenging, and work to-date has required methods that could be difficult to implement by the life sciences community. Here we present a new syringe-injectable mesh electronics design with plug-and-play I/O interfacing that is rapid, scalable, and user-friendly to nonexperts. The basic design tapers the ultraflexible mesh electronics to a narrow stem that routes all of the device/electrode interconnects to I/O pads that are inserted into a standard zero insertion force (ZIF) connector. Studies show that the entire plug-and-play mesh electronics can be delivered through capillary needles with precise targeting using microliter-scale injection volumes similar to the standard mesh electronics design. Electrical characterization of mesh electronics containing platinum (Pt) electrodes and silicon (Si) nanowire field-effect transistors (NW-FETs) demonstrates the ability to interface arbitrary devices with a contact resistance of only 3 Ω. Finally, in vivo injection into mice required only minutes for I/O connection and yielded expected local field potential (LFP) recordings from a compact head-stage compatible with chronic studies. Our results substantially lower barriers for use by new investigators and open the door for increasingly sophisticated and multifunctional mesh electronics designs for both basic and translational studies.

Reactivity and morphology of vapor-deposited Al/polymer interfaces for organic semiconductor devices

International Nuclear Information System (INIS)

Demirkan, K.; Mathew, A.; Weiland, C.; Opila, R. L.; Reid, M.

2008-01-01

The chemistry and the morphology of metal-deposited organic semiconductor interfaces play a significant role in determining the performance and reliability of organic semiconductor devices. We investigated the aluminum metallization of poly(2-methoxy-5,2 ' -ethyl-hexyloxy-phenylene vinylene) (MEH-PPV), polystyrene, and ozone-treated polystyrene surfaces by chemical (x-ray and ultraviolet photoelectron spectroscopy) and microscopic [atomic force microscopy, scanning electron microscopy (SEM), focused ion beam (FIB)] analyses. Photoelectron spectroscopy showed the degree of chemical interaction between Al and each polymer; for MEH-PPV, the chemical interactions were mainly through the C-O present in the side chain of the polymer structure. The chemical interaction of aluminum with polystyrene was less significant, but it showed a dramatic increase after ozone treatment of the polystyrene surface (due to the formation of exposed oxygen sites). Results showed a strong relationship between the surface reactivity and the condensation/sticking of the aluminum atoms on the surface. SEM analysis showed that, during the initial stages of the metallization, a significant clustering of aluminum takes place. FIB analysis showed that such clustering yields a notably porous structure. The chemical and the morphological properties of the vapor-deposited Al on organic semiconductor surfaces makes such electrical contacts more complicated. The possible effects of surface chemistry and interface morphology on the electrical properties and reliability of organic semiconductor devices are discussed in light of the experimental findings

EDITORIAL: Why we need a new journal in neural engineering

Science.gov (United States)

Durand, Dominique M.

2004-03-01

The field of neural engineering crystallizes for many engineers and scientists an area of research at the interface between neuroscience and engineering. For the last 15 years or so, the discipline of neural engineering (neuroengineering) has slowly appeared at conferences as a theme or track. The first conference devoted entirely to this area was the 1st International IEEE EMBS Conference on Neural Engineering which took place in Capri, Italy in 2003. Understanding how the brain works is considered the ultimate frontier and challenge in science. The complexity of the brain is so great that understanding even the most basic functions will require that we fully exploit all the tools currently at our disposal in science and engineering and simultaneously develop new methods of analysis. While neuroscientists and engineers from varied fields such as brain anatomy, neural development and electrophysiology have made great strides in the analysis of this complex organ, there remains a great deal yet to be uncovered. The potential for applications and remedies deriving from scientific discoveries and breakthroughs is extremely high. As a result of the growing availability of micromachining technology, research into neurotechnology has grown relatively rapidly in recent years and appears to be approaching a critical mass. For example, by understanding how neuronal circuits process and store information, we could design computers with capabilities beyond current limits. By understanding how neurons develop and grow, we could develop new technologies for spinal cord repair or central nervous system repair following neurological disorders. Moreover, discoveries related to higher-level cognitive function and consciousness could have a profound influence on how humans make sense of their surroundings and interact with each other. The ability to successfully interface the brain with external electronics would have enormous implications for our society and facilitate a

Analysis of buried interfaces in multilayer device structures with hard XPS (HAXPES) using a CrKα source

DEFF Research Database (Denmark)

Renault, O.; Martinez, E.; Zborowski, C.

2018-01-01

Applications of laboratory hard X-ray photoelectron spectroscopy on buried interfaces in devices are presented. We use a novel spectrometer fitted with a monochromated CrKα source (photon energy: 5414.9 eV) and a high-voltage analyzer. Elements buried at depths as deep as 25 nm underneath various...

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

Science.gov (United States)

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

2006-01-01

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

Investigation of the GaN-on-GaAs interface for vertical power device applications

International Nuclear Information System (INIS)

Möreke, Janina; Uren, Michael J.; Kuball, Martin; Novikov, Sergei V.; Foxon, C. Thomas; Hosseini Vajargah, Shahrzad; Wallis, David J.; Humphreys, Colin J.; Haigh, Sarah J.; Al-Khalidi, Abdullah; Wasige, Edward; Thayne, Iain

2014-01-01

GaN layers were grown onto (111) GaAs by molecular beam epitaxy. Minimal band offset between the conduction bands for GaN and GaAs materials has been suggested in the literature raising the possibility of using GaN-on-GaAs for vertical power device applications. I-V and C-V measurements of the GaN/GaAs heterostructures however yielded a rectifying junction, even when both sides of the junction were heavily doped with an n-type dopant. Transmission electron microscopy analysis further confirmed the challenge in creating a GaN/GaAs Ohmic interface by showing a large density of dislocations in the GaN layer and suggesting roughening of the GaN/GaAs interface due to etching of the GaAs by the nitrogen plasma, diffusion of nitrogen or melting of Ga into the GaAs substrate.

Investigation of the GaN-on-GaAs interface for vertical power device applications

Energy Technology Data Exchange (ETDEWEB)

Möreke, Janina, E-mail: janina.moereke@bristol.ac.uk; Uren, Michael J.; Kuball, Martin [H.H. Wills Physics Laboratory, Tyndall Avenue, Bristol BS8 1TL (United Kingdom); Novikov, Sergei V.; Foxon, C. Thomas [Department of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD (United Kingdom); Hosseini Vajargah, Shahrzad; Wallis, David J.; Humphreys, Colin J. [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom); Haigh, Sarah J. [Super STEM Laboratory, STFC Daresbury Campus, Keckwick Lane, Daresbury WA4 4AD (United Kingdom); School of Materials, University of Manchester, Manchester M13 9PL (United Kingdom); Al-Khalidi, Abdullah; Wasige, Edward; Thayne, Iain [School of Engineering, University of Glasgow, Rankine Bldg, Oakfield Avenue, Glasgow G12 8LT (United Kingdom)

2014-07-07

GaN layers were grown onto (111) GaAs by molecular beam epitaxy. Minimal band offset between the conduction bands for GaN and GaAs materials has been suggested in the literature raising the possibility of using GaN-on-GaAs for vertical power device applications. I-V and C-V measurements of the GaN/GaAs heterostructures however yielded a rectifying junction, even when both sides of the junction were heavily doped with an n-type dopant. Transmission electron microscopy analysis further confirmed the challenge in creating a GaN/GaAs Ohmic interface by showing a large density of dislocations in the GaN layer and suggesting roughening of the GaN/GaAs interface due to etching of the GaAs by the nitrogen plasma, diffusion of nitrogen or melting of Ga into the GaAs substrate.

Visual communication interface for severe physically disabled patients

Science.gov (United States)

Savino, M. J.; Fernández, E. A.

2007-11-01

During the last years several interfaces have been developed to allow communication to those patients suffering serious physical disabilities. In this work, a computer based communication interface is presented. It was designed to allow communication to those patients that cannot use neither their hands nor their voice but they can do it through their eyes. The system monitors the eyes movements by means of a webcam. Then, by means of an Artificial Neural Network, the system allows the identification of specified position on the screen through the identification of the eyes positions. This way the user can control a virtual keyboard on a screen that allows him to write and browse the system and enables him to send e-mails, SMS, activate video/music programs and control environmental devices. A patient was simulated to evaluate the versatility of the system. Its operation was satisfactory and it allowed the evaluation of the system potential. The development of this system requires low cost elements that are easily found in the market.

Visual communication interface for severe physically disabled patients

International Nuclear Information System (INIS)

Savino, M J; Fernandez, E A

2007-01-01

During the last years several interfaces have been developed to allow communication to those patients suffering serious physical disabilities. In this work, a computer based communication interface is presented. It was designed to allow communication to those patients that cannot use neither their hands nor their voice but they can do it through their eyes. The system monitors the eyes movements by means of a webcam. Then, by means of an Artificial Neural Network, the system allows the identification of specified position on the screen through the identification of the eyes positions. This way the user can control a virtual keyboard on a screen that allows him to write and browse the system and enables him to send e-mails, SMS, activate video/music programs and control environmental devices. A patient was simulated to evaluate the versatility of the system. Its operation was satisfactory and it allowed the evaluation of the system potential. The development of this system requires low cost elements that are easily found in the market

Visual communication interface for severe physically disabled patients

Energy Technology Data Exchange (ETDEWEB)

Savino, M J [Fac. de Ingenieria, Universidad Catolica de Cordoba, Camino a Alta Gracia km. 10 (5000) Cordoba, Cordoba (Argentina); Fernandez, E A [Fac. de Ingenieria, Universidad Catolica de Cordoba, Camino a Alta Gracia km. 10 (5000) Cordoba, Cordoba (Argentina)

2007-11-15

During the last years several interfaces have been developed to allow communication to those patients suffering serious physical disabilities. In this work, a computer based communication interface is presented. It was designed to allow communication to those patients that cannot use neither their hands nor their voice but they can do it through their eyes. The system monitors the eyes movements by means of a webcam. Then, by means of an Artificial Neural Network, the system allows the identification of specified position on the screen through the identification of the eyes positions. This way the user can control a virtual keyboard on a screen that allows him to write and browse the system and enables him to send e-mails, SMS, activate video/music programs and control environmental devices. A patient was simulated to evaluate the versatility of the system. Its operation was satisfactory and it allowed the evaluation of the system potential. The development of this system requires low cost elements that are easily found in the market.

water demand prediction using artificial neural network

African Journals Online (AJOL)

user

2017-01-01

Jan 1, 2017 ... Interface for activation and deactivation of valves. •. Interface demand ... process could be done and monitored at the computer terminal as expected of a .... [15] Arbib, M. A.The Handbook of Brain Theory and Neural. Networks.

An architecture for device independent interfacing

International Nuclear Information System (INIS)

Pace, D.M.

1990-01-01

Achieving device independence for software applications is required for all but a small number of critical real time applications. Device independence is achieved by establishing protocols and building protocol interpreters for the specific devices. Data structures containing pointers to functions provide a flexible architecture for implementing protocol translation. 3 refs., 5 figs

A piezo-ring-on-chip microfluidic device for simple and low-cost mass spectrometry interfacing.

Science.gov (United States)

Tsao, Chia-Wen; Lei, I-Chao; Chen, Pi-Yu; Yang, Yu-Liang

2018-02-12

Mass spectrometry (MS) interfacing technology provides the means for incorporating microfluidic processing with post MS analysis. In this study, we propose a simple piezo-ring-on-chip microfluidic device for the controlled spraying of MALDI-MS targets. This device uses a low-cost, commercially-available ring-shaped piezoelectric acoustic atomizer (piezo-ring) directly integrated into a polydimethylsiloxane microfluidic device to spray the sample onto the MS target substrate. The piezo-ring-on-chip microfluidic device's design, fabrication, and actuation, and its pulsatile pumping effects were evaluated. The spraying performance was examined by depositing organic matrix samples onto the MS target substrate by using both an automatic linear motion motor, and manual deposition. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was performed to analyze the peptide samples on the MALDI target substrates. Using our technique, model peptides with 10 -6 M concentration can be successfully detected. The results also indicate that the piezo-ring-on-chip approach forms finer matrix crystals and presents better MS signal uniformity with little sample consumption compared to the conventional pipetting method.

Digital implementation of a neural network for imaging

Science.gov (United States)

Wood, Richard; McGlashan, Alex; Yatulis, Jay; Mascher, Peter; Bruce, Ian

2012-10-01

This paper outlines the design and testing of a digital imaging system that utilizes an artificial neural network with unsupervised and supervised learning to convert streaming input (real time) image space into parameter space. The primary objective of this work is to investigate the effectiveness of using a neural network to significantly reduce the information density of streaming images so that objects can be readily identified by a limited set of primary parameters and act as an enhanced human machine interface (HMI). Many applications are envisioned including use in biomedical imaging, anomaly detection and as an assistive device for the visually impaired. A digital circuit was designed and tested using a Field Programmable Gate Array (FPGA) and an off the shelf digital camera. Our results indicate that the networks can be readily trained when subject to limited sets of objects such as the alphabet. We can also separate limited object sets with rotational and positional invariance. The results also show that limited visual fields form with only local connectivity.

Visual Prosthesis: Interfacing Stimulating Electrodes with Retinal Neurons to Restore Vision

Directory of Open Access Journals (Sweden)

Alejandro Barriga-Rivera

2017-11-01

Full Text Available The bypassing of degenerated photoreceptors using retinal neurostimulators is helping the blind to recover functional vision. Researchers are investigating new ways to improve visual percepts elicited by these means as the vision produced by these early devices remain rudimentary. However, several factors are hampering the progression of bionic technologies: the charge injection limits of metallic electrodes, the mechanical mismatch between excitable tissue and the stimulating elements, neural and electric crosstalk, the physical size of the implanted devices, and the inability to selectively activate different types of retinal neurons. Electrochemical and mechanical limitations are being addressed by the application of electromaterials such as conducting polymers, carbon nanotubes and nanocrystalline diamonds, among other biomaterials, to electrical neuromodulation. In addition, the use of synthetic hydrogels and cell-laden biomaterials is promising better interfaces, as it opens a door to establishing synaptic connections between the electrode material and the excitable cells. Finally, new electrostimulation approaches relying on the use of high-frequency stimulation and field overlapping techniques are being developed to better replicate the neural code of the retina. All these elements combined will bring bionic vision beyond its present state and into the realm of a viable, mainstream therapy for vision loss.

Providing Device Independence to Mobile Services

OpenAIRE

Nylander, Stina; Bylund, Markus

2002-01-01

People want user interfaces to services that are functional and well suited to the device they choose for access. To provide this, services must be able to offer device specific user interfaces for the wide range of devices available today. We propose to combine the two dominant approaches to platform independence, "Write Once, Run Every-where™" and "different version for each device", to create multiple device specific user interfaces for mobile services. This gives possibilities to minimize...

Structure of the Buried Metal-Molecule Interface in Organic Thin Film Devices

DEFF Research Database (Denmark)

Hansen, Christian Rein; Sørensen, Thomas Just; Glyvradal, Magni

2009-01-01

By use of specular X-ray reflectivity (XR) the structure of a metal-covered organic thin film device is measured with angstrom resolution. The model system is a Langmuir-Blodgett (LB) film, sandwiched between a silicon substrate and a top electrode consisting of 25 Å titanium and 100 Å aluminum....... By comparison of XR data for the five-layer Pb2+ arachidate LB film before and after vapor deposition of the Ti/Al top electrode, a detailed account of the structural damage to the organic film at the buried metal-molecule interface is obtained. We find that the organized structure of the two topmost LB layers...

Pulsed neural networks consisting of single-flux-quantum spiking neurons

International Nuclear Information System (INIS)

Hirose, T.; Asai, T.; Amemiya, Y.

2007-01-01

An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

Localizing Tortoise Nests by Neural Networks.

Directory of Open Access Journals (Sweden)

Roberto Barbuti

Full Text Available The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating. Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN. We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours, the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

International Nuclear Information System (INIS)

De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

2012-01-01

A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

Method for Reading Sensors and Controlling Actuators Using Audio Interfaces of Mobile Devices

Science.gov (United States)

Aroca, Rafael V.; Burlamaqui, Aquiles F.; Gonçalves, Luiz M. G.

2012-01-01

This article presents a novel closed loop control architecture based on audio channels of several types of computing devices, such as mobile phones and tablet computers, but not restricted to them. The communication is based on an audio interface that relies on the exchange of audio tones, allowing sensors to be read and actuators to be controlled. As an application example, the presented technique is used to build a low cost mobile robot, but the system can also be used in a variety of mechatronics applications and sensor networks, where smartphones are the basic building blocks. PMID:22438726

Method for reading sensors and controlling actuators using audio interfaces of mobile devices.

Science.gov (United States)

Aroca, Rafael V; Burlamaqui, Aquiles F; Gonçalves, Luiz M G

2012-01-01

This article presents a novel closed loop control architecture based on audio channels of several types of computing devices, such as mobile phones and tablet computers, but not restricted to them. The communication is based on an audio interface that relies on the exchange of audio tones, allowing sensors to be read and actuators to be controlled. As an application example, the presented technique is used to build a low cost mobile robot, but the system can also be used in a variety of mechatronics applications and sensor networks, where smartphones are the basic building blocks.

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

Science.gov (United States)

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

2018-05-11

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

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

Science.gov (United States)

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

2017-10-23

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

Designing Gestural Interfaces Touchscreens and Interactive Devices

CERN Document Server

Saffer, Dan

2008-01-01

If you want to get started in new era of interaction design, this is the reference you need. Packed with informative illustrations and photos, Designing Gestural Interfaces provides you with essential information about kinesiology, sensors, ergonomics, physical computing, touchscreen technology, and new interface patterns -- information you need to augment your existing skills in traditional" websites, software, or product development. This book will help you enter this new world of possibilities."

Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

International Nuclear Information System (INIS)

Nishitani, Y.; Kaneko, Y.; Ueda, M.; Fujii, E.; Morie, T.

2012-01-01

Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O 3 (PZT)/SrRuO 3 . In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission for learning.

Intention concepts and brain-machine interfacing

Directory of Open Access Journals (Sweden)

Franziska eThinnes-Elker

2012-11-01

Full Text Available Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs that are currently being developed to restore speech and motor control in paralyzed subjects. Such BMI devices record the brain activity of the agent, interpret (‘decode’ the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications.

Intention concepts and brain-machine interfacing.

Science.gov (United States)

Thinnes-Elker, Franziska; Iljina, Olga; Apostolides, John Kyle; Kraemer, Felicitas; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2012-01-01

Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret ("decode") the agent's intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent's intentions from neural signals in practical BMI applications.

CAMAC to GPIB interface

International Nuclear Information System (INIS)

Naivar, F.J.

1978-01-01

A CAMAC module developed at the Los Alamos Scientific Laboratory allows any device conforming to the GPIB standard to be connected to a CAMAC system. This module incorporates a microprocessor to control up to 14 GPIB-compatible instruments using a restricted set of CAMAC F-N-A commands. The marriage of a device-independent bus (IEEE Standard 488-1975) to a computer-independent bus (IEEE Standard 583-1975) provides a general method for interfacing a system of programmable instruments to any computer. This module is being used to interface a variety of interactive devices on a control console to a control computer

Distributed user interfaces for clinical ubiquitous computing applications.

Science.gov (United States)

Bång, Magnus; Larsson, Anders; Berglund, Erik; Eriksson, Henrik

2005-08-01

Ubiquitous computing with multiple interaction devices requires new interface models that support user-specific modifications to applications and facilitate the fast development of active workspaces. We have developed NOSTOS, a computer-augmented work environment for clinical personnel to explore new user interface paradigms for ubiquitous computing. NOSTOS uses several devices such as digital pens, an active desk, and walk-up displays that allow the system to track documents and activities in the workplace. We present the distributed user interface (DUI) model that allows standalone applications to distribute their user interface components to several devices dynamically at run-time. This mechanism permit clinicians to develop their own user interfaces and forms to clinical information systems to match their specific needs. We discuss the underlying technical concepts of DUIs and show how service discovery, component distribution, events and layout management are dealt with in the NOSTOS system. Our results suggest that DUIs--and similar network-based user interfaces--will be a prerequisite of future mobile user interfaces and essential to develop clinical multi-device environments.

Cu(In,Ga)Se{sub 2} absorber thinning and the homo-interface model: Influence of Mo back contact and 3-stage process on device characteristics

Energy Technology Data Exchange (ETDEWEB)

Leonard, E.; Arzel, L.; Tomassini, M.; Barreau, N., E-mail: nicolas.barreau@univ-nantes.fr [Institut des Matériaux Jean Rouxel (IMN)-UMR 6502, Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3 (France); Zabierowski, P. [Faculty of Physics, Warsaw University of Technology, Koszykowa 75, PL 00-662 Warsaw (Poland); Fuertes Marrón, D. [Instituto de Energía Solar–ETSIT, Technical University of Madrid, Ciudad Universitaria s.n., 28040 Madrid (Spain)

2014-08-21

Thinning the absorber layer is one of the possibilities envisaged to further decrease the production costs of Cu(In,Ga)Se{sub 2} (CIGSe) thin films solar cell technology. In the present study, the electronic transport in submicron CIGSe-based devices has been investigated and compared to that of standard devices. It is observed that when the absorber is around 0.5 μm-thick, tunnelling enhanced interface recombination dominates, which harms cells energy conversion efficiency. It is also shown that by varying either the properties of the Mo back contact or the characteristics of 3-stage growth processing, one can shift the dominating recombination mechanism from interface to space charge region and thereby improve the cells efficiency. Discussions on these experimental facts led to the conclusions that 3-stage process implies the formation of a CIGSe/CIGSe homo-interface, whose location as well as properties rule the device operation; its influence is enhanced in submicron CIGSe based solar cells.

Adhesion-delamination phenomena at the surfaces and interfaces in microelectronics and MEMS structures and packaged devices

International Nuclear Information System (INIS)

Khanna, V K

2011-01-01

Physico-chemical mechanisms of adhesion and debonding at the various surfaces and interfaces of semiconductor devices, integrated circuits and microelectromechanical systems are systematically examined, starting from chip manufacturing and traversing the process stages to the ultimate finished product. Sources of intrinsic and thermal stresses in these devices are pointed out. Thin film ohmic contacts to the devices call for careful attention. The role of an adhesion layer in multilayer metallization schemes is highlighted. In packaged devices, sites facing potential risks of delamination are indicated. As MEMS devices incorporate moving parts, there are additional issues due to adhesion of suspended structures to surfaces in the vicinity, both during chip fabrication and their subsequent operation. Proper surface treatments for preventing adhesion together with design considerations for overcoming stiction pave the way to reliable functioning of these devices. Adhesion-delamination issues in microelectronics and MEMS continue to pose significant challenges to both design and process engineers. This paper is an attempt to survey the adhesion characteristics of materials, their compatibilities and limitations and look at future research trends. In addition, it addresses some of the techniques for improved or reduced adhesion, as demanded by the situation. The paper encompasses fundamental aspects to contemporary applications.

What will this do to me and my brain? Ethical issues in brain-to-brain interfacing

Directory of Open Access Journals (Sweden)

Elisabeth eHildt

2015-02-01

Full Text Available For several years now, brain-computer interfaces (BCIs in which brain signals are used to navigate a computer, a prostheses or a technical device, have been developed in various experimental contexts (Wolpaw & Wolpaw 2012; Grübler & Hildt 2014. Researchers have recently taken the next step and run experiments based on connections between two brains. These so-called brain-to-brain interfaces (abbreviation: BBIs or BTBIs involve not only a BCI component deriving information from a brain and sending it to a computer, but also a computer-brain interface (CBI component delivering information to another brain. What results is technology-mediated brain-to-brain communication (B2B communication, i.e. direct communication between two brains that does not involve any activity of the peripheral nervous system. In what follows, ethical issues that arise in neural interfacing will be discussed after a short introduction to recent BBI experiments. In this, the focus will be on the implications BBIs may have on the individual at the CBI side of the BBI, i.e. on the recipient.

Modeling the Charge Transport in Graphene Nano Ribbon Interfaces for Nano Scale Electronic Devices

Science.gov (United States)

Kumar, Ravinder; Engles, Derick

2015-05-01

In this research work we have modeled, simulated and compared the electronic charge transport for Metal-Semiconductor-Metal interfaces of Graphene Nano Ribbons (GNR) with different geometries using First-Principle calculations and Non-Equilibrium Green's Function (NEGF) method. We modeled junctions of Armchair GNR strip sandwiched between two Zigzag strips with (Z-A-Z) and Zigzag GNR strip sandwiched between two Armchair strips with (A-Z-A) using semi-empirical Extended Huckle Theory (EHT) within the framework of Non-Equilibrium Green Function (NEGF). I-V characteristics of the interfaces were visualized for various transport parameters. The distinct changes in conductance and I-V curves reported as the Width across layers, Channel length (Central part) was varied at different bias voltages from -1V to 1 V with steps of 0.25 V. From the simulated results we observed that the conductance through A-Z-A graphene junction is in the range of 10-13 Siemens whereas the conductance through Z-A-Z graphene junction is in the range of 10-5 Siemens. These suggested conductance controlled mechanisms for the charge transport in the graphene interfaces with different geometries is important for the design of graphene based nano scale electronic devices like Graphene FETs, Sensors.

A novel flexible cuff-like microelectrode for dual purpose, acute and chronic electrical interfacing with the mouse cervical vagus nerve

Science.gov (United States)

Caravaca, A. S.; Tsaava, T.; Goldman, L.; Silverman, H.; Riggott, G.; Chavan, S. S.; Bouton, C.; Tracey, K. J.; Desimone, R.; Boyden, E. S.; Sohal, H. S.; Olofsson, P. S.

2017-12-01

Objective. Neural reflexes regulate immune responses and homeostasis. Advances in bioelectronic medicine indicate that electrical stimulation of the vagus nerve can be used to treat inflammatory disease, yet the understanding of neural signals that regulate inflammation is incomplete. Current interfaces with the vagus nerve do not permit effective chronic stimulation or recording in mouse models, which is vital to studying the molecular and neurophysiological mechanisms that control inflammation homeostasis in health and disease. We developed an implantable, dual purpose, multi-channel, flexible ‘microelectrode’ array, for recording and stimulation of the mouse vagus nerve. Approach. The array was microfabricated on an 8 µm layer of highly biocompatible parylene configured with 16 sites. The microelectrode was evaluated by studying the recording and stimulation performance. Mice were chronically implanted with devices for up to 12 weeks. Main results. Using the microelectrode in vivo, high fidelity signals were recorded during physiological challenges (e.g potassium chloride and interleukin-1β), and electrical stimulation of the vagus nerve produced the expected significant reduction of blood levels of tumor necrosis factor (TNF) in endotoxemia. Inflammatory cell infiltration at the microelectrode 12 weeks of implantation was limited according to radial distribution analysis of inflammatory cells. Significance. This novel device provides an important step towards a viable chronic interface for cervical vagus nerve stimulation and recording in mice.

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

Science.gov (United States)

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

2013-12-01

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

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

Science.gov (United States)

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

2013-12-01

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

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

Science.gov (United States)

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

2014-01-01

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

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Science.gov (United States)

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Stab injury and device implantation within the brain results in inversely multiphasic neuroinflammatory and neurodegenerative responses

Science.gov (United States)

Potter, Kelsey A.; Buck, Amy C.; Self, Wade K.; Capadona, Jeffrey R.

2012-08-01

An estimated 25 million people in the US alone rely on implanted medical devices, ˜2.5 million implanted within the nervous system. Even though many devices perform adequately for years, the host response to medical devices often severely limits tissue integration and long-term performance. This host response is believed to be particularly limiting in the case of intracortical microelectrodes, where it has been shown that glial cell encapsulation and localized neuronal cell loss accompany intracortical microelectrode implantation. Since neuronal ensembles must be within ˜50 µm of the electrode to obtain neuronal spikes and local field potentials, developing a better understanding of the molecular and cellular environment at the device-tissue interface has been the subject of significant research. Unfortunately, immunohistochemical studies of scar maturation in correlation to device function have been inconclusive. Therefore, here we present a detailed quantitative study of the cellular events and the stability of the blood-brain barrier (BBB) following intracortical microelectrode implantation and cortical stab injury in a chronic survival model. We found two distinctly inverse multiphasic profiles for neuronal survival in device-implanted tissue compared to stab-injured animals. For chronically implanted animals, we observed a biphasic paradigm between blood-derived/trauma-induced and CNS-derived inflammatory markers driving neurodegeneration at the interface. In contrast, stab injured animals demonstrated a CNS-mediated neurodegenerative environment. Collectively these data provide valuable insight to the possibility of multiple roles of chronic neuroinflammatory events on BBB disruption and localized neurodegeneration, while also suggesting the importance to consider multiphasic neuroinflammatory kinetics in the design of therapeutic strategies for stabilizing neural interfaces.

QoE-Aware Device-to-Device Multimedia Communications

Directory of Open Access Journals (Sweden)

Liang ZHOU

2015-08-01

Full Text Available Multimedia services over mobile device-to-device (D2D networks has recently received considerable attention. In this scenario, each device is equipped with a cellular communication interface, as well as a D2D interface over a shared medium. In this work, we study the performance properties of the mobile D2D communications in the framework of user satisfaction, and develop a fully distributed QoE-aware multimedia communication scheme (QAMCS. Specifically, we translate the opportunistic multimedia communications issue into a stochastic optimization problem, which opens up a new degree of performance to exploit. Moreover, QAMCS is designed for a heterogeneous and dynamic environment, in which user demand, device mobility, and transmission fashion may vary across different devices and applications. Importantly, QAMCS is able to maximize the user satisfaction and only needs each device to implement its own scheme individually in the absence of a central controller.

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

Directory of Open Access Journals (Sweden)

Hossein Bashashati

2017-07-01

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

Insulator-semiconductor interface fixed charges in AlGaN/GaN metal-insulator-semiconductor devices with Al2O3 or AlTiO gate dielectrics

Science.gov (United States)

Le, Son Phuong; Nguyen, Duong Dai; Suzuki, Toshi-kazu

2018-01-01

We have investigated insulator-semiconductor interface fixed charges in AlGaN/GaN metal-insulator-semiconductor (MIS) devices with Al2O3 or AlTiO (an alloy of Al2O3 and TiO2) gate dielectrics obtained by atomic layer deposition on AlGaN. Analyzing insulator-thickness dependences of threshold voltages for the MIS devices, we evaluated positive interface fixed charges, whose density at the AlTiO/AlGaN interface is significantly lower than that at the Al2O3/AlGaN interface. This and a higher dielectric constant of AlTiO lead to rather shallower threshold voltages for the AlTiO gate dielectric than for Al2O3. The lower interface fixed charge density also leads to the fact that the two-dimensional electron concentration is a decreasing function of the insulator thickness for AlTiO, whereas being an increasing function for Al2O3. Moreover, we discuss the relationship between the interface fixed charges and interface states. From the conductance method, it is shown that the interface state densities are very similar at the Al2O3/AlGaN and AlTiO/AlGaN interfaces. Therefore, we consider that the lower AlTiO/AlGaN interface fixed charge density is not owing to electrons trapped at deep interface states compensating the positive fixed charges and can be attributed to a lower density of oxygen-related interface donors.

Autonomous miniaturised device with USB interface for pulse height analysis and multi-channel scaling (TUKAN-8K-USB)

International Nuclear Information System (INIS)

Guzik, Z.; Borsuk, S.; Plominski, M.; Traczyk, K.

2005-01-01

We present autonomous a 8K-channel miniature device designed for spectroscopy or intensity vs. time measurements. The device (TUKAN-8K-USB) is based on the USB interface, and is contained in a screened separate box - it can be proved either directly from the USB port or from an external DC source (wall adapter of battery). The device may work in two independent operational modes: Multi-Channel Analysis (MCA) and Multi-Channel Scaling (MCS). The crucial MCA component - Peak detect and Hold circuitry - is featuring a novel architecture based on a diamond transistor. Its analog stage can accept analog pulses with front edges down to 100 ns and has a differential linearity below 0.5% (full scale sliding scale averaging). Automatic stops on count in Region-Of-Interest (ROI) and on preset live or real time are implemented. The MCS works at medium speed counting rates (up to 8 MHz), with preset dwell time, number of channels and multi-sweep mode. Each these parameters can also be controlled externally. Digital interfacing is based on four used configurable logical I/O lines. A single CYCLONE EP1C3 Altera FPGA provides all control functions. The USB communication is based on FYDI FIFO controller. The analyzer is equipped with advanced, user-friendly software, which is subjected of another publication. )author)

XML Interfaces to the Internet of Things

NARCIS (Netherlands)

S. Pemberton (Steven); C Foster

2015-01-01

htmlabstractThe internet of things is predicated on tiny, cheap, lower power computers being embedded in devices everywhere. However such tiny devices by definition have very little memory and computing power available to support user interfaces or extended servers, and so the user interface

Augmenting intracortical brain-machine interface with neurally driven error detectors

Science.gov (United States)

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

2017-12-01

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

Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome.

Science.gov (United States)

Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y; Stavisky, Sergey D; Simeral, John D; Newell, Katherine; Oakley, Erin M; Cash, Sydney S; Friehs, Gerhard; Hochberg, Leigh R

2015-06-01

A goal of brain-computer interface research is to develop fast and reliable means of communication for individuals with paralysis and anarthria. We evaluated the ability of an individual with incomplete locked-in syndrome enrolled in the BrainGate Neural Interface System pilot clinical trial to communicate using neural point-and-click control. A general-purpose interface was developed to provide control of a computer cursor in tandem with one of two on-screen virtual keyboards. The novel BrainGate Radial Keyboard was compared to a standard QWERTY keyboard in a balanced copy-spelling task. The Radial Keyboard yielded a significant improvement in typing accuracy and speed-enabling typing rates over 10 correct characters per minute. The participant used this interface to communicate face-to-face with research staff by using text-to-speech conversion, and remotely using an internet chat application. This study demonstrates the first use of an intracortical brain-computer interface for neural point-and-click communication by an individual with incomplete locked-in syndrome. © The Author(s) 2014.

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

Directory of Open Access Journals (Sweden)

Tayfun Gokmen

2016-07-01

Full Text Available In recent years, deep neural networks (DNN have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors while providing power efficiency of 84,000 GigaOps/s/W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things sensors.

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

Science.gov (United States)

Gokmen, Tayfun; Vlasov, Yurii

2016-01-01

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

Orientation of a 3D object: implementation with an artificial neural network using a programmable logic device

International Nuclear Information System (INIS)

Carnevale, Federico J.

2010-01-01

Complex information extraction from images is a key skill of intelligent machines, with wide application in automated systems, robotic manipulation and human-computer interaction. However, solving this problem with traditional, geometric or analytical, strategies is extremely difficult. Therefore, an approach based on learning from examples seems to be more appropriate. This thesis addresses the problem of 3D orientation, aiming to estimate the angular coordinates of a known object from an image shot from any direction. We describe a system based on artificial neural networks to solve this problem in real time. The implementation is performed using a programmable logic device. The digital system described in this paper has the ability to estimate two rotational coordinates of a 3D known object, in ranges from -80 0 to 80 0 . The operation speed allows a real time performance at video rate. The system accuracy can be successively increased by increasing the size of the artificial neural network and using a larger number of training examples [es

Graphene nanocomposites as thermal interface materials for cooling energy devices

Science.gov (United States)

Dmitriev, A. S.; Valeev, A. R.

2017-11-01

The paper describes the technology of creating samples of graphene nanocomposites based on graphene flakes obtained by splitting graphite with ultrasound of high power. Graphene nanocomposites in the form of samples are made by the technology of weak sintering at high pressure (200-300 bar) and temperature up to 150 0 C, and also in the form of compositions with polymer matrices. The reflection spectra in the visible range and the near infrared range for the surface of nanocomposite samples are studied, the data of optical and electronic spectroscopy of such samples are givenIn addition, data on the electrophysical and thermal properties of the nanocomposites obtained are presented. Some analytical models of wetting and spreading over graphene nanocomposite surfaces have been constructed and calculated, and their effective thermal conductivity has been calculated and compared with the available experimental data. Possible applications of graphene nanocomposites for use as thermal interface materials for heat removal and cooling for power equipment, as well as microelectronics and optoelectronics devices are described.

ANNarchy: a code generation approach to neural simulations on parallel hardware

Science.gov (United States)

Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

2015-01-01

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

Emerging trends in neuro engineering and neural computation

CERN Document Server

Lee, Kendall; Garmestani, Hamid; Lim, Chee

2017-01-01

This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

Photovoltaic device

Energy Technology Data Exchange (ETDEWEB)

Reese, Jason A; Keenihan, James R; Gaston, Ryan S; Kauffmann, Keith L; Langmaid, Joseph A; Lopez, Leonardo; Maak, Kevin D; Mills, Michael E; Ramesh, Narayan; Teli, Samar R

2017-03-21

The present invention is premised upon an improved photovoltaic device ("PV device"), more particularly to an improved photovoltaic device with a multilayered photovoltaic cell assembly and a body portion joined at an interface region and including an intermediate layer, at least one interconnecting structural member, relieving feature, unique component geometry, or any combination thereof.

Photovoltaic device

Science.gov (United States)

Reese, Jason A.; Keenihan, James R.; Gaston, Ryan S.; Kauffmann, Keith L.; Langmaid, Joseph A.; Lopez, Leonardo C.; Maak, Kevin D.; Mills, Michael E.; Ramesh, Narayan; Teli, Samar R.

2015-06-02

The present invention is premised upon an improved photovoltaic device ("PV device"), more particularly to an improved photovoltaic device with a multilayered photovoltaic cell assembly and a body portion joined at an interface region and including an intermediate layer, at least one interconnecting structural member, relieving feature, unique component geometry, or any combination thereof.

Microfluidic PMMA interfaces for rectangular glass capillaries

International Nuclear Information System (INIS)

Evander, Mikael; Tenje, Maria

2014-01-01

We present the design and fabrication of a polymeric capillary fluidic interface fabricated by micro-milling. The design enables the use of glass capillaries with any kind of cross-section in complex microfluidic setups. We demonstrate two different designs of the interface; a double-inlet interface for hydrodynamic focusing and a capillary interface with integrated pneumatic valves. Both capillary interfaces are presented together with examples of practical applications. This communication shows the design optimization and presents details of the fabrication process. The capillary interface opens up for the use of complex microfluidic systems in single-use glass capillaries. They also enable simple fabrication of glass/polymer hybrid devices that can be beneficial in many research fields where a pure polymer chip negatively affects the device's performance, e.g. acoustofluidics. (technical note)

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

Science.gov (United States)

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

2009-01-01

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

Electronic Processes at Organic−Organic Interfaces: Insight from Modeling and Implications for Opto-electronic Devices †

KAUST Repository

Beljonne, David

2011-02-08

We report on the recent progress achieved in modeling the electronic processes that take place at interfaces between π-conjugated materials in organic opto-electronic devices. First, we provide a critical overview of the current computational techniques used to assess the morphology of organic: organic heterojunctions; we highlight the compromises that are necessary to handle large systems and multiple time scales while preserving the atomistic details required for subsequent computations of the electronic and optical properties. We then review some recent theoretical advances in describing the ground-state electronic structure at heterojunctions between donor and acceptor materials and highlight the role played by charge-transfer and long-range polarization effects. Finally, we discuss the modeling of the excited-state electronic structure at organic:organic interfaces, which is a key aspect in the understanding of the dynamics of photoinduced electron-transfer processes. © 2010 American Chemical Society.

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.

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

Science.gov (United States)

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

2015-01-01

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

Mobile Phone User Interfaces in Multiplayer Games

OpenAIRE

NURMINEN, MINNA

2007-01-01

This study focuses on the user interface elements of mobile phones and their qualities in multiplayer games. Mobile phone is not intended as a gaming device. Therefore its technology has many shortcomings when it comes to playing mobile games on the device. One of those is the non-standardized user interface design. However, it has also some strengths, such as the portability and networked nature. In addition, many mobile phone models today have a camera, a feature only few gaming devices hav...

Local Electronic And Dielectric Properties at Nanosized Interfaces

Energy Technology Data Exchange (ETDEWEB)

Bonnell, Dawn A. [Univ. of Pennsylvania, Philadelphia, PA (United States)

2015-02-23

Final Report to the Department of Energy for period 6/1/2000 to 11/30/2014 for Grant # DE-FG02-00ER45813-A000 to the University of Pennsylvania Local Electronic And Dielectric Properties at Nanosized Interfaces PI: Dawn Bonnell The behavior of grain boundaries and interfaces has been a focus of fundamental research for decades because variations of structure and composition at interfaces dictate mechanical, electrical, optical and dielectric properties in solids. Similarly, the consequence of atomic and electronic structures of surfaces to chemical and physical interactions are critical due to their implications to catalysis and device fabrication. Increasing fundamental understanding of surfaces and interfaces has materially advanced technologies that directly bear on energy considerations. Currently, exciting developments in materials processing are enabling creative new electrical, optical and chemical device configurations. Controlled synthesis of nanoparticles, semiconducting nanowires and nanorods, optical quantum dots, etc. along with a range of strategies for assembling and patterning nanostructures portend the viability of new devices that have the potential to significantly impact the energy landscape. As devices become smaller the impact of interfaces and surfaces grows geometrically. As with other nanoscale phenomena, small interfaces do not exhibit the same properties as do large interfaces. The size dependence of interface properties had not been explored and understanding at the most fundamental level is necessary to the advancement of nanostructured devices. An equally important factor in the behavior of interfaces in devices is the ability to examine the interfaces under realistic conditions. For example, interfaces and boundaries dictate the behavior of oxide fuel cells which operate at extremely high temperatures in dynamic high pressure chemical environments. These conditions preclude the characterization of local properties during fuel cell

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

Science.gov (United States)

Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

2005-06-01

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

Quantum neural networks: Current status and prospects for development

Science.gov (United States)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Photovoltaic device

Science.gov (United States)

Reese, Jason A.; Keenihan, James R.; Gaston, Ryan S.; Kauffmann, Keith L.; Langmaid, Joseph A.; Lopez, Leonardo C.; Maak, Kevin D.; Mills, Michael E.; Ramesh, Narayan; Teli, Samar R.

2015-09-01

The present invention is premised upon an improved photovoltaic device ("PV device"), more particularly to an improved photovoltaic device (10) with a multilayered photovoltaic cell assembly (100) and a body portion (200) joined at an interface region (410) and including an intermediate layer (500), at least one interconnecting structural member (1500), relieving feature (2500), unique component geometry, or any combination thereof.

Activity patterns of cultured neural networks on micro electrode arrays

NARCIS (Netherlands)

Rutten, Wim; van Pelt, J.

2001-01-01

A hybrid neuro-electronic interface is a cell-cultured micro electrode array, acting as a neural information transducer for stimulation and/or recording of neural activity in the brain or the spinal cord (ventral motor region or dorsal sensory region). It consists of an array of micro electrodes on

Inkjet color-printer control interface

Science.gov (United States)

Kistler, R.; Kriegler, F. J.; Marshall, R. E.

1977-01-01

Special purpose interface permits computer-driven control of inkjet printers. Inkjet printers are answer to problem of high-speed peripheral output devices for computer systems. Control interface was developed to provide high-resolution color-classification maps quickly and economically from multispectral data.

Internal Interface Diversification as a Security Measure in Sensor Networks

Directory of Open Access Journals (Sweden)

Sampsa Rauti

2018-03-01

Full Text Available More actuator and sensor devices are connected to the Internet of Things (IoT every day, and the network keeps growing, while software security of the devices is often incomplete. Sensor networks and the IoT in general currently cover a large number of devices with an identical internal interface structure. By diversifying the internal interfaces, the interfaces on each node of the network are made unique, and it is possible to break the software monoculture of easily exploitable identical systems. This paper proposes internal interface diversification as a security measure for sensor networks. We conduct a study on diversifiable internal interfaces in 20 IoT operating systems. We also present two proof-of-concept implementations and perform experiments to gauge the feasibility in the IoT environment. Internal interface diversification has practical limitations, and not all IoT operating systems have that many diversifiable interfaces. However, because of low resource requirements, compatibility with other security measures and wide applicability to several interfaces, we believe internal interface diversification is a promising and effective approach for securing nodes in sensor networks.

A Study of an Assistance SystemUsing a Haptic Interface

OpenAIRE

浅川, 貴史

2011-01-01

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

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Science.gov (United States)

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

Neural networks in continuous optical media

International Nuclear Information System (INIS)

Anderson, D.Z.

1987-01-01

The authors' interest is to see to what extent neural models can be implemented using continuous optical elements. Thus these optical networks represent a continuous distribution of neuronlike processors rather than a discrete collection. Most neural models have three characteristic features: interconnections; adaptivity; and nonlinearity. In their optical representation the interconnections are implemented with linear one- and two-port optical elements such as lenses and holograms. Real-time holographic media allow these interconnections to become adaptive. The nonlinearity is achieved with gain, for example, from two-beam coupling in photorefractive media or a pumped dye medium. Using these basic optical elements one can in principle construct continuous representations of a number of neural network models. The authors demonstrated two devices based on continuous optical elements: an associative memory which recalls an entire object when addressed with a partial object and a tracking novelty filter which identifies time-dependent features in an optical scene. These devices demonstrate the potential of distributed optical elements to implement more formal models of neural networks

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Science.gov (United States)

Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

2012-01-01

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals. PMID:22596161

Human Factors and Medical Devices

International Nuclear Information System (INIS)

Dick Sawyer

1998-01-01

Medical device hardware- and software-driven user interfaces should be designed to minimize the likelihood of use-related errors and their consequences. The role of design-induced errors in medical device incidents is attracting widespread attention. The U.S. Food and Drug Administration (FDA) is fully cognizant that human factors engineering is critical to the design of safe medical devices, and user interface design is receiving substantial attention by the agency. Companies are paying more attention to the impact of device design, including user instructions, upon the performance of those health professionals and lay users who operate medical devices. Concurrently, the FDA is monitoring human factors issues in its site inspections, premarket device approvals, and postmarket incident evaluations. Overall, the outlook for improved designs and safer device operation is bright

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.

Methods for studying medical device technology and practitioner cognition: the case of user-interface issues with infusion pumps.

Science.gov (United States)

Schraagen, Jan Maarten; Verhoeven, Fenne

2013-02-01

The aims of this study were to investigate how a variety of research methods is commonly employed to study technology and practitioner cognition. User-interface issues with infusion pumps were selected as a case because of its relevance to patient safety. Starting from a Cognitive Systems Engineering perspective, we developed an Impact Flow Diagram showing the relationship of computer technology, cognition, practitioner behavior, and system failure in the area of medical infusion devices. We subsequently conducted a systematic literature review on user-interface issues with infusion pumps, categorized the studies in terms of methods employed, and noted the usability problems found with particular methods. Next, we assigned usability problems and related methods to the levels in the Impact Flow Diagram. Most study methods used to find user interface issues with infusion pumps focused on observable behavior rather than on how artifacts shape cognition and collaboration. A concerted and theory-driven application of these methods when testing infusion pumps is lacking in the literature. Detailed analysis of one case study provided an illustration of how to apply the Impact Flow Diagram, as well as how the scope of analysis may be broadened to include organizational and regulatory factors. Research methods to uncover use problems with technology may be used in many ways, with many different foci. We advocate the adoption of an Impact Flow Diagram perspective rather than merely focusing on usability issues in isolation. Truly advancing patient safety requires the systematic adoption of a systems perspective viewing people and technology as an ensemble, also in the design of medical device technology. Copyright © 2012 Elsevier Inc. All rights reserved.

The importance of Fe interface states for ferromagnet-semiconductor based spintronic devices

Science.gov (United States)

Chantis, Athanasios

2009-03-01

I present our recent theoretical studies of the bias-controlled spin injection, detection sensitivity and tunneling anisotropic magnetoresistance in ferromagnetic-semiconductor tunnel junctions. Using first-principles electron transport methods we have shown that Fe 3d minority-spin surface (interface) states are responsible for at least two important effects for spin electronics. First, they can produce a sizable Tunneling Anisotropic Magnetoresistance in magnetic tunnel junctions with a single Fe electrode. The effect is driven by a Rashba shift of the resonant surface band when the magnetization changes direction. This can introduce a new class of spintronic devices, namely, Tunneling Magnetoresistance junctions with a single ferromagnetic electrode that can function at room temperatures. Second, in Fe/GaAs(001) magnetic tunnel junctions they produce a strong dependence of the tunneling current spin-polarization on applied electrical bias. A dramatic sign reversal within a voltage range of just a few tenths of an eV is found. This explains the observed sign reversal of spin-polarization in recent experiments of electrical spin injection in Fe/GaAs(001) and related reversal of tunneling magnetoresistcance through vertical Fe/GaAs/Fe trilayers. We also present a theoretical description of electrical spin-detection at a ferromagnet/semiconductor interface. We show that the sensitivity of the spin detector has strong bias dependence which, in the general case, is dramatically different from that of the tunneling current spin-polarization. We show that in realistic ferromagnet/semiconductor junctions this bias dependence can originate from two distinct physical mechanisms: 1) the bias dependence of tunneling current spin-polarization, which is of microscopic origin and depends on the specific properties of the interface, and 2) the macroscopic electron spin transport properties in the semiconductor. Our numerical results show that the magnitude of the voltage signal

Efficient Cancer Detection Using Multiple Neural Networks.

Science.gov (United States)

Shell, John; Gregory, William D

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

Construction of a Piezoresistive Neural Sensor Array

Science.gov (United States)

Carlson, W. B.; Schulze, W. A.; Pilgrim, P. M.

1996-01-01

The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors

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

Science.gov (United States)

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

2014-10-08

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

Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.

Science.gov (United States)

Dai, Chenyun; Zheng, Yang; Hu, Xiaogang

2018-01-01

Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.

Plasticity in memristive devices for Spiking Neural Networks

Directory of Open Access Journals (Sweden)

Sylvain eSaïghi

2015-03-01

Full Text Available Memristive devices present a new device technology allowing for the realization of compact nonvolatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Temporal information encoding in dynamic memristive devices

Energy Technology Data Exchange (ETDEWEB)

Ma, Wen; Chen, Lin; Du, Chao; Lu, Wei D., E-mail: wluee@eecs.umich.edu [Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109 (United States)

2015-11-09

We show temporal and frequency information can be effectively encoded in memristive devices with inherent short-term dynamics. Ag/Ag{sub 2}S/Pd based memristive devices with low programming voltage (∼100 mV) were fabricated and tested. At weak programming conditions, the devices exhibit inherent decay due to spontaneous diffusion of the Ag atoms. When the devices were subjected to pulse train inputs emulating different spiking patterns, the switching probability distribution function diverges from the standard Poisson distribution and evolves according to the input pattern. The experimentally observed switching probability distributions and the associated cumulative probability functions can be well-explained using a model accounting for the short-term decay effects. Such devices offer an intriguing opportunity to directly encode neural signals for neural information storage and analysis.

Electrical doping: the impact on interfaces of π-conjugated molecular films

International Nuclear Information System (INIS)

Gao Weiying; Kahn, Antoine

2003-01-01

Organic-metal and organic-organic interfaces play crucial roles in charge injection in, and transport through, organic thin film devices. Their electronic structure, chemical properties and electrical behaviour must be fully characterized and understood if engineering and control of organic devices are to reach the levels attained for inorganic semiconductor devices. Recent fundamental, as well as device, work has demonstrated that electrical doping provides a very interesting way to improve carrier injection into molecular films and, eventually, control molecular level alignment at their interfaces. This brief review emphasizes the current understanding of the effects of doping on organic interfaces

Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

Directory of Open Access Journals (Sweden)

W. L. C. Rutten

2006-01-01

Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

Smoothing effect of the thermal interface material on the temperature distribution in a stepwise varying width microchannel cooling device

Science.gov (United States)

Riera, Sara; Barrau, Jérôme; Rosell, Joan I.; Fréchette, Luc G.; Omri, Mohamed; Vilarrubí, Montse; Laguna, Gerard

2017-09-01

The impact of the thermal interface material (TIM) layer on the performance of a stepwise varying width microchannel cooling device is analysed. A numerical model shows that the TIM layer, besides its well known negative impact on the temperature, also generates a smoothing effect on the temperature distribution. In this study, an analytical model is used to define a nondimensional parameter, called Smoothing Resistance ratio, as the quotient between the origin of the temperature non uniformities and the TIM thermal resistance that flatten the temperature distribution. The relationship between the temperature uniformity of the cooled device, expressed through the temperature standard deviation, and the Smoothing Resistance ratio is shown to be linear. These results lead to the definition of a new design procedure for this kind of cooling device, which aims to reduce the Smoothing Resistance ratio. Two solutions are identified and their drawbacks are analysed.

Augmented reality as a design tool for mobile interfaces

DEFF Research Database (Denmark)

Bertelsen, Olav Wedege; Nielsen, Christina

2000-01-01

applications derived from the classification of augmented reality interfaces. The focus on physical interaction with objects of work and with the mobile device provides us with a range of interaction styles, based on e.g. gestures and manipulation of objects. Furthermore, issues of transparency and directness......This paper challenges user interface paradigms for mobile devices, by using the technical classification of augmented reality interfaces as a thinking tool to develop ideas for interaction with mobile devices. The paper presents future work scenarios from a wastewater treatment plant embodying PDA...... are addressed. The future scenarios indicate that the concepts of augmented reality support solving context problems in mobile design....

Advances in the development of a cognitive user interface

Directory of Open Access Journals (Sweden)

Jokisch Oliver

2018-01-01

Full Text Available In this contribution, we want to summarize recent development steps of the embedded cognitive user interface UCUI, which enables a user-adaptive scenario in human-machine or even human-robot interactions by considering sophisticated cognitive and semantic modelling. The interface prototype is developed by different German institutes and companies with their steering teams at Fraunhofer IKTS and Brandenburg University of Technology. The interface prototype is able to communicate with users via speech and gesture recognition, speech synthesis and a touch display. The device includes an autarkic semantic processing and beyond a cognitive behavior control, which supports an intuitive interaction to control different kinds of electronic devices, e. g. in a smart home environment or in interactive respectively collaborative robotics. Contrary to available speech assistance systems such as Amazon Echo or Google Home, the introduced cognitive user interface UCUI ensures the user privacy by processing all necessary information without any network access of the interface device.

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

Structure and properties of interfaces in ceramics

International Nuclear Information System (INIS)

Bonnell, D.; Ruehle, M.; Chowdhry, U.

1995-01-01

The motivation for the symposium was the observation that interfaces in crystallographically and compositionally complex systems often dictate the performance and reliability of devices that utilize functional ceramics. The current level of understanding of interface-property relations in silicon-based devices required over 30 years of intensive research. Similar issues influence the relationship between atomic bonding at interfaces and properties in functional ceramic systems. The current understanding of these complex interfaces does not allow correlation between atomic structure and interface properties, in spite of their importance to a number of emerging technologies (wireless communications, radar-based positioning systems, sensors, etc.). The objective of this symposium was to focus attention on these fundamental issues by featuring recent theoretical and experimental work from various disciplines that impact the understanding of interface chemistry, structure, and properties. The emphasis was on relating properties of surfaces and interfaces to structure through an understanding of atomic level phenomena. Interfaces of interest include metal/ceramic, ceramic/ceramic, ceramic/vapor, etc., in electronic, magnetic, optical, ferroelectric, piezoelectric, and dielectric applications. Sixty one papers have been processed separately for inclusion on the data base

8051 microcontroller to FPGA and ADC interface design for high speed parallel processing systems – Application in ultrasound scanners

Directory of Open Access Journals (Sweden)

J. Jean Rossario Raj

2016-09-01

Full Text Available Microcontrollers perform the hardware control in many instruments. Instruments requiring huge data throughput and parallel computing use FPGA’s for data processing. The microcontroller in turn configures the application hardware devices such as FPGA’s, ADC’s and Ethernet chips etc. The interfacing of these devices uses address/data bus interface, serial interface or serial peripheral interface. The choice of the interface depends upon the input/output pins available with different devices, programming ease and proprietary interfaces supported by devices such as ADC’s. The novelty of this paper is to describe the programming logic used for various types of interface scenarios from microcontroller to different programmable devices. The study presented describes the methods and logic flowcharts for different interfaces. The implementation of the interface logics were in prototype hardware for ultrasound scanner. The internal devices were controlled from the graphical user interface in a laptop and the scan results are taken. It is seen that the optimum solution of the hardware design can be achieved by using a common serial interface towards all the devices.

The development of neural stimulators: a review of preclinical safety and efficacy studies.

Science.gov (United States)

Shepherd, Robert K; Villalobos, Joel; Burns, Owen; Nayagam, David

2018-05-14

Given the rapid expansion of the field of neural stimulation and the rigorous regulatory approval requirements required before these devices can be applied clinically, it is important that there is clarity around conducting preclinical safety and efficacy studies required for the development of this technology. The present review examines basic design principles associated with the development of a safe neural stimulator and describes the suite of preclinical safety studies that need to be considered when taking a device to clinical trial. Neural stimulators are active implantable devices that provide therapeutic intervention, sensory feedback or improved motor control via electrical stimulation of neural or neuro-muscular tissue in response to trauma or disease. Because of their complexity, regulatory bodies classify these devices in the highest risk category (Class III), and they are therefore required to go through a rigorous regulatory approval process before progressing to market. The successful development of these devices is achieved through close collaboration across disciplines including engineers, scientists and a surgical/clinical team, and the adherence to clear design principles. Preclinical studies form one of several key components in the development pathway from concept to product release of neural stimulators. Importantly, these studies provide iterative feedback in order to optimise the final design of the device. Key components of any preclinical evaluation include: in vitro studies that are focussed on device reliability and include accelerated testing under highly controlled environments; in vivo studies using animal models of the disease or injury in order to assess safety and, given an appropriate animal model, the efficacy of the technology under both passive and electrically active conditions; and human cadaver and ex vivo studies designed to ensure the device's form factor conforms to human anatomy, to optimise the surgical approach and to

Surface and interface effects in VLSI

CERN Document Server

Einspruch, Norman G

1985-01-01

VLSI Electronics Microstructure Science, Volume 10: Surface and Interface Effects in VLSI provides the advances made in the science of semiconductor surface and interface as they relate to electronics. This volume aims to provide a better understanding and control of surface and interface related properties. The book begins with an introductory chapter on the intimate link between interfaces and devices. The book is then divided into two parts. The first part covers the chemical and geometric structures of prototypical VLSI interfaces. Subjects detailed include, the technologically most import

Hard X-ray PhotoElectron Spectroscopy of transition metal oxides: Bulk compounds and device-ready metal-oxide interfaces

International Nuclear Information System (INIS)

Borgatti, F.; Torelli, P.; Panaccione, G.

2016-01-01

Highlights: • Hard X-ray PhotoElectron Spectroscopy (HAXPES) applied to buried interfaces of systems involving Transition Metal Oxides. • Enhanced contribution of the s states at high kinetic energies both for valence and core level spectra. • Sensitivity to chemical changes promoted by electric field across metal-oxide interfaces in resistive switching devices. - Abstract: Photoelectron spectroscopy is one of the most powerful tool to unravel the electronic structure of strongly correlated materials also thanks to the extremely large dynamic range in energy, coupled to high energy resolution that this form of spectroscopy covers. The kinetic energy range typically used for photoelectron experiments corresponds often to a strong surface sensitivity, and this turns out to be a disadvantage for the study of transition metal oxides, systems where structural and electronic reconstruction, different oxidation state, and electronic correlation may significantly vary at the surface. We report here selected Hard X-ray PhotoElectron Spectroscopy (HAXPES) results from transition metal oxides, and from buried interfaces, where we highlight some of the important features that such bulk sensitive technique brings in the analysis of electronic properties of the solids.

Hard X-ray PhotoElectron Spectroscopy of transition metal oxides: Bulk compounds and device-ready metal-oxide interfaces

Energy Technology Data Exchange (ETDEWEB)

Borgatti, F., E-mail: francesco.borgatti@cnr.it [Istituto per lo Studio dei Materiali Nanostrutturati (ISMN), Consiglio Nazionale delle Ricerche (CNR), via P. Gobetti 101, Bologna I-40129 (Italy); Torelli, P.; Panaccione, G. [Istituto Officina dei Materiali (IOM)-CNR, Laboratorio TASC, Area Science Park, Trieste I-34149 (Italy)

2016-04-15

Highlights: • Hard X-ray PhotoElectron Spectroscopy (HAXPES) applied to buried interfaces of systems involving Transition Metal Oxides. • Enhanced contribution of the s states at high kinetic energies both for valence and core level spectra. • Sensitivity to chemical changes promoted by electric field across metal-oxide interfaces in resistive switching devices. - Abstract: Photoelectron spectroscopy is one of the most powerful tool to unravel the electronic structure of strongly correlated materials also thanks to the extremely large dynamic range in energy, coupled to high energy resolution that this form of spectroscopy covers. The kinetic energy range typically used for photoelectron experiments corresponds often to a strong surface sensitivity, and this turns out to be a disadvantage for the study of transition metal oxides, systems where structural and electronic reconstruction, different oxidation state, and electronic correlation may significantly vary at the surface. We report here selected Hard X-ray PhotoElectron Spectroscopy (HAXPES) results from transition metal oxides, and from buried interfaces, where we highlight some of the important features that such bulk sensitive technique brings in the analysis of electronic properties of the solids.

Brain-Computer Interfaces in Medicine

Science.gov (United States)

Shih, Jerry J.; Krusienski, Dean J.; Wolpaw, Jonathan R.

2012-01-01

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. PMID:22325364

Optimized Neural Network for Fault Diagnosis and Classification

International Nuclear Information System (INIS)

Elaraby, S.M.

2005-01-01

This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

Key-value store with internal key-value storage interface

Science.gov (United States)

Bent, John M.; Faibish, Sorin; Ting, Dennis P. J.; Tzelnic, Percy; Gupta, Uday; Grider, Gary; Bonnie, David J.

2018-01-16

A key-value store is provided having one or more key-value storage interfaces. A key-value store on at least one compute node comprises a memory for storing a plurality of key-value pairs; and an abstract storage interface comprising a software interface module that communicates with at least one persistent storage device providing a key-value interface for persistent storage of one or more of the plurality of key-value pairs, wherein the software interface module provides the one or more key-value pairs to the at least one persistent storage device in a key-value format. The abstract storage interface optionally processes one or more batch operations on the plurality of key-value pairs. A distributed embodiment for a partitioned key-value store is also provided.

3D silicon neural probe with integrated optical fibers for optogenetic modulation.

Science.gov (United States)

Kim, Eric G R; Tu, Hongen; Luo, Hao; Liu, Bin; Bao, Shaowen; Zhang, Jinsheng; Xu, Yong

2015-07-21

Optogenetics is a powerful modality for neural modulation that can be useful for a wide array of biomedical studies. Penetrating microelectrode arrays provide a means of recording neural signals with high spatial resolution. It is highly desirable to integrate optics with neural probes to allow for functional study of neural tissue by optogenetics. In this paper, we report the development of a novel 3D neural probe coupled simply and robustly to optical fibers using a hollow parylene tube structure. The device shanks are hollow tubes with rigid silicon tips, allowing the insertion and encasement of optical fibers within the shanks. The position of the fiber tip can be precisely controlled relative to the electrodes on the shank by inherent design features. Preliminary in vivo rat studies indicate that these devices are capable of optogenetic modulation simultaneously with 3D neural signal recording.

Desien, ConstruThe design, fabrication and evaluation of egg weighing device using capacitive sensor and neural networksction and Evaluation of Egg Weighing Device Using Capacitive Sensor and Neural Networks

Directory of Open Access Journals (Sweden)

S Khalili

2015-09-01

Full Text Available Introduction: Grading agricultural products always has a particular important position for submission to domestic and overseas markets. The grading causes more profitable product ranges and customer satisfaction. Grading treatment is carried out based on various parameters such as color, ripeness level, dimensions and weight. Product weight is one of the most effective parameters in grading operation. Egg weight is directly related to the smallness and coarseness of eggs. In egg grading, the largeness value is very important in marketing. This research aimed to design, fabricate and evaluate the egg weighing system based on its dielectric properties. Materials and Methods: To perform this research, the stages of work are divided into several sections including, design and construction of the hardware section, writing code for the software section to collect data, conducting nondestructive tests and data collection, analysis of obtained data using artificial intelligence, and giving the results of analysis for device calibration of the system as the software code. The large eggs as dielectric substances cause more increase in the capacity of the capacitive sensor. Furthermore, by derivation of a relation between capacity of capacitive sensor and egg weight, one can predict the weight of the sample. A prototype unit of weighing system was designed and fabricated. The designed unit was composed of a chassis, a voltage source, a sinusoidal signal generator, a voltage measurement unit, an AVR micro controller, a COM port, a capacitive sensor, and an LCD and a keyboard. Neural network technique was used for egg weight prediction. The designed net receives 16 voltage values at different frequencies as inputs and its output is the egg weight. In order to calibrate and evaluate the weighing unit, 150 fresh egg samples were provided on egg laying day from a local poultry farm. Experiments were divided into three groups. The experiments were carried out on

SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

Science.gov (United States)

Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

2018-01-01

Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

LabVIEW Interface for PCI-SpaceWire Interface Card

Science.gov (United States)

Lux, James; Loya, Frank; Bachmann, Alex

2005-01-01

This software provides a LabView interface to the NT drivers for the PCISpaceWire card, which is a peripheral component interface (PCI) bus interface that conforms to the IEEE-1355/ SpaceWire standard. As SpaceWire grows in popularity, the ability to use SpaceWire links within LabVIEW will be important to electronic ground support equipment vendors. In addition, there is a need for a high-level LabVIEW interface to the low-level device- driver software supplied with the card. The LabVIEW virtual instrument (VI) provides graphical interfaces to support all (1) SpaceWire link functions, including message handling and routing; (2) monitoring as a passive tap using specialized hardware; and (3) low-level access to satellite mission-control subsystem functions. The software is supplied in a zip file that contains LabVIEW VI files, which provide various functions of the PCI-SpaceWire card, as well as higher-link-level functions. The VIs are suitably named according to the matching function names in the driver manual. A number of test programs also are provided to exercise various functions.

Memristor-based neural networks

International Nuclear Information System (INIS)

Thomas, Andy

2013-01-01

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

The challenges of neural mind-reading paradigms

OpenAIRE

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neura...

Real-time solution of the forward kinematics for a parallel haptic device using a numerical approach based on neural networks

International Nuclear Information System (INIS)

Liu, Guan Yang; Zhang, Yuru; Wang, Yan; Xie, Zheng

2015-01-01

This paper proposes a neural network (NN)-based approach to solve the forward kinematics of a 3-RRR spherical parallel mechanism designed for a haptic device. The proposed algorithm aims to remarkably speed up computation to meet the requirement of high frequency rendering for haptic display. To achieve high accuracy, the workspace of the haptic device is divided into smaller subspaces. The proposed algorithm contains NNs of two different precision levels: a rough estimation NN to identify the index of the subspace and several precise estimation networks with expected accuracy to calculate the forward kinematics. For continuous motion, the algorithm structure is further simplified to save internal memory and increase computing speed, which are critical for a haptic device control system running on an embedded platform. Compared with the mostly used Newton-Raphson method, the proposed algorithm and its simplified version greatly increase the calculation speed by about four times and 10 times, respectively, while achieving the same accuracy level. The proposed approach is of great significance for solving the forward kinematics of parallel mechanism used as haptic devices when high update frequency is needed but hardware resources are limited.

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.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Simulation of Magnetic Phenomena at Realistic Interfaces

KAUST Repository

Grytsyuk, Sergiy

2016-02-04

In modern technology exciting developments are related to the ability to understand and control interfaces. Particularly, magnetic interfaces revealing spindependent electron transport are of great interest for modern spintronic devices, such as random access memories and logic devices. From the technological point of view, spintronic devices based on magnetic interfaces enable manipulation of the magnetism via an electric field. Such ability is a result of the different quantum effects arising from the magnetic interfaces (for example, spin transfer torque or spin-orbit torque) and it can reduce the energy consumption as compared to the traditional semiconductor electronic devices. Despite many appealing characteristics of these materials, fundamental understanding of their microscopic properties and related phenomena needs to be established by thorough investigation. In this work we implement first principles calculations in order to study the structural, electric, and magnetic properties as well as related phenomena of two types of interfaces with large potential in spintronic applications: 1) interfaces between antiferromagnetic 3d-metal-oxides and ferromagnetic 3d-metals and 2) interfaces between non-magnetic 5d(4d)- and ferromagnetic 3d-metals. A major difficulty in studying such interfaces theoretically is the typically large lattice mismatch. By employing supercells with Moir e patterns, we eliminate the artificial strain that leads to doubtful results and are able to describe the dependence of the atomic density at the interfaces on the component materials and their thicknesses. After establishing understanding about the interface structures, we investigate the electronic and magnetic properties. A Moir e supercell with transition layer is found to reproduce the main experimental findings and thus turns out to be the appropriate model for simulating magnetic misfit interfaces. In addition, we systematically study the magnetic anisotropy and Rashba band

Natural User Interfaces

OpenAIRE

Câmara , António

2011-01-01

Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra This project’s main subject are Natural User Interfaces. These interfaces’ main purpose is to allow the user to interact with computer systems in a more direct and natural way. The popularization of touch and gesture devices in the last few years has allowed for them to become increasingly common and today we are experiencing a transition of interface p...

Ultra low-power integrated circuit design for wireless neural interfaces

CERN Document Server

Holleman, Jeremy; Otis, Brian

2014-01-01

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

A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers.

Science.gov (United States)

Heidrich, Regina O; Jensen, Emely; Rebelo, Francisco; Oliveira, Tiago

2015-01-01

This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).

Culture, Interface Design, and Design Methods for Mobile Devices

Science.gov (United States)

Lee, Kun-Pyo

Aesthetic differences and similarities among cultures are obviously one of the very important issues in cultural design. However, ever since products became knowledge-supporting tools, the visible elements of products have become more universal so that the invisible parts of products such as interface and interaction are getting more important. Therefore, the cultural design should be extended to the invisible elements of culture like people's conceptual models beyond material and phenomenal culture. This chapter aims to explain how we address the invisible cultural elements in interface design and design methods by exploring the users' cognitive styles and communication patterns in different cultures. Regarding cultural interface design, we examined users' conceptual models while interacting with mobile phone and website interfaces, and observed cultural difference in performing tasks and viewing patterns, which appeared to agree with cultural cognitive styles known as Holistic thoughts vs. Analytic thoughts. Regarding design methods for culture, we explored how to localize design methods such as focus group interview and generative session for specific cultural groups, and the results of comparative experiments revealed cultural difference on participants' behaviors and performance in each design method and led us to suggest how to conduct them in East Asian culture. Mobile Observation Analyzer and Wi-Pro, user research tools we invented to capture user behaviors and needs especially in their mobile context, were also introduced.

Control of neural stem cell survival by electroactive polymer substrates.

Directory of Open Access Journals (Sweden)

Vanessa Lundin

Full Text Available Stem cell function is regulated by intrinsic as well as microenvironmental factors, including chemical and mechanical signals. Conducting polymer-based cell culture substrates provide a powerful tool to control both chemical and physical stimuli sensed by stem cells. Here we show that polypyrrole (PPy, a commonly used conducting polymer, can be tailored to modulate survival and maintenance of rat fetal neural stem cells (NSCs. NSCs cultured on PPy substrates containing different counter ions, dodecylbenzenesulfonate (DBS, tosylate (TsO, perchlorate (ClO(4 and chloride (Cl, showed a distinct correlation between PPy counter ion and cell viability. Specifically, NSC viability was high on PPy(DBS but low on PPy containing TsO, ClO(4 and Cl. On PPy(DBS, NSC proliferation and differentiation was comparable to standard NSC culture on tissue culture polystyrene. Electrical reduction of PPy(DBS created a switch for neural stem cell viability, with widespread cell death upon polymer reduction. Coating the PPy(DBS films with a gel layer composed of a basement membrane matrix efficiently prevented loss of cell viability upon polymer reduction. Here we have defined conditions for the biocompatibility of PPy substrates with NSC culture, critical for the development of devices based on conducting polymers interfacing with NSCs.

VMEbus interface for spectroscopy ADCs

International Nuclear Information System (INIS)

Jaeaeskelaeinen, M.

1987-01-01

A high performance VMEbus interface for spectroscopy ADCs and other similar devices used in nuclear spectroscopy coincidence experiments has been developed. This new module can be used to interface existing spectroscopy ADCs with fast parallel data transfer into the industry standard multiprocessor VMEbus. The unit provides a fast direct readout of the ADC data into the VMEbus memory. The interface also has built-in capabilities that enable it to be used in coincidence experiments for slow data timing and ADC pattern recognition. (orig.)

Plug-and-Design: Bringing a Design Environment to a Mobile Device

OpenAIRE

MESKENS, Jan; LUYTEN, Kris; CONINX, Karin

2009-01-01

Due to the large amount of mobile devices that continue to appear on the consumer market, mobile user interface design becomes increasingly important. The major issue with many existing mobile user interface design approaches is the time and effort that is needed to deploy a user interface design to the target device. In order to address this issue, we propose the plug-and-design tool that relies on a continuous multi-device mouse pointer to design user interfaces directly on the mobile targe...

Device- and system-independent personal touchless user interface for operating rooms : One personal UI to control all displays in an operating room.

Science.gov (United States)

Ma, Meng; Fallavollita, Pascal; Habert, Séverine; Weidert, Simon; Navab, Nassir

2016-06-01

In the modern day operating room, the surgeon performs surgeries with the support of different medical systems that showcase patient information, physiological data, and medical images. It is generally accepted that numerous interactions must be performed by the surgical team to control the corresponding medical system to retrieve the desired information. Joysticks and physical keys are still present in the operating room due to the disadvantages of mouses, and surgeons often communicate instructions to the surgical team when requiring information from a specific medical system. In this paper, a novel user interface is developed that allows the surgeon to personally perform touchless interaction with the various medical systems, switch effortlessly among them, all of this without modifying the systems' software and hardware. To achieve this, a wearable RGB-D sensor is mounted on the surgeon's head for inside-out tracking of his/her finger with any of the medical systems' displays. Android devices with a special application are connected to the computers on which the medical systems are running, simulating a normal USB mouse and keyboard. When the surgeon performs interaction using pointing gestures, the desired cursor position in the targeted medical system display, and gestures, are transformed into general events and then sent to the corresponding Android device. Finally, the application running on the Android devices generates the corresponding mouse or keyboard events according to the targeted medical system. To simulate an operating room setting, our unique user interface was tested by seven medical participants who performed several interactions with the visualization of CT, MRI, and fluoroscopy images at varying distances from them. Results from the system usability scale and NASA-TLX workload index indicated a strong acceptance of our proposed user interface.

Optimal design method for a digital human–computer interface based on human reliability in a nuclear power plant. Part 3: Optimization method for interface task layout

International Nuclear Information System (INIS)

Jiang, Jianjun; Wang, Yiqun; Zhang, Li; Xie, Tian; Li, Min; Peng, Yuyuan; Wu, Daqing; Li, Peiyao; Ma, Congmin; Shen, Mengxu; Wu, Xing; Weng, Mengyun; Wang, Shiwei; Xie, Cen

2016-01-01

Highlights: • The authors present an optimization algorithm for interface task layout. • The performing process of the proposed algorithm was depicted. • The performance evaluation method adopted neural network method. • The optimization layouts of an event interface tasks were obtained by experiments. - Abstract: This is the last in a series of papers describing the optimal design for a digital human–computer interface of a nuclear power plant (NPP) from three different points based on human reliability. The purpose of this series is to propose different optimization methods from varying perspectives to decrease human factor events that arise from the defects of a human–computer interface. The present paper mainly solves the optimization method as to how to effectively layout interface tasks into different screens. The purpose of this paper is to decrease human errors by reducing the distance that an operator moves among different screens in each operation. In order to resolve the problem, the authors propose an optimization process of interface task layout for digital human–computer interface of a NPP. As to how to automatically layout each interface task into one of screens in each operation, the paper presents a shortest moving path optimization algorithm with dynamic flag based on human reliability. To test the algorithm performance, the evaluation method uses neural network based on human reliability. The less the human error probabilities are, the better the interface task layouts among different screens are. Thus, by analyzing the performance of each interface task layout, the optimization result is obtained. Finally, the optimization layouts of spurious safety injection event interface tasks of the NPP are obtained by an experiment, the proposed methods has a good accuracy and stabilization.

Neural networks with discontinuous/impact activations

CERN Document Server

Akhmet, Marat

2014-01-01

This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

Metal semiconductor contacts and devices

CERN Document Server

Cohen, Simon S; Einspruch, Norman G

1986-01-01

VLSI Electronics Microstructure Science, Volume 13: Metal-Semiconductor Contacts and Devices presents the physics, technology, and applications of metal-semiconductor barriers in digital integrated circuits. The emphasis is placed on the interplay among the theory, processing, and characterization techniques in the development of practical metal-semiconductor contacts and devices.This volume contains chapters that are devoted to the discussion of the physics of metal-semiconductor interfaces and its basic phenomena; fabrication procedures; and interface characterization techniques, particularl

Ionic conductivity in oxide heterostructures: the role of interfaces

Directory of Open Access Journals (Sweden)

Emiliana Fabbri, Daniele Pergolesi and Enrico Traversa

2010-01-01

Full Text Available Rapidly growing attention is being directed to the investigation of ionic conductivity in oxide film heterostructures. The main reason for this interest arises from interfacial phenomena in these heterostructures and their applications. Recent results revealed that heterophase interfaces have faster ionic conduction pathways than the bulk or homophase interfaces. This finding can open attractive opportunities in the field of micro-ionic devices. The influence of the interfaces on the conduction properties of heterostructures is becoming increasingly important with the miniaturization of solid-state devices, which leads to an enhanced interface density at the expense of the bulk. This review aims to describe the main evidence of interfacial phenomena in ion-conducting film heterostructures, highlighting the fundamental and technological relevance and offering guidelines to understanding the interface conduction mechanisms in these structures.

Optoelectronics Interfaces for Power Converters

Directory of Open Access Journals (Sweden)

Ovidiu Neamtu

2009-05-01

Full Text Available The most important issue interface is galvanicseparation between the signal part and the power board.Standards in the field have increased continuouslyelectro-security requirements on the rigidity of thedielectric and insulation resistance. Recommendations forclassical solutions require the use of galvanic separationoptoelectronics devices. Interfacing with a PC or DSP -controller is a target of interposition optical signals viathe power hardware commands.

Charge-carrier dynamics in polycrystalline thin-film CuIn{sub 1−x}Ga{sub x}Se{sub 2} photovoltaic devices after pulsed laser excitation: Interface and space-charge region analysis

Energy Technology Data Exchange (ETDEWEB)

Kuciauskas, Darius; Li, Jian V.; Kanevce, Ana; Guthrey, Harvey; Contreras, Miguel; Pankow, Joel; Dippo, Pat; Ramanathan, Kannan [National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401-3305 (United States)

2015-05-14

We used time-resolved photoluminescence (TRPL) spectroscopy to analyze time-domain and spectral-domain charge-carrier dynamics in CuIn{sub 1−x}Ga{sub x}Se{sub 2} (CIGS) photovoltaic (PV) devices. This new approach allowed detailed characterization for the CIGS/CdS buffer interface and for the space-charge region. We find that dynamics at the interface is dominated by diffusion, where the diffusion rate is several times greater than the thermionic emission or interface recombination rate. In the space-charge region, the electric field of the pn junction has the largest effect on the carrier dynamics. Based on the minority-carrier (electron) drift-rate dependence on the electric field strength, we estimated drift mobility in compensated CuIn{sub 1−x}Ga{sub x}Se{sub 2} (with x ≈ 0.3) as 22 ± 2 cm{sup 2}(Vs){sup −1}. Analysis developed in this study could be applied to evaluate interface and junction properties of PV and other electronic devices. For CIGS PV devices, TRPL spectroscopy could contribute to understanding effects due to absorber compositional grading, which is one of the focus areas in developing record-efficiency CIGS solar cells.

Transformable Menu Component for Mobile Device Applications: Working with both Adaptive and Adaptable User Interfaces

Directory of Open Access Journals (Sweden)

V. Glavinic

2008-08-01

Full Text Available Using a learning system in a mobile environmentis not effective if barriers are not overcome in theinteraction with targeted users. For that purpose all mobileservices, including m-learning ones, demand specialattention being paid to interaction with the user. Whilemobile device applications are becoming more powerful,their development process must utilize the concepts ofuniversal access and universal usability. This paperdescribes the model of both adaptable and adaptive mobileuser interface, through the introduction of a transformablemenu component capable to be personalized to eachindividual user with respect to her/his preferences andinteraction style. We discuss the use of customization andadaptation techniques, with the aim to both enhance mobileHCI and to increase user satisfaction, particularly whenworking with graphically rich m-learning applications.

Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks

Directory of Open Access Journals (Sweden)

Blerim Rexha

2018-02-01

Full Text Available Personal mobile devices currently have access to a significant portion of their user’s private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location.

Surfaces and interfaces of electronic materials

CERN Document Server

Brillson, Leonard J

2012-01-01

An advanced level textbook covering geometric, chemical, and electronic structure of electronic materials, and their applications to devices based on semiconductor surfaces, metal-semiconductor interfaces, and semiconductor heterojunctions. Starting with the fundamentals of electrical measurements on semiconductor interfaces, it then describes the importance of controlling macroscopic electrical properties by atomic-scale techniques. Subsequent chapters present the wide range of surface and interface techniques available to characterize electronic, optical, chemical, and structural propertie

Evaluation of the Impact of Groundwater Pumping on Freshwater-Saltwater Interface Fluctuations in a Coastal Aquifer of South Korea

Science.gov (United States)

Yoon, H.; Kim, Y.; Lee, S. H.; Ha, K.

2017-12-01

It is necessary to monitor the variation of freshwater-saltwater interface for the sustainable use of groundwater resources in coastal areas. In the present study, we developed a device to measure the location of the freshwater-saltwater interface based on the concept of the neutral buoyancy and installed it in a coastal aquifer of the western sea, South Korea. To evaluate the impact of pumping on the groundwater and saltwater-freshwater interface level, we designed nine different pumping scenarios and monitored the groundwater and saltwater-freshwater interface levels of pumping well and two observation wells. The result of monitoring groundwater level shows that the response of observation wells to the pumping is relatively fast and high, however, the response of freshwater-saltwater interface occurred when the pumping rate and duration are over 25m3/day and 48hours, respectively. For the prediction and simulation of the groundwater level fluctuation under groundwater pumping events, we designed a artificial neural network based time series model considering rainfall, tide, and pumping rate as input components. The result of the prediction shows that the correlation coefficient between observed and estimated groundwater levels is as high as 0.7. It is expected that the result of this research can provide useful information for the effective management of groundwater resources in the study area.

Nanoscale patterning of electronic devices at the amorphous LaAlO3/SrTiO3 oxide interface using an electron sensitive polymer mask

DEFF Research Database (Denmark)

Bjorlig, Anders V.; von Soosten, Merlin; Erlandsen, Ricci

2018-01-01

A simple approach is presented for designing complex oxide mesoscopic electronic devices based on the conducting interfaces of room temperature grown LaAlO3/SrTiO3 heterostructures. The technique is based entirely on methods known from conventional semiconductor processing technology, and we demo...

Low-cost universal stereoscopic virtual reality interfaces

Science.gov (United States)

Starks, Michael R.

1993-09-01

Low cost stereoscopic virtual reality hardware interfacing with nearly any computer and stereoscopic software running on any PC is described. Both are user configurable for serial or parallel ports. Stereo modeling, rendering, and interaction via gloves or 6D mice are provided. Low cost LCD Visors and external interfaces represent a breakthrough in convenience and price/performance. A complete system with software, Visor, interface and Power Glove is under $DOL500. StereoDrivers will interface with any system giving video sync (e.g., G of RGB). PC3D will access any standard serial port, while PCVR works with serial or parallel ports and glove devices. Model RF Visors detect magnetic fields and require no connection to the system. PGSI is a microprocessor control for the Power Glove and Visors. All interfaces will operate to 120 Hz with Model G Visors. The SpaceStations are demultiplexing, field doubling devices which convert field sequential video or graphics for stereo display with dual video projection or dual LCD SpaceHelmets.

Future developments in brain-machine interface research.

Science.gov (United States)

Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L

2011-01-01

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Future developments in brain-machine interface research

Directory of Open Access Journals (Sweden)

Mikhail A. Lebedev

2011-01-01

Full Text Available Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Microencapsulation and Electrostatic Processing Device

Science.gov (United States)

Morrison, Dennis R. (Inventor); Mosier, Benjamin (Inventor); Cassanto, John M. (Inventor)

2001-01-01

A microencapsulation and electrostatic processing (MEP) device is provided for forming microcapsules. In one embodiment, the device comprises a chamber having a filter which separates a first region in the chamber from a second region in the chamber. An aqueous solution is introduced into the first region through an inlet port, and a hydrocarbon/ polymer solution is introduced into the second region through another inlet port. The filter acts to stabilize the interface and suppress mixing between the two immiscible solutions as they are being introduced into their respective regions. After the solutions have been introduced and have become quiescent, the interface is gently separated from the filter. At this point, spontaneous formation of microcapsules at the interface may begin to occur, or some fluid motion may be provided to induce microcapsule formation. In any case, the fluid shear force at the interface is limited to less than 100 dynes/sq cm. This low-shear approach to microcapsule formation yields microcapsules with good sphericity and desirable size distribution. The MEP device is also capable of downstream processing of microcapsules, including rinsing, re-suspension in tertiary fluids, electrostatic deposition of ancillary coatings, and free-fluid electrophoretic separation of charged microcapsules.

Plug-and-Design: Embracing Mobile Devices as Part of the Design Environment

OpenAIRE

MESKENS, Jan; LUYTEN, Kris; CONINX, Karin

2009-01-01

Due to the large amount of mobile devices that continue to appear on the consumer market, mobile user interface design becomes increasingly important. The major issue with many existing mobile user interface design approaches is the time and effort that is needed to deploy a user interface design to the target device. In order to address this issue, we propose the plug-and-design tool that relies on a continuous multi-device mouse pointer to design user interfaces directly on the mobile targe...

The Challenges of Neural Mind-reading Paradigms

Directory of Open Access Journals (Sweden)

Oscar eVilarroya

2013-06-01

Full Text Available Neural mind-reading studies, based on multivariate pattern analysis (MVPA methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: a the BOLD signal is a marker of neural activity; b the BOLD pattern identified by a MVPA is a neurally sound pattern; c the MVPA’s feature space is a good mapping of the neural representation of a stimulus, and d the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

The challenges of neural mind-reading paradigms.

Science.gov (United States)

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

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

Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101 \\xAF 0) interface from a high-dimensional neural network potential

Science.gov (United States)

Quaranta, Vanessa; Hellström, Matti; Behler, Jörg; Kullgren, Jolla; Mitev, Pavlin D.; Hermansson, Kersti

2018-06-01

Unraveling the atomistic details of solid/liquid interfaces, e.g., by means of vibrational spectroscopy, is of vital importance in numerous applications, from electrochemistry to heterogeneous catalysis. Water-oxide interfaces represent a formidable challenge because a large variety of molecular and dissociated water species are present at the surface. Here, we present a comprehensive theoretical analysis of the anharmonic OH stretching vibrations at the water/ZnO(101 ¯ 0) interface as a prototypical case. Molecular dynamics simulations employing a reactive high-dimensional neural network potential based on density functional theory calculations have been used to sample the interfacial structures. In the second step, one-dimensional potential energy curves have been generated for a large number of configurations to solve the nuclear Schrödinger equation. We find that (i) the ZnO surface gives rise to OH frequency shifts up to a distance of about 4 Å from the surface; (ii) the spectrum contains a number of overlapping signals arising from different chemical species, with the frequencies decreasing in the order ν(adsorbed hydroxide) > ν(non-adsorbed water) > ν(surface hydroxide) > ν(adsorbed water); (iii) stretching frequencies are strongly influenced by the hydrogen bond pattern of these interfacial species. Finally, we have been able to identify substantial correlations between the stretching frequencies and hydrogen bond lengths for all species.

Metal-dielectric interfaces in gigascale electronics thermal and electrical stability

CERN Document Server

He, Ming

2012-01-01

Metal-dielectric interfaces are ubiquitous in modern electronics. As advanced gigascale electronic devices continue to shrink, the stability of these interfaces is becoming an increasingly important issue that has a profound impact on the operational reliability of these devices. In this book, the authors present the basic science underlying  the thermal and electrical stability of metal-dielectric interfaces and its relationship to the operation of advanced interconnect systems in gigascale electronics. Interface phenomena, including chemical reactions between metals and dielectrics, metallic-atom diffusion, and ion drift, are discussed based on fundamental physical and chemical principles. Schematic diagrams are provided throughout the book to illustrate  interface phenomena and the principles that govern them. Metal-Dielectric Interfaces in Gigascale Electronics  provides a unifying approach to the diverse and sometimes contradictory test results that are reported in the literature on metal-dielectric i...

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

Science.gov (United States)

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

2011-09-05

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

The Integration of Bacteriorhodopsin Proteins with Semiconductor Heterostructure Devices

Science.gov (United States)

Xu, Jian

2008-03-01

Bioelectronics has emerged as one of the most rapidly developing fields among the active frontiers of interdisciplinary research. A major thrust in this field is aimed at the coupling of the technologically-unmatched performance of biological systems, such as neural and sensing functions, with the well developed technology of microelectronics and optoelectronics. To this end we have studied the integration of a suitably engineered protein, bacteriorhodopsin (BR), with semiconductor optoelectronic devices and circuits. Successful integration will potentially lead to ultrasensitive sensors with polarization selectivity and built-in preprocessing capabilities that will be useful for high speed tracking, motion and edge detection, biological detection, and artificial vision systems. In this presentation we will summarize our progresses in this area, which include fundamental studies on the transient dynamics of photo-induced charge shift in BR and the coupling mechanism at protein-semiconductor interface for effective immobilizing and selectively integrating light sensitive proteins with microelectronic devices and circuits, and the device engineering of BR-transistor-integrated optical sensors as well as their applications in phototransceiver circuits. Work done in collaboration with Pallab Bhattacharya, Jonghyun Shin, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI; Robert R. Birge, Department of Chemistry, University of Connecticut, Storrs, CT 06269; and György V'ar'o, Institute of Biophysics, Biological Research Center of the Hungarian Academy of Science, H-6701 Szeged, Hungary.

Ontology-Based Device Descriptions and Device Repository for Building Automation Devices

Directory of Open Access Journals (Sweden)

Dibowski Henrik

2011-01-01

Full Text Available Device descriptions play an important role in the design and commissioning of modern building automation systems and help reducing the design time and costs. However, all established device descriptions are specialized for certain purposes and suffer from several weaknesses. This hinders a further design automation, which is strongly needed for the more and more complex building automation systems. To overcome these problems, this paper presents novel Ontology-based Device Descriptions (ODDs along with a layered ontology architecture, a specific ontology view approach with virtual properties, a generic access interface, a triple store-based database backend, and a generic search mask GUI with underlying query generation algorithm. It enables a formal, unified, and extensible specification of building automation devices, ensures their comparability, and facilitates a computer-enabled retrieval, selection, and interoperability evaluation, which is essential for an automated design. The scalability of the approach to several ten thousand devices is demonstrated.

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network.

Science.gov (United States)

Zhao, Yongjia; Zhou, Suiping

2017-02-28

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN's input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network

Science.gov (United States)

Zhao, Yongjia; Zhou, Suiping

2017-01-01

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns. PMID:28264503

Controlling the properties of ferroelectric-nickelate interfaces

Science.gov (United States)

Marshall, Matthew S. J.; Malashevich, Andrei; Disa, Ankit; Han, Myung-Geun; Zhu, Yimei; Ismail-Beigi, Sohrab; Walker, Frederick; Ahn, Charles

2015-03-01

Ferroelectrics are a class of materials that exhibit a stable, reversible polarization making them useful for non-volatile electronic devices. In devices consisting of thin film ferroelectric PZT acting as a gate and a thin film of the conductive oxide LaNiO3 grown on LaAlO3(001) acting as a channel, we have realized a large change in room temperature channel resistance by switching the ferroelectric polarization. The effect of switching the polarization of the ferroelectric is to modify the electronic structure of the interface between the gate and channel, resulting in conduction in the otherwise insulating ferroelectric. Here, we discuss how changing the epitaxial strain and interface termination of LaNiO3 can result in larger changes in resistivity. The epitaxial strain is varied by growing the devices on LaAlO3 for tensile strain and SrTiO3 for compressive strain. An interface termination of either an atomic layer of NiO2 or LaO is achieved via atomic layering using oxygen plasma assisted molecular beam epitaxy (MBE).

A generic service interfacing approach for home networking

NARCIS (Netherlands)

Chen, S.; Lukkien, J.J.; Bosman, R.P.; Verhoeven, R.

2010-01-01

This paper presents a generic service interfacing approach which enables the interoperability of networked devices and the reusability of services. Services are specified through a set of interfaces which are language and deployment platform independent. External service orchestration is applied to

Artificial neural networks for plasma spectroscopy analysis

International Nuclear Information System (INIS)

Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

1992-01-01

Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

A Neural Network Aero Design System for Advanced Turbo-Engines

Science.gov (United States)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a neural network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. The neural network technique works well not only as an interpolating device but also as an extrapolating device to achieve blade designs from a given database. Two validating test cases are discussed.

Interfacing of DNA with carbon nanotubes for nanodevice applications

International Nuclear Information System (INIS)

Rastogi, Richa; Dhindsa, Navneet; Suri, C. Raman; Pant, B.D.; Tripathi, S.K.; Kaur, Inderpreet; Bharadwaj, Lalit M.

2012-01-01

In nanotechnology, carbon nanotubes are evolving as ‘hot spot’ due to their applications as most sensitive biosensors. Thus, study of effect of biomolecular interaction is prerequisite for their electrical application in biosensors and bioelectronics. Here, we have explored this effect on electrical properties of carbon nanotubes with DNA as a model biomolecule. A stable conjugate of carbon nanotubes with DNA is formed via covalent methodology employing quantum dot as fluoropore and characterized with various spectroscopic, fluoroscopic and microscopic techniques. CNT–DNA adduct showed decreased transconductance (from 614.46 μS to 1.34 μS) and shift of threshold voltage (from −0.85 V to 2.5 V) due to change in Schottky barriers at metal–nanotube contact. In addition, decrease in hole mobility (from 4.46 × 10 6 to 9.72 × 10 3 cm 2 V −1 s −1 ) and increase in ON-linear resistance (from 74 kΩ to 0.44 MΩ) conclude large change in device parameters. On the one hand, this substantial change in device parameters after interfacing with biomolecules supports application of carbon nanotubes in the field of biosensors while on the other hand, the same can limit their use in future power electronic devices where stability in device parameters is essential. -- Graphical abstract: Carbon nanotubes are interfaced with DNA via covalent interactions and characterized with spectroscopic, fluoroscopic and microscopic techniques. Electrical characterization of this stable SWNT–DNA conjugate shows decreased transconductance and shift of threshold voltage towards positive gate voltages. On the one hand, this substantial change in device parameters after interfacing with biomolecules supports application of carbon nanotubes in the field of biosensors while on the other hand, the same can limit their use in future power electronic devices where stability in device parameters is essential. Highlights: ► Effect of biomolecular (DNA) interaction on electrical

Interfaces and thin films physics

International Nuclear Information System (INIS)

Equer, B.

1988-01-01

The 1988 progress report of the Interfaces and Thin Film Physics laboratory (Polytechnic School France) is presented. The research program is focused on the thin films and on the interfaces of the amorphous semiconductor materials: silicon and silicon germanium, silicon-carbon and silicon-nitrogen alloys. In particular, the following topics are discussed: the basic processes and the kinetics of the reactive gas deposition, the amorphous materials manufacturing, the physico-chemical characterization of thin films and interfaces and the electron transport in amorphous semiconductors. The construction and optimization of experimental devices, as well as the activities concerning instrumentation, are also described [fr

Is the interface OK?

Digital Repository Service at National Institute of Oceanography (India)

Suresh, T.

When a peripheral device fails, software methods can be initially resorted to before the usual hardware test procedures are used. A test program is presented here that allows various peripherals, inter-faced to a Norsk Data computer, to be tested...

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

Science.gov (United States)

2012-01-01

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

Augmented Reality user interface analysis in mobile devices

OpenAIRE

Guzmán Guzmán, José Daniel

2014-01-01

[ENGLISH] The presence of high-end phones in the telephony market, has allowed consumers to have access to the computational power of mobile smart-phone devices. Powerful processors, combined with cameras and ease of development encourage an increasing number of Augmented Reality (AR) researchers to adopt mobile smart-phones as AR platform. The same way, Augmented Reality on mobile devices has become increasingly popular for many applications, including search and location, tourism, and shopp...

Command Interface ASIC - Analog Interface ASIC Chip Set

Science.gov (United States)

Ruiz, Baldes; Jaffe, Burton; Burke, Gary; Lung, Gerald; Pixler, Gregory; Plummer, Joe; Katanyoutanant,, Sunant; Whitaker, William

2003-01-01

A command interface application-specific integrated circuit (ASIC) and an analog interface ASIC have been developed as a chip set for remote actuation and monitoring of a collection of switches, which can be used to control generic loads, pyrotechnic devices, and valves in a high-radiation environment. The command interface ASIC (CIA) can be used alone or in combination with the analog interface ASIC (AIA). Designed primarily for incorporation into spacecraft control systems, they are also suitable for use in high-radiation terrestrial environments (e.g., in nuclear power plants and facilities that process radioactive materials). The primary role of the CIA within a spacecraft or other power system is to provide a reconfigurable means of regulating the power bus, actuating all valves, firing all pyrotechnic devices, and controlling the switching of power to all switchable loads. The CIA is a mixed-signal (analog and digital) ASIC that includes an embedded microcontroller with supporting fault-tolerant switch control and monitoring circuitry that is capable of connecting to a redundant set of interintegrated circuit (I(sup 2)C) buses. Commands and telemetry requests are communicated to the CIA. Adherence to the I(sup 2)C bus standard helps to reduce development costs by facilitating the use of previously developed, commercially available components. The AIA is a mixed-signal ASIC that includes the analog circuitry needed to connect the CIA to a custom higher powered version of the I(sup 2)C bus. The higher-powered version is designed to enable operation with bus cables longer than those contemplated in the I(sup 2)C standard. If there are multiple higher-power I(sup 2)C-like buses, then there must an AIA between the CIA and each such bus. The AIA includes two identical interface blocks: one for the side-A I(sup 2)C clock and data buses and the other for the side B buses. All the AIAs on each side are powered from a common power converter module (PCM). Sides A and B

Adding tactile realism to a virtual reality laparoscopic surgical simulator with a cost-effective human interface device

Science.gov (United States)

Mack, Ian W.; Potts, Stephen; McMenemy, Karen R.; Ferguson, R. S.

2006-02-01

The laparoscopic technique for performing abdominal surgery requires a very high degree of skill in the medical practitioner. Much interest has been focused on using computer graphics to provide simulators for training surgeons. Unfortunately, these tend to be complex and have a very high cost, which limits availability and restricts the length of time over which individuals can practice their skills. With computer game technology able to provide the graphics required for a surgical simulator, the cost does not have to be high. However, graphics alone cannot serve as a training simulator. Human interface hardware, the equivalent of the force feedback joystick for a flight simulator game, is required to complete the system. This paper presents a design for a very low cost device to address this vital issue. The design encompasses: the mechanical construction, the electronic interfaces and the software protocols to mimic a laparoscopic surgical set-up. Thus the surgeon has the capability of practicing two-handed procedures with the possibility of force feedback. The force feedback and collision detection algorithms allow surgeons to practice realistic operating theatre procedures with a good degree of authenticity.

Interface characterization of InSb MOS structures

Energy Technology Data Exchange (ETDEWEB)

Shapira, Y.; Bregman, J.; Calahorra, Z.; Goshen, R.

1982-03-26

The electrical properties of MOS devices are critically dependent on the oxide-semiconductor interface. The preparation of suitable insulating layers of oxide or other material is essential for the performance of such devices and it is particularly difficult in the case of III-V compound semiconductors. We report a method of preparing an insulating layer on InSb by a plasma oxidation process. The oxidation method will be described as well as results of the analysis of the oxide-semiconductor interface by electrical and compositional techniques. Capacitance-voltage characteristics reveal the existence of interface states which are distributed near the conduction and the valence bands with a higher density near the former. Depth profiling of the oxide by Ar/sup +/ sputtering and Auger electron spectroscopy (AES) shows that the oxide is composed of a mixture of indium oxide with antimony oxide.

Hands in space: gesture interaction with augmented-reality interfaces.

Science.gov (United States)

Billinghurst, Mark; Piumsomboon, Tham; Huidong Bai

2014-01-01

Researchers at the Human Interface Technology Laboratory New Zealand (HIT Lab NZ) are investigating free-hand gestures for natural interaction with augmented-reality interfaces. They've applied the results to systems for desktop computers and mobile devices.

An application of neural networks and artificial intelligence for in-core fuel management

International Nuclear Information System (INIS)

Miller, L.F.; Algutifan, F.; Uhrig, R.E.

1992-01-01

This paper reports the feasibility of using expert systems in combination with neural networks and neutronics calculations to improve the efficiency for obtaining optimal candidate reload core designs. The general objectives of this research are as follows: (1) generate a suitable data base and ancillary software for training neural networks that duplicate neutronics calculations. (2) develop a graphical interface with neutronics software and neural networks for manual shuffling of reload cores. (3) construct an expert system for shuffling reload cores with specified rules. (4) develp neural networks that capture the nonlinear behavior of fuel depletion. (5) integrate the neural networks and neutronics software with an expert system to specify reload cores that obtain appropriate figure of merit

Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Directory of Open Access Journals (Sweden)

Fernando Moya Rueda

2018-05-01

Full Text Available Human activity recognition (HAR is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs.

SPR imaging combined with cyclic voltammetry for the detection of neural activity

Directory of Open Access Journals (Sweden)

Hui Li

2014-03-01

Full Text Available Surface plasmon resonance (SPR detects changes in refractive index at a metal-dielectric interface. In this study, SPR imaging (SPRi combined with cyclic voltammetry (CV was applied to detect neural activity in isolated bullfrog sciatic nerves. The neural activities induced by chemical and electrical stimulation led to an SPR response, and the activities were recorded in real time. The activities of different parts of the sciatic nerve were recorded and compared. The results demonstrated that SPR imaging combined with CV is a powerful tool for the investigation of neural activity.

Probe-pin device for optical neurotransmitter sensing in the brain

Science.gov (United States)

Kim, Min Hyuck; Song, Kyo D.; Yoon, Hargsoon; Park, Yeonjoon; Choi, Sang H.; Lee, Dae-Sung; Shin, Kyu-Sik; Hwang, Hak-In; Lee, Uhn

2015-04-01

Development of an optical neurotransmitter sensing device using nano-plasmonic probes and a micro-spectrometer for real time monitoring of neural signals in the brain is underway. Clinical application of this device technology is to provide autonomous closed-loop feedback control to a deep brain stimulation (DBS) system and enhance the accuracy and efficacy of DBS treatment. By far, we have developed an implantable probe-pin device based on localized field enhancement of surface plasmonic resonance on a nanostructured sensing domain which can amplify neurochemical signals from evoked neural activity in the brain. In this paper, we will introduce the details of design and sensing performance of a proto-typed microspectrometer and nanostructured probing devices for real time measurement of neurotransmitter concentrations.

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.

S-Band POSIX Device Drivers for RTEMS

Science.gov (United States)

Lux, James P.; Lang, Minh; Peters, Kenneth J.; Taylor, Gregory H.

2011-01-01

This is a set of POSIX device driver level abstractions in the RTEMS RTOS (Real-Time Executive for Multiprocessor Systems real-time operating system) to SBand radio hardware devices that have been instantiated in an FPGA (field-programmable gate array). These include A/D (analog-to-digital) sample capture, D/A (digital-to-analog) sample playback, PLL (phase-locked-loop) tuning, and PWM (pulse-width-modulation)-controlled gain. This software interfaces to Sband radio hardware in an attached Xilinx Virtex-2 FPGA. It uses plug-and-play device discovery to map memory to device IDs. Instead of interacting with hardware devices directly, using direct-memory mapped access at the application level, this driver provides an application programming interface (API) offering that easily uses standard POSIX function calls. This simplifies application programming, enables portability, and offers an additional level of protection to the hardware. There are three separate device drivers included in this package: sband_device (ADC capture and DAC playback), pll_device (RF front end PLL tuning), and pwm_device (RF front end AGC control).

Robust Brain-Computer Interfaces

NARCIS (Netherlands)

Reuderink, B.

2011-01-01

A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Current BCIs aimed at patients require that the user invests weeks, or even months, to learn the skill to intentionally modify their brain

Neuron Stimulation Device Integrated with Silicon Nanowire-Based Photodetection Circuit on a Flexible Substrate

Directory of Open Access Journals (Sweden)

Suk Won Jung

2016-12-01

Full Text Available This paper proposes a neural stimulation device integrated with a silicon nanowire (SiNW-based photodetection circuit for the activation of neurons with light. The proposed device is comprised of a voltage divider and a current driver in which SiNWs are used as photodetector and field-effect transistors; it has the functions of detecting light, generating a stimulation signal in proportion to the light intensity, and transmitting the signal to a micro electrode. To show the applicability of the proposed neural stimulation device as a high-resolution retinal prosthesis system, a high-density neural stimulation device with a unit cell size of 110 × 110 μ m and a resolution of 32 × 32 was fabricated on a flexible film with a thickness of approximately 50 μm. Its effectiveness as a retinal stimulation device was then evaluated using a unit cell in an in vitro animal experiment involving the retinal tissue of retinal Degeneration 1 (rd1 mice. Experiments wherein stimulation pulses were applied to the retinal tissues successfully demonstrate that the number of spikes in neural response signals increases in proportion to light intensity.

The Enemy Within: The Emerging Threats to Healthcare from Malicious Mobile Devices

OpenAIRE

Zawoad, Shams; Hasan, Ragib

2012-01-01

With the proliferation of wireless networks, mobile devices and medical devices are increasingly being equipped with wireless interfaces, such as Bluetooth and WiFi to allow easy access to and control of the medical devices. Unfortunately, the very presence and usage of such interfaces also expose the medical devices to novel attacks from malicious parties. The emerging threat from malicious mobile devices is significant and severe, since attackers can steal confidential data from a patient's...

Neural Signatures of Trust During Human-Automation Interactions

Science.gov (United States)

2016-04-01

also automated devices such as a Global Positioning System. For instance, to provide advanced safety measures, the Transportation Safety...AFRL-AFOSR-VA-TR-2016-0160 Neural Signatures of Trust during Human- Automation Interactions Frank Krueger GEORGE MASON UNIVERSITY Final Report 04/01...SUBTITLE Neural Signatures of Trust during Human- Automation Interactions 5a. CONTRACT NUMBER FA9550-13-1-0017 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

Role of Stress Factors on the Adhesion of Interfaces in R2R Fabricated Organic Photovoltaics

DEFF Research Database (Denmark)

Corazza, Michael; Rolston, Nicholas; Dauskardt, Reinhold H.

2016-01-01

adhesion properties. Depth profiling analysis on the fractured samples reveals interdiffusion of layers in the structure, which results in the increase of adhesion and change of the debond path. It is shown that through diffusion and intermixing of internal interfaces coupled stresses can increase......The role of the common stress factors such as high temperature, humidity,and UV irradiation on interface adhesion of roll-to-roll fabricated organic photovoltaic (OPV) devices is investigated. The samples range from bare front electrodes to complete devices. It is shown that applying single stress...... or combinations of stresses onto the samples variably affect the adhesion properties of the different interfaces in the OPV device. It is revealed that while the exposure of the complete devices to the stresses results in the loss of photovoltaic performance, some interfaces in the devices present improved...

Disposable world-to-chip interface for digital microfluidics

Science.gov (United States)

Van Dam, R. Michael; Shah, Gaurav; Keng, Pei-Yuin

2017-05-16

The present disclosure sets forth incorporating microfluidic chips interfaces for use with digital microfluidic processes. Methods and devices according to the present disclosure utilize compact, integrated platforms that interface with a chip upstream and downstream of the reaction, as well as between intermediate reaction steps if needed. In some embodiments these interfaces are automated, including automation of a multiple reagent process. Various reagent delivery systems and methods are also disclosed.

Brain machine interface and limb reanimation technologies: restoring function after spinal cord injury through development of a bypass system.

Science.gov (United States)

Lobel, Darlene A; Lee, Kendall H

2014-05-01

Functional restoration of limb movement after traumatic spinal cord injury (SCI) remains the ultimate goal in SCI treatment and directs the focus of current research strategies. To date, most investigations in the treatment of SCI focus on repairing the injury site. Although offering some promise, these efforts have met with significant roadblocks because treatment measures that are successful in animal trials do not yield similar results in human trials. In contrast to biologic therapies, there are now emerging neural interface technologies, such as brain machine interface (BMI) and limb reanimation through electrical stimulators, to create a bypass around the site of the SCI. The BMI systems analyze brain signals to allow control of devices that are used to assist SCI patients. Such devices may include a computer, robotic arm, or exoskeleton. Limb reanimation technologies, which include functional electrical stimulation, epidural stimulation, and intraspinal microstimulation systems, activate neuronal pathways below the level of the SCI. We present a concise review of recent advances in the BMI and limb reanimation technologies that provides the foundation for the development of a bypass system to improve functional outcome after traumatic SCI. We also discuss challenges to the practical implementation of such a bypass system in both these developing fields. Copyright © 2014 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Neural networks within multi-core optic fibers.

Science.gov (United States)

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-07-07

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

Science.gov (United States)

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Body-Machine Interface Enables People With Cervical Spinal Cord Injury to Control Devices With Available Body Movements: Proof of Concept.

Science.gov (United States)

Abdollahi, Farnaz; Farshchiansadegh, Ali; Pierella, Camilla; Seáñez-González, Ismael; Thorp, Elias; Lee, Mei-Hua; Ranganathan, Rajiv; Pedersen, Jessica; Chen, David; Roth, Elliot; Casadio, Maura; Mussa-Ivaldi, Ferdinando

2017-05-01

This study tested the use of a customized body-machine interface (BoMI) for enhancing functional capabilities in persons with cervical spinal cord injury (cSCI). The interface allows people with cSCI to operate external devices by reorganizing their residual movements. This was a proof-of-concept phase 0 interventional nonrandomized clinical trial. Eight cSCI participants wore a custom-made garment with motion sensors placed on the shoulders. Signals derived from the sensors controlled a computer cursor. A standard algorithm extracted the combinations of sensor signals that best captured each participant's capacity for controlling a computer cursor. Participants practiced with the BoMI for 24 sessions over 12 weeks performing 3 tasks: reaching, typing, and game playing. Learning and performance were evaluated by the evolution of movement time, errors, smoothness, and performance metrics specific to each task. Through practice, participants were able to reduce the movement time and the distance from the target at the 1-second mark in the reaching task. They also made straighter and smoother movements while reaching to different targets. All participants became faster in the typing task and more skilled in game playing, as the pong hit rate increased significantly with practice. The results provide proof-of-concept for the customized BoMI as a means for people with absent or severely impaired hand movements to control assistive devices that otherwise would be manually operated.

Surface and interface sciences of Li-ion batteries. -Research progress in electrode-electrolyte interface-

Science.gov (United States)

Minato, Taketoshi; Abe, Takeshi

2017-12-01

The application potential of Li-ion batteries is growing as demand increases in different fields at various stages in energy systems, in addition to their conventional role as power sources for portable devices. In particular, applications in electric vehicles and renewable energy storage are increasing for Li-ion batteries. For these applications, improvements in battery performance are necessary. The Li-ion battery produces and stores electric power from the electrochemical redox reactions between the electrode materials. The interface between the electrodes and electrolyte strongly affects the battery performance because the charge transfer causing the electrode redox reaction begins at this interface. Understanding of the surface structure, electronic structure, and chemical reactions at the electrode-electrolyte interface is necessary to improve battery performance. However, the interface is located between the electrode and electrolyte materials, hindering the experimental analysis of the interface; thus, the physical properties and chemical processes have remained poorly understood until recently. Investigations of the physical properties and chemical processes at the interface have been performed using advanced surface science techniques. In this review, current knowledge and future research prospects regarding the electrode-electrolyte interface are described for the further development of Li-ion batteries.

Multiphase Microfluidics The Diffuse Interface Model

CERN Document Server

2012-01-01

Multiphase flows are typically described assuming that the different phases are separated by a sharp interface, with appropriate boundary conditions. This approach breaks down whenever the lengthscale of the phenomenon that is being studied is comparable with the real interface thickness, as it happens, for example, in the coalescence and breakup of bubbles and drops, the wetting and dewetting of solid surfaces and, in general, im micro-devices. The diffuse interface model resolves these probems by assuming that all quantities can vary continuously, so that interfaces have a non-zero thickness, i.e. they are "diffuse". The contributions in this book review the theory and describe some relevant applications of the diffuse interface model for one-component, two-phase fluids and for liquid binary mixtures, to model multiphase flows in confined geometries.

Interfacing of DNA with carbon nanotubes for nanodevice applications

Energy Technology Data Exchange (ETDEWEB)

Rastogi, Richa, E-mail: richa.bend@gmail.com [Biomolecular Electronics and Nanotechnology Division (BEND), Central Scientific Instruments Organisation (CSIO), Sector-30C, Chandigarh 160030 (India); Centre of Advanced Studies in Physics, Punjab University, Sector-14, Chandigarh 160014 (India); Dhindsa, Navneet [Biomolecular Electronics and Nanotechnology Division (BEND), Central Scientific Instruments Organisation (CSIO), Sector-30C, Chandigarh 160030 (India); Suri, C. Raman [Biosensor Division, Institute of Microbial Technology (IMTECH), Sector-39, Chandigarh 160039 (India); Pant, B.D. [Central Electronics Engineering Research Institute, Pilani, Rajasthan (India); Tripathi, S.K. [Centre of Advanced Studies in Physics, Punjab University, Sector-14, Chandigarh 160014 (India); Kaur, Inderpreet; Bharadwaj, Lalit M. [Biomolecular Electronics and Nanotechnology Division (BEND), Central Scientific Instruments Organisation (CSIO), Sector-30C, Chandigarh 160030 (India)

2012-08-15

In nanotechnology, carbon nanotubes are evolving as 'hot spot' due to their applications as most sensitive biosensors. Thus, study of effect of biomolecular interaction is prerequisite for their electrical application in biosensors and bioelectronics. Here, we have explored this effect on electrical properties of carbon nanotubes with DNA as a model biomolecule. A stable conjugate of carbon nanotubes with DNA is formed via covalent methodology employing quantum dot as fluoropore and characterized with various spectroscopic, fluoroscopic and microscopic techniques. CNT-DNA adduct showed decreased transconductance (from 614.46 {mu}S to 1.34 {mu}S) and shift of threshold voltage (from -0.85 V to 2.5 V) due to change in Schottky barriers at metal-nanotube contact. In addition, decrease in hole mobility (from 4.46 Multiplication-Sign 10{sup 6} to 9.72 Multiplication-Sign 10{sup 3} cm{sup 2} V{sup -1} s{sup -1}) and increase in ON-linear resistance (from 74 k Ohm-Sign to 0.44 M Ohm-Sign ) conclude large change in device parameters. On the one hand, this substantial change in device parameters after interfacing with biomolecules supports application of carbon nanotubes in the field of biosensors while on the other hand, the same can limit their use in future power electronic devices where stability in device parameters is essential. -- Graphical abstract: Carbon nanotubes are interfaced with DNA via covalent interactions and characterized with spectroscopic, fluoroscopic and microscopic techniques. Electrical characterization of this stable SWNT-DNA conjugate shows decreased transconductance and shift of threshold voltage towards positive gate voltages. On the one hand, this substantial change in device parameters after interfacing with biomolecules supports application of carbon nanotubes in the field of biosensors while on the other hand, the same can limit their use in future power electronic devices where stability in device parameters is essential

Study of Electric Music Baton using Haptic Interface for Assistance of Visually Disabled Persons

OpenAIRE

浅川, 貴史

2012-01-01

[Abstract] We have made a proposal for a music baton system for visual disabled persons. The system is constituted by an acceleration sensor, a radio module, and a haptic interface device. When a conductor moves the baton, Players are able to acknowledge the action using the haptic interface device. We have carried out an experiment of comparing the visual and the haptic interface. The result declared that a pre-motion is important for the visual interface. In the paper, we make a proposal fo...

Interfacing external quantum devices to a universal quantum computer.

Directory of Open Access Journals (Sweden)

Antonio A Lagana

Full Text Available We present a scheme to use external quantum devices using the universal quantum computer previously constructed. We thereby show how the universal quantum computer can utilize networked quantum information resources to carry out local computations. Such information may come from specialized quantum devices or even from remote universal quantum computers. We show how to accomplish this by devising universal quantum computer programs that implement well known oracle based quantum algorithms, namely the Deutsch, Deutsch-Jozsa, and the Grover algorithms using external black-box quantum oracle devices. In the process, we demonstrate a method to map existing quantum algorithms onto the universal quantum computer.

Autologous Cell Delivery to the Skin-Implant Interface via the Lumen of Percutaneous Devices in vitro

Directory of Open Access Journals (Sweden)

Antonio Peramo

2010-11-01

Full Text Available Induced tissue regeneration around percutaneous medical implants could be a useful method to prevent the failure of the medical device, especially when the epidermal seal around the implant is disrupted and the implant must be maintained over a long period of time. In this manuscript, a novel concept and technique is introduced in which autologous keratinocytes were delivered to the interfacial area of a skin-implant using the hollow interior of a fixator pin as a conduit. Full thickness human skin explants discarded from surgeries were cultured at the air-liquid interface and were punctured to fit at the bottom of hollow cylindrical stainless steel fixator pins. Autologous keratinocytes, previously extracted from the same piece of skin and cultured separately, were delivered to the specimens thorough the interior of the hollow pins. The delivered cells survived the process and resembled undifferentiated epithelium, with variations in size and shape. Viability was demonstrated by the lack of morphologic evidence of necrosis or apoptosis. Although the cells did not form organized epithelial structures, differentiation toward a keratinocyte phenotype was evident immunohistochemically. These results suggest that an adaptation of this technique could be useful for the treatment of complications arising from the contact between skin and percutaneous devices in vivo.

Contribution to the modeling and the identification of haptic interfaces; Contribution a la modelisation et a l'identification des interfaces haptiques

Energy Technology Data Exchange (ETDEWEB)

Janot, A

2007-12-15

This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)

Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

Science.gov (United States)

Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel

2016-02-01

Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.

Bioelectrochemical control of neural cell development on conducting polymers.

Science.gov (United States)

Collazos-Castro, Jorge E; Polo, José L; Hernández-Labrado, Gabriel R; Padial-Cañete, Vanesa; García-Rama, Concepción

2010-12-01

Electrically conducting polymers hold promise for developing advanced neuroprostheses, bionic systems and neural repair devices. Among them, poly(3, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) exhibits superior physicochemical properties but biocompatibility issues have limited its use. We describe combinations of electrochemical and molecule self-assembling methods to consistently control neural cell development on PEDOT:PSS while maintaining very low interfacial impedance. Electro-adsorbed polylysine enabled long-term neuronal survival and growth on the nanostructured polymer. Neurite extension was strongly inhibited by an additional layer of PSS or heparin, which in turn could be either removed electrically or further coated with spermine to activate cell growth. Binding basic fibroblast growth factor (bFGF) to the heparin layer inhibited neurons but promoted proliferation and migration of precursor cells. This methodology may orchestrate neural cell behavior on electroactive polymers, thus improving cell/electrode communication in prosthetic devices and providing a platform for tissue repair strategies. Copyright © 2010 Elsevier Ltd. All rights reserved.

An optical brain computer interface for environmental control.

Science.gov (United States)

Ayaz, Hasan; Shewokis, Patricia A; Bunce, Scott; Onaral, Banu

2011-01-01

A brain computer interface (BCI) is a system that translates neurophysiological signals detected from the brain to supply input to a computer or to control a device. Volitional control of neural activity and its real-time detection through neuroimaging modalities are key constituents of BCI systems. The purpose of this study was to develop and test a new BCI design that utilizes intention-related cognitive activity within the dorsolateral prefrontal cortex using functional near infrared (fNIR) spectroscopy. fNIR is a noninvasive, safe, portable and affordable optical technique with which to monitor hemodynamic changes, in the brain's cerebral cortex. Because of its portability and ease of use, fNIR is amenable to deployment in ecologically valid natural working environments. We integrated a control paradigm in a computerized 3D virtual environment to augment interactivity. Ten healthy participants volunteered for a two day study in which they navigated a virtual environment with keyboard inputs, but were required to use the fNIR-BCI for interaction with virtual objects. Results showed that participants consistently utilized the fNIR-BCI with an overall success rate of 84% and volitionally increased their cerebral oxygenation level to trigger actions within the virtual environment.

Neural-Network Control Of Prosthetic And Robotic Hands

Science.gov (United States)

Buckley, Theresa M.

1991-01-01

Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

Science.gov (United States)

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Dlx proteins position the neural plate border and determine adjacent cell fates.

Science.gov (United States)

Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

2003-01-01

The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

Ludic interfaces. Driver and product of gamification

Directory of Open Access Journals (Sweden)

Mathias Fuchs

2012-05-01

Full Text Available The recent success of non-standard and playful interface devices like Wii Remote, Move, and Kinect is an indicator of a process that demonstrates that ludic interfaces might be the core driver for a transformation in the sector of video games cultures and beyond. Yet, ludic interfaces are drivers—as well as driven by social developments known as the ludification (Raessens, 2006; Fuchs & Strouhal, 2008, or the gamification of society (Schell, 2010; Bogost, 2010; Ionifides, 2011; Deterding, Khaled, Nacke, & Dixon, 2011.

CuPc/C60 heterojunction thin film optoelectronic devices

International Nuclear Information System (INIS)

Murtaza, Imran; Karimov, Khasan S.; Qazi, Ibrahim

2010-01-01

The optoelectronic properties of heterojunction thin film devices with ITO/CuPc/C 60 /Al structure have been investigated by analyzing their current-voltage characteristics, optical absorption and photocurrent. In this organic photovoltaic device, CuPc acts as an optically active layer, C 60 as an electron-transporting layer and ITO and Al as electrodes. It is observed that, under illumination, excitons are formed, which subsequently drift towards the interface with C 60 , where an internal electric field is present. The excitons that reach the interface are subsequently dissociated into free charge carriers due to the electric field present at the interface. The experimental results show that in this device the total current density is a function of injected carriers at the electrode-organic semiconductor surface, the leakage current through the organic layer and collected photogenerated current that results from the effective dissociation of excitons. (semiconductor devices)

User Interface Framework for the National Ignition Facility (NIF)

International Nuclear Information System (INIS)

Fisher, J M; Bowers, G A; Carey, R W; Daveler, S A; Herndon Ford, K B; Ho, J C; Lagin, L J; Lambert, C J; Mauvais, J; Stout, E A; West, S L

2007-01-01

A user interface (UI) framework supports the development of user interfaces to operate the National Ignition Facility (NIF) using the Integrated Computer Control System (ICCS). [1] This framework simplifies UI development and ensures consistency for NIF operators. A comprehensive, layered collection of UIs in ICCS provides interaction with system-level processes, shot automation, and subsystem-specific devices. All user interfaces are written in Java, employing CORBA to interact with other ICCS components. ICCS developers use these frameworks to compose two major types of user interfaces: broadviews and control panels. Broadviews provide a visual representation of the NIF beamlines through interactive schematic drawings. Control panels provide status and control at a device level. The UI framework includes a suite of display components to standardize user interaction through data entry behaviors, common connection and threading mechanisms, and a common appearance. With these components, ICCS developers can more efficiently address usability issues in the facility when needed. The ICCS UI framework helps developers create consistent and easy-to-understand user interfaces for NIF operators

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

International Nuclear Information System (INIS)

Heger, A.S.

1989-01-01

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

Neural engineering from advanced biomaterials to 3D fabrication techniques

CERN Document Server

Kaplan, David

2016-01-01

This book covers the principles of advanced 3D fabrication techniques, stem cells and biomaterials for neural engineering. Renowned contributors cover topics such as neural tissue regeneration, peripheral and central nervous system repair, brain-machine interfaces and in vitro nervous system modeling. Within these areas, focus remains on exciting and emerging technologies such as highly developed neuroprostheses and the communication channels between the brain and prostheses, enabling technologies that are beneficial for development of therapeutic interventions, advanced fabrication techniques such as 3D bioprinting, photolithography, microfluidics, and subtractive fabrication, and the engineering of implantable neural grafts. There is a strong focus on stem cells and 3D bioprinting technologies throughout the book, including working with embryonic, fetal, neonatal, and adult stem cells and a variety of sophisticated 3D bioprinting methods for neural engineering applications. There is also a strong focus on b...

Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks

OpenAIRE

Shuhui, L.; Fu, X.; Jaithwa, I.; Alonso, E.; Fairbank, M.; Wunsch, D. C.

2015-01-01

A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network, which implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Compared to conventional...

Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes

Science.gov (United States)

Liyanagedera, Chamika M.; Sengupta, Abhronil; Jaiswal, Akhilesh; Roy, Kaushik

2017-12-01

Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.

Ion track based tunable device as humidity sensor: a neural network approach

Science.gov (United States)

Sharma, Mamta; Sharma, Anuradha; Bhattacherjee, Vandana

2013-01-01

Artificial Neural Network (ANN) has been applied in statistical model development, adaptive control system, pattern recognition in data mining, and decision making under uncertainty. The nonlinear dependence of any sensor output on the input physical variable has been the motivation for many researchers to attempt unconventional modeling techniques such as neural networks and other machine learning approaches. Artificial neural network (ANN) is a computational tool inspired by the network of neurons in biological nervous system. It is a network consisting of arrays of artificial neurons linked together with different weights of connection. The states of the neurons as well as the weights of connections among them evolve according to certain learning rules.. In the present work we focus on the category of sensors which respond to electrical property changes such as impedance or capacitance. Recently, sensor materials have been embedded in etched tracks due to their nanometric dimensions and high aspect ratio which give high surface area available for exposure to sensing material. Various materials can be used for this purpose to probe physical (light intensity, temperature etc.), chemical (humidity, ammonia gas, alcohol etc.) or biological (germs, hormones etc.) parameters. The present work involves the application of TEMPOS structures as humidity sensors. The sample to be studied was prepared using the polymer electrolyte (PEO/NH4ClO4) with CdS nano-particles dispersed in the polymer electrolyte. In the present research we have attempted to correlate the combined effects of voltage and frequency on impedance of humidity sensors using a neural network model and results have indicated that the mean absolute error of the ANN Model for the training data was 3.95% while for the validation data it was 4.65%. The corresponding values for the LR model were 8.28% and 8.35% respectively. It was also demonstrated the percentage improvement of the ANN Model with respect to the