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检索条件"机构=Microelectronics System Design Laboratory"

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Dynamic PECT Effect for Nanotransducers and Hybrid Devices

Dynamic PECT Effect for Nanotransducers and Hybrid Devices

引用

Midwest Symposium on Circuits and systems (MWSCAS)

作者： F. Lincoln Vogel Hoda S. Abdel-Aty-Zohdy Robert L. Ewing GraphiTech Inc. St. Clair Shores MI 48080-1567 Microelectronics System Design Laboratory Electrical and Computer Engineering Oakland University Rochester MI USA Information Directorate Air Force Research Laboratory OH USA

Recent advances in nanotechnology have opened the door to the use of hybrid devices in integrated system-on-a-chip. The Piezo Electrochemical Transducer (PECT) effect device uses nano-dimensioned graphite fibers, where the PECT effect can be effectively used to control current and voltage levels. The PECT effect, discovered (in early 1980's) and named by co-author F. L. Vogel, uses graphite fiber intercalated with sulfuric acid (H2SO4) to shrink parallel to the graphite planes (basal plane), whereas the reverse PECT effect causes the graphite fiber to expand. The reverse PECT effect appears by reversing the direction of the electrolysis current. This paper explores the physical properties of PECT and its applications to developing novel solid-state hybrid devices, including new kinds of transducers and transistors based on graphite that will have a significant impact on future bio-inspired integrated circuits and nano-technology.

关键词： Nanoscale devices Optical fiber devices Transducers Nanotechnology system-on-a-chip Voltage control Electrochemical processes Solid state circuits Application specific integrated circuits Hybrid integrated circuits

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A Compact Correlation Filter For On-Chip Learning in a Spiking Neural Network

A Compact Correlation Filter For On-Chip Learning in a Spiki...

引用

Midwest Symposium on Circuits and systems (MWSCAS)

作者： Jacob N. Allen Hoda S. Abdel-Aty-Zohdy Robert L. Ewing Microelectronics System Design Laboratory Department of Elect. and Computer Engineering Oakland University Rochester MI USA Member IEEE Information Directorate Air Force Research Labs Wright-Patterson Air Force Base Ohio 45433

A Hebbian learning algorithm based on proportion sampling is presented that can be used to implement on-chip learning for a binary spiking neural network. A correlation filter estimates when statistical independence has been obtained between subsequent samples. Simulation shows that the correlation filter reduces falsely learned connections in environments were inputs are randomly activated an average of 83% of the total time. A correlation filter for 255 binary samples is implemented using 21 gates and a surface area of .0008cm2 for a .5μ fabrication process. Compared to traditional neural networks, the spiking neural network learned an odor in a single epoch resulting in only a 7% error, while classical learning algorithms required multiple epochs and typically resulted in 30% error.

关键词： Filters Network-on-a-chip Neural networks Neurons Hebbian theory Working environment noise microelectronics Sampling methods Active noise reduction Jacobian matrices

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Energy diffusion model of bR for voxel identification

Energy diffusion model of bR for voxel identification

引用

2005 IEEE International 48th Midwest Symposium on Circuits and systems, MWSCAS 2005

作者： Neto, Didimo Garcia Abdel-Aty-Zohdy, Hoda S. Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester United States

ISBN: (纸本)0780391977

In this paper, a potential biological structure for computer volumetric memory is investigated. Bacteriorhodopsin is a protein found in the purple membrane of the bacteria Halobacterium salinarum. This protein has light absorption properties that generate different stable states allowing the use of multi-level logic as well as binary logic. CHARMm™ has been used to simulate the energy response of Bacteriorhodopsin at room temperature 300K for the bR ground state and the other states. The K state takes place after blue light (λ= 635nm) absorption on the bR state. The energy difference is found to be an increase of 1086.92 kcal/mol which is responsible for triggering the photocycle that also includes the states K, L, M, N, and O. An energy surplus is found between 6 and bR state that may be spread to surrounding molecules. However, conformational interference will not produce photons necessary for the isomerization reaction. Further Quantum Molecular (QM) analysis should provide an extended model for this macromolecular energy study. © 2005 IEEE.

关键词： Storage allocation (computer)

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Plasticity recurrent spiking neural networks for olfactory pattern recognition

Plasticity recurrent spiking neural networks for olfactory p...

引用

2005 IEEE International 48th Midwest Symposium on Circuits and systems, MWSCAS 2005

作者： Allen, J.N. Abdel-Aty-Zohdy, H.S. Ewing, R.L. Microelectronics System Design Laboratory Department of Electrical and System Engineering Oakland University Rochester MI 48309 Air Force Research Laboratory Information Directorate Wright-Patterson AFB OH 45433

ISBN: (纸本)0780391977

This paper introduces a novel spiking neural network methodology, and applies it to an odorant learning and detection application. Spike-time dependent plasticity can support coding schemes that are based on spatio-temporal spike patterns. Spiking (or pulsed) neural networks (SNNs) are models which explicitly take into account the timing of inputs. The network input and output are usually represented as series of spikes (delta function or more complex shapes). Plasticity SNNs have an advantage of being able to recurrently process information. Spike-time dependent plasticity can enhance signal transmission by selectively strengthening synaptic connections that transmit precisely timed spikes at the expense of those synapses that transmit poorly timed spikes. Complete theory describes the spiking network's digital implementation. Theory is verified using simulation of a biologically plausible odor environment with 1023 odor receptor inputs. Stochastic on-chip learning uses sampling and a correlation filter to learn odors despite noisy environment conditions. Simulation and Verilog chip design are tested on a Field Programmable Gate Array. A scalable Field Programmable Gate Array implementation with 1024 inputs, 1 output, and expansion capability for unlimited outputs is synthesized into 87,062 gates. © 2005 IEEE.

关键词： Neural networks

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Exploring the biocomputing frontier

Exploring the biocomputing frontier

引用

2005 IEEE International 48th Midwest Symposium on Circuits and systems, MWSCAS 2005

作者： Ewing, R.L. Abdel-Aty-Zohdy, H.S. Schuermeyer, F. Liou, L. Moncrief, J.B. Rubeiz, M. Williamson, M. Air Force Research Laboratory Information Directorate Wright-Patterson AFB OH Microelectronics System Design Laboratory Department of Electrical and System Engineering Oakland University Rochester MI Wright State University Department of Electrical Engineering Dayton OH

ISBN: (纸本)0780391977

Stop thinking that the universe is best described by boolean order operations, consider the world of biocomputing. The idea in exploring the multidimensional characteristics of biocomputing systems involves multilevel protein logic interaction with hybrid digital systems (bio/digital/analog systems). In the protein biocomputing world, all models are multilevel and nonlinear exponential in nature. These biocomputing systems are linearized by boundary limited frequency, time response and dimensional ranges. The protein model and its correct development is probably the most difficult aspect for biocomputing issues. Previous protein models were not dealing with 2 and 3 dimensional models, while the current protein models are now faced with high frequency design, due to the emergence of bio-nanodevices. These future biocomputing devices will be chemical sensors, fluidic computer systems, optical switches, nanoactuators, self-repairing polymorphic computing systems, cognitive processors and biorobotics. © 2005 IEEE.

关键词： Boolean algebra

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Dynamic impedance model of piezo ElectroChemical transducer effect for a smart battery

Dynamic impedance model of piezo ElectroChemical transducer ...

引用

2005 IEEE International 48th Midwest Symposium on Circuits and systems, MWSCAS 2005

作者： Teli, Purvi S. Abdel-Aty-Zohdy, Hoda S. Line Vogel, F. Ewing, Robert L. Microelectronics System Design Laboratory Department of Electrical and System Engineering Oakland University Rochester MI 48309 GraphiTech Inc. St. Clair Shores MI 48080 Air Force Research Laboratory Information Directorate Wright-Patterson AFB OH 45433

ISBN: (纸本)0780391977

Intelligent power management for dry cell batteries may be accomplished by external circuitry which is connected to the individual power cells. In comparison, the presented Graphite circuit model, along with lead-acid battery technology facilitates the power management of wet cells in sealed packaging. This paper reiterates the Piezo ElectroChemical Transducer (PECT) effect based on the intercalation of graphite with sulfuric acid;Introduces a first attempt at developing a dynamic circuit model which will facilitate for a wide range of smart PECT applications. The paper also addresses the usage of PECT effect device for smart battery management, which operates in sulfuric acid environment. The developed dynamic impedance circuit model of PECT effect device can be used further to understand the changes in resistance and reactance properties of PECT device with the change in the temperature, voltage, current, stages, type of graphite and the concentration of sulfuric acid of electrochemical intercalation of graphite with sulfuric acid. © 2005 IEEE.

关键词： Photoelectrochemical cells

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Energy diffusion model of bR for voxel identification

Energy diffusion model of bR for voxel identification

引用

Midwest Symposium on Circuits and systems (MWSCAS)

作者： D. Garcia Neto H.S. Abdel-Aty-Zohdy Microelectronics System Design Laboratory-Department of Electrical and Systems Engineering Oakland University Rochester USA

In this paper, a potential biological structure for computer volumetric memory is investigated. Bacteriorhodopsin is a protein found in the purple membrane of the bacteria Halobacterium salinarum. This protein has light absorption properties that generate different stable states allowing the use of multilevel logic as well as binary logic. CHARMmtrade has been used to simulate the energy response of Bacteriorhodopsin at room temperature 300K for the bR ground state and the other states. The K state takes place after blue light (lambda= 635nm) absorption on the bR state. The energy difference is found to be an increase of 1086.92 kcal/mol which is responsible for triggering the photocycle that also includes the states K, L, M, N, and O. An energy surplus is found between O and bR state that may be spread to surrounding molecules. However, conformational interference does not produce photons necessary for the isomerization reaction. Further quantum molecular (QM) analysis should provide an extended model for this macromolecular energy study

关键词： Biological system modeling Proteins Absorption Logic Biology computing Biomembranes Microorganisms Computational modeling Land surface temperature Stationary state

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A genetic algorithm tracking model for product deployment in telecom services

A genetic algorithm tracking model for product deployment in...

引用

2005 IEEE International 48th Midwest Symposium on Circuits and systems, MWSCAS 2005

作者： Murphy, Linda Abdel-Aty-Zohdy, Hoda S. Hashem-Sherif, M. Microelectronics System Design Laboratory Oakland University Electrical and Systems Engineering Rochester MI United States AT and T Bell Labs. C5-2018 200 S. Laurel Avenue Middletown NJ United States

ISBN: (纸本)0780391977

Genetic algorithms (GA) are designed to search for, discover and emphasize good solutions by applying selection and crossover techniques, inspired by nature, to supply solutions to engineering systems. Genetic algorithms operate on pieces of information like nature does on genes in the course of evolution. All individuals of one generation are evaluated by a fitness function. This paper presents an approach to implement a genetic algorithm to depict a recently developed "Integrated Model" for defect tracking for product deployment in telecommunication systems by means of a solution space representing target variables. The genetic algorithm inputs these target values as dynamic input measurements. From these measurements, the best possible fused values are found for the target variables. The goal of the genetic algorithm is dynamically to update the parameters applied to the input measurements to find the optimum solution for the defect tracking model system. Focus will be given to one dimension of sensor input measurements consisting of three four-bit binary sensor inputs varying within a given range of data points. The genetic algorithm is applied to these inputs by means of a 64 6-bit random member binary population using the "Half-Sibling and a Clone" (HSAC) technique of crossover and mutation. The population members are evaluated using a fitness function where the fittest individuals are retained to survive and produce offspring for the next generation and the remainder are discarded. Genetic diversity is maintained through using a roulette wheel technique. The simulation tool MATLAB will be used to simulate the genetic algorithm. The genetic algorithm will be completed in VLSI using Verilog and the Mentor Graphics tools. The first stage components have been designed and completed using the Mentor Graphics tool-set. © 2005 IEEE.

关键词： Genetic algorithms

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Plasticity recurrent spiking neural networks for olfactory pattern recognition

Plasticity recurrent spiking neural networks for olfactory p...

引用

Midwest Symposium on Circuits and systems (MWSCAS)

作者： J.N. Allen H.S. Abdel-Aty-Zohdy R.L. Ewing Microelectronics System Design Laboratory Department of Electrical and System Engineering Oakland University Rochester MI USA Information Directorate Air Force Research Laboratory OH USA

This paper introduces a novel spiking neural network methodology, and applies it to an odorant learning and detection application. Spike-time dependent plasticity can support coding schemes that are based on spatio-temporal spike patterns. Spiking (or pulsed) neural networks (SNNs) are models which explicitly take into account the timing of inputs. The network input and output are usually represented as series of spikes (delta function or more complex shapes). Plasticity SNNs have an advantage of being able to recurrently process information. Spike-time dependent plasticity can enhance signal transmission by selectively strengthening synaptic connections that transmit precisely timed spikes at the expense of those synapses that transmit poorly timed spikes. Complete theory describes the spiking network's digital implementation. Theory is verified using simulation of a biologically plausible odor environment with 1023 odor receptor inputs. Stochastic on-chip learning uses sampling and a correlation filter to learn odors despite noisy environment conditions. Simulation and Verilog chip design are tested on a field programmable gate array. A scalable field programmable gate array implementation with 1024 inputs, 1 output, and expansion capability for unlimited outputs is synthesized into 87,062 gates

关键词： Neural networks Recurrent neural networks Olfactory Pattern recognition Biological system modeling Field programmable gate arrays Timing Shape Stochastic processes Sampling methods

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Exploring the biocomputing frontier

Exploring the biocomputing frontier

引用

Midwest Symposium on Circuits and systems (MWSCAS)

作者： R.L. Ewing H.S. Abdel-Aty-Zohdy F. Schuermeyer L. Liou J.B. Moncrief M. Rubeiz M. Williamson Air Force Research Laboratory OH USA Microelectronics System Design Laboratory Department of Electrical and System Engineering Oakland University Rochester MI USA Department of Electrical Engineering Wright State University Dayton OH USA

Stop thinking that the universe is best described by Boolean order operations, consider the world of biocomputing. The idea in exploring the multidimensional characteristics of biocomputing systems involves multilevel protein logic interaction with hybrid digital systems (bio/digital/analog systems). In the protein biocomputing world, all models are multilevel and nonlinear exponential in nature. These biocomputing systems are linearized by boundary limited frequency, time response and dimensional ranges. The protein model and its correct development is probably the most difficult aspect for biocomputing issues. Previous protein models were not dealing with 2 and 3 dimensional models, while the current protein models are now faced with high frequency design, due to the emergence of bio-nanodevices. These future biocomputing devices will be chemical sensors, fluidic computer systems, optical switches, nanoactuators, self-repairing polymorphic computing systems, cognitive processors and biorobotics

关键词： Frequency Optical computing Multidimensional systems Logic Digital systems Time factors Protein engineering Chemical sensors Optical switches Nanoscale devices

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