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检索条件"机构=Microelectronics System Design Laboratory-Department of Electrical and Systems Engineering"

共 126 条记录，以下是31-40 订阅

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Biologically-Based Signal Processing Chips with Emphasis on Telecommunication Defect Tracking and Reliability Estimation

Biologically-Based Signal Processing Chips with Emphasis on ...

引用

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Hoda S Abdel-Aty-Zohdy Microelectronics System Design Laboratory Department of Electrical and Computer Engineering Oakland University Rochester MI USA

This paper provides observations and motivations to mimic biological information processing. Alternative bio-inspired systems definitions, basics, approaches, algorithms, and chip implementations will be illustrated to offer a base of choice for bio-based Intelligent Information Processing (IIP) systems. Hybrid biological and bio-based IIP are briefly presented. Two specific applications follow with embedded bio-based systems: Bio-chemical sensing and detection E-nose; and Track improvements In the reliability of the software used in telecommunication network deployments. The biologically-based processing discoveries gleaned from observing the spikes in the brain activity of monkeys, introduced the concept of plasticity in synapses used in our embedded Spiking Neural Network (SNN) system for the E-Nose The mathematical construct of a defect tracking classifier is nonlinear, and the event to be recognized involves a sequentially varying or non-stationary phenomenon for telecommunication defect tracking and reliability estimation. Thus, Adaptive Recurrent Dynamic Neural Network (ARDNN) system using wavelet function as the basis improved the failure event estimation of software defect tracking in telecommunications and reduced the error from 88% to L25-8%.

关键词： Biomedical signal processing Signal processing algorithms Information processing Telecommunication network reliability Biological neural networks Application software Embedded software Brain Nonlinear dynamical systems Neural networks

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ANN digitally programmable analog synapse

ANN digitally programmable analog synapse

引用

Midwest Symposium on Circuits and systems (MWSCAS)

作者： M. Al-Nsour H. Abdel-Aty-Zhody Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA Oakland University Rochester MI US

An Artificial Neural Network (ANN) synapse with local digital weight storage capability is presented. It utilizes a novel, compact, voltage mode multiplying digital-to-analog converter to perform multiplication between an analog input and a digital weight. A 6-bit signed weight synapse chip has been implemented in VLSI through MOSIS 1.2 micrometer n-well CMOS technology. It occupies a total area of 1.35 mm/sup 2/. With +3 Volts supply, the circuit accepts analog inputs within /spl plusmn/1 V. Output is also within /spl plusmn/1 V. Maximum power consumption is 20 mW and total error is less than 3%. Circuit analysis and performance evaluation is presented.

关键词： Voltage Circuits Artificial neural networks Digital-analog conversion CMOS technology Energy consumption Silicon Capacitance microelectronics Laboratories

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Simulation and Optimization of In-Situ Integration Ultraviolet HEMT-LED Enabled by P-GaN Selective Epitaxy Growth

Simulation and Optimization of In-Situ Integration Ultraviol...

引用

SID International Symposium Digest of Technical Papers, 2023

作者： Zhang, Jingyang Huang, Wei Liu, Zhaojun Department of Electronic and Electrical Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China A State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics and Systems School of Microelectronics Fudan University Shanghai200433 China

Based on previous work, the ultraviolet HEMT-LED we fabricated by selective epitaxy growth, this paper further performed simulation to explore the most appropriate p-GaN selection size. P-GaN grown by Selective Epitaxy Growth (SEG) can not only be employed as a junction termination to reduce the electric field crowding of the gate of HEMT, but also form an active region by contacting with 2-dimentional electron gas (2DEG). The simulation results provide an effective reference for the design of p-GaN grown by SEG, and further pave the way for the realization of the idea of integration through the selection of growth methods. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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design and Simulation of Integrated Ultraviolet HEMT-LED enabled by P-GaN Selective Epitaxy Growth

Design and Simulation of Integrated Ultraviolet HEMT-LED ena...

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The 2023 International Conference on Display Technology, (ICDT 2023)

作者： Zhang, Jingyang Huang, Wei Liu, Zhaojun Department of electronic and electrical engineering Southern University of Science and Technology Guangdong Shenzhen518055 China A State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China

Based on previous work, the ultraviolet HEMT-LED we fabricated by selective epitaxy growth, this paper further performed simulation to explore the most appropriate p-GaN selection size. P- GaN grown by Selective Epitaxy Growth (SEG) can not only be employed as a junction termination to reduce the electric field crowding of the gate of HEMT, but also form an active region by contacting with 2-dimentional electron gas (2DEG). The simulation results provide an effective reference for the design of p-GaN grown by SEG, and further pave the way for the realization of the idea of integration through the selection of growth methods. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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Uncertain nonlinear algebraic solutions and their implementation using neural networks

Uncertain nonlinear algebraic solutions and their implementa...

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： H.S. Abdel-Aty-Zohdy M.A. Zohdy M. Karam Microelectronics System Design Lab Department of Electrical and Systems Engineering Oakland University Rochester MI USA Department of Electrical and Systems Engineering Oakland University Rochester MI USA

In this article, we propose a dynamic recurrent approach to solve uncertain nonlinear algebraic equations. The approach is justified on the basis of net construction that recursively produces minimum neuron state energy which corresponds to the desired solution. Linearization via the Newton-Raphson method is employed in order to make the net converge to an appropriate region in the solution space. Some preliminary experimentation on non-trivial nonlinear examples are included and discussed. Approaches for hardware implementation of the recurrent dynamic neural network are presented. Comparison between a totally digital chip design and a hybrid analog/digital implementation utilizing MOSIS facilities is made. Evaluation and simulations on system, logic, and circuit levels are emphasized.

关键词： Nonlinear equations Neurons Newton method Neural network hardware Nonlinear dynamical systems Neural networks Recurrent neural networks Chip scale packaging Circuit simulation Logic circuits

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Bio-Inspired Adaptive Integrated Information Processing

Bio-Inspired Adaptive Integrated Information Processing

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Hoda S. Abdel-Aty-Zohdy Microelectronics Systems Design Laboratory Department of Electrical and Computer Engineering Oakland University Rochester MI USA

Biological brains are dramatically more effective in dealing with real-world adaptive information processes and decisions than most advanced computers. Advanced computers can utilize the discipline of classical signal processing whereby providing theoretical mathematical and statistical approaches for information processing, and with the vision of bio-inspired adaptive processing are evolving into neuromorphic integrated sensory processing systems. Numerous MISO (multi-input, single output) sensory applications demand reliable and effective information processing which, at an initial stage may be addressed by solving the key problems of advanced computing platforms, which are: i-massive parallelism; ii-low power consumption of massively large systems; iii-intelligent systems that learn from observations and perform better on the next run; iv-integrated systems for embedded feasibility; and v-systems that adapt to the environment. Thus, our Adaptive Integrated Information Processing (AIIP) approaches, presented in this paper. Two AIIP systems are presented: Neural networks with synaptic plasticity, as our Spiking Neural Networks (SNNs), with up to one million inputs, for chemical sensing and detection; and Adaptive Recurrent Dynamic Neural Networks (ARDNNs) for defect tracking, reliable system deployment, and prognosis for telecommunication and similar applications. Further-our presented AIIP systems may provide a viable solution to offering powerful modulation schemes and transmission rates far beyond current possible communications systems.

关键词： Information processing Power system reliability Biological neural networks Neural networks Recurrent neural networks Biology computing Computer vision Adaptive signal processing Biomedical signal processing Neuromorphics

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Next-generation advances in cognitive processing using spiking neural networks for biochemical sensing, radar and rapid HDL

Next-generation advances in cognitive processing using spiki...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Hoda S. Abdel-Aty-Zohdy Jacob Allen Microelectronics Systems Design Laboratory Department of Electrical and Computer Engineering Oakland University Rochester MI USA

ISBN: (纸本)9781424444946

This invited plenary paper introduces a novel spiking neural network methodology, and applies it to an odorant learning, medical and radar detection applications. Rapid HDL is introduced as a 15 minute rapid prototyping approach, where real-time implementations will be demoed on FPGAs. The 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.

关键词： Next generation networking Neural networks Radar Hardware design languages Signal design Signal processing Signal processing algorithms Biomedical signal processing Field programmable gate arrays Electronic noses

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Neural networks for pattern discovery and optimization in signal processing and applications

Neural networks for pattern discovery and optimization in si...

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Canadian Conference on electrical and Computer engineering (CCECE)

作者： H.S. Abdel-Aty-Zohdy M.A. Zondy Microelectronics System Design Laboratory Electrical and Systems Engineering Oakland University Rochester MI USA Center for Advanced Automation Electrical and Systems Engineering Oakland University Rochester MI USA

The article is intended to advance both conceptual and implementation techniques of supervised and discovery-driven neural networks in noisy time varying signal processing applications. Successful neural networks and their significance in applications are based on; selection of proper theoretical algorithms for learning, appropriate selection of the sequencing of signal processing tasks, and efficient VLSI system implementation. We present a pattern discovery self organizing feature map (SOFM), followed by a recurrent dynamic neural network (RDNN) algorithm for signal representation and processing. This approach combines the benefits of RDNNs with its SOFM counter part. Preliminary designs, implementations, test results and validation of silicon-chips for each of the above neural network approach are also presented.

关键词： Neural networks Intelligent networks Signal processing Signal processing algorithms Multidimensional signal processing Organizing systems engineering and theory Very large scale integration Recurrent neural networks Stochastic processes

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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 L. Liou J.B. Moncrief Information Directorate US Air Force Research Laboratory OH USA Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI 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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New genetic algorithm approach for dynamic biochemical sensor measurements characterization

New genetic algorithm approach for dynamic biochemical senso...

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： D. Gantla H.S. Abdel-Aty-Zohdy R.L. Ewing Microelectronics System Design Laboratory Department of Electrical & Systems Engineering Oakland University Rochester MI USA Wright Patterson Air Force Base OH USA

In an olfaction system (E-Nose) hardware implementation, outputs from the GA approach are used as inputs to an intelligent neural network system for biochemical detection and decision-making. In this paper we present a Genetic Algorithm for measurement characterization with dynamic inputs. Input measurements are from a given range and are assumed in parallel from chemical-sensor array. An input multiplexer/controller/Analog-Digital converter preprocessing stage is used to control these input measurements. The new dynamical approach presents measurement characterization and also optimum fused measurements without loosing the integrity of incoming signals. Through a novel mutation and crossover approach (Half Sibling and A Clone) optimum characteristic weight chromosomes are achieved. HSAC represents both crossover and mutation. A key feature of the new approach is that no pre-assigned minimum error is specified, rather error is dynamically evaluated based on measurements. Simulation results of the new GA with dynamic measurements are compared with one of the approaches from GAlib (A library of genetic Algorithm approaches from MIT) and proved the new approach has minimum error and a early convergence. MATLAB has been used as the simulation tool.

关键词： Genetic algorithms Biosensors Genetic mutations Neural network hardware Intelligent systems Intelligent networks Intelligent sensors Neural networks Decision making Chemicals

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