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检索条件"机构=Circuits and Systems and Artificial Neural Networks Laboratory"

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A data-derived quadratic independence measure for adaptive blind source recovery in practical applications

A data-derived quadratic independence measure for adaptive b...

引用

Midwest Symposium on circuits and systems (MWSCAS)

作者： K. Waheed F.M. Salam Circuits Systems and Artificial Neural Networks Laboratory Michigan State University East Lansing MI USA

We present a novel performance index to measure the statistical independence of data sequences and apply it to the framework of blind source recovery (BSR) that includes blind source separation, deconvolution and equalization. This performance index is capable of measuring the mutual independence of data sequences directly from the data. This information theoretic; quadratic independence measure (QIM) is derived based on Renyi's quadratic entropy estimated by a finite data length Parzen window using a Gaussian kernel. Simulation results are presented to validate the performance of the proposed benchmark and compare it with other established benchmarks.

关键词： Signal to noise ratio Extraterrestrial measurements Deconvolution Performance analysis Blind source separation Blind equalizers Entropy Signal processing algorithms Transfer functions Intersymbol interference

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Cascaded structures for blind source recovery

Cascaded structures for blind source recovery

引用

Midwest Symposium on circuits and systems (MWSCAS)

作者： K. Waheed F.M. Salam Circuits Systems and Artificial Neural Networks Laboratory Michigan State University East Lansing MI USA

Blind Source Recovery (BSR) is an interesting autonomous and unsupervised stochastic adaptation problem that includes the well-known blind adaptive problems of Blind Source Separation (BSS), Deconvolution (BSD) and Equalization (BSE). BSR includes also the nonlinear case and hence focuses on reproducing or estimating the source signals even if environment identification is not achieved. A number of outstanding research contributions have been made in this field, however, the issues of application are still in their infancy. Most of the BSR algorithms have characteristics, which make them suitable for a particular subclass of problems. In order to develop a generalized source recovery framework and yet achieve optimal performance in all cases, there is a need to explore further architectural and/or algorithmic domains. In this paper, we approach this goal in the architecture domain by focusing on the use of cascaded structures for BSR. The paper discusses the need, choice, possible forms and properties of several cascaded structures. Some illustrative simulations have been included to highlight the advantages of some of the proposed structures.

关键词： circuits artificial neural networks Laboratories Stochastic systems Blind source separation Source separation Deconvolution Adaptive equalizers Blind equalizers Signal processing

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State space blind source recovery for mixtures of multiple source distributions

State space blind source recovery for mixtures of multiple s...

引用

IEEE International Symposium on circuits and systems (ISCAS)

作者： K. Waheed F.M. Salam Circuits Systems and Artificial Neural Networks Laboratory Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

The paper discusses state space blind source recovery (BSR) for minimum phase and non-minimum phase mixtures of Gaussian and non-Gaussian distributions. The state space natural gradient approach results in compact iterative update laws for BSR. Two separate state space algorithms for minimum phase and non-minimum phase mixing environments are presented. The advantages and disadvantages of both algorithms in the context of multiple source distribution mixtures are examined. The presented BSR algorithms require use of nonlinearities, which depend on the distribution of the unknown sources. We propose use of an adaptive nonlinearity based on the batch kurtosis of the output. This renders the adaptive estimation of the demixing network completely blind.

关键词： State-space methods Signal processing algorithms Iterative algorithms Gaussian distribution circuits artificial neural networks Laboratories Iterative methods Adaptive estimation Adaptive signal processing

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A mixed mode neuro-VLSI chip for high speed applications

A mixed mode neuro-VLSI chip for high speed applications

引用

Midwest Symposium on circuits and systems (MWSCAS)

作者： K. Waheed F.M. Salam Circuits Systems And Neural Networks Laboratory Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (纸本)078037150X

This paper presents selected design and operation details of a custom integrated neural chip. This neural processing chip is designed in the recent 0.18 /spl mu/m, single poly, six-layer Cu interconnect technology. The chip realizes an architecture that achieves the task of autonomous-learning execution times in micro to milli seconds. The core consists of basic building blocks of 4-quadrant multipliers, transconductance amplifiers, and active load resistances, for analog (forward) network processing and learning modules. Super-imposed on the processing network are digital memory and control modules composed of D-Flipflops, ADC, Multiplying D/A Converter (MDAC), decoders, multiplexers and comparators for parameter (weight) storage, logical control and analog/digital conversions.

关键词： Copper Integrated circuit interconnections Very large scale integration neural networks Process design LAN interconnection Communication system control Testing Application software Laboratories

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Sensor errors prediction using neural networks

Sensor errors prediction using neural networks

引用

International Joint Conference on neural networks (IJCNN)

作者： A. Sachenko V. Kochan V. Turchenko V. Golovko J. Savitsky A. Dunets T. Laopoulos Research Laboratory of Automated Systems and Networks Temopil Academy of National Economy Ternopil Ukraine Laboratory of Artificial Neural Networks Department of Electronics and Mechanics Brest Polytechnic Institute Brest France Electronics Laboratory Physics Department Aristotle University of Thessaloniki Thessaloniki Greece

The features of neural networks used for increasing the accuracy of physical quantity measurement are considered by prediction of sensor drift. The technique of data volume increasing for predicting neural network training is offered at the expense of various data types replacement for neural network training and at the expense of the separate approximating neural network use.

关键词： neural networks artificial neural networks Calibration Laboratories Electronic mail Signal processing Testing Sensor systems Intelligent sensors Neurons

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Features of intelligent distributed sensor network higher level development

Features of intelligent distributed sensor network higher le...

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IEEE Instrumentation and Measurement Technology Conference

作者： A. Sachenko V. Kochan V. Turchenko T. Laopoulos V. Golovko L. Grandinetti Ternopil Academy of National Economy Institute of Computer Information Technologies Ternopil Ukraine Electronics Laboratory Physics Department Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Artificial Neural Networks Departments of Computers Brest Polytechnic Institute Brest France Department of Electronics Informatics and Systems Parallel Computing Laboratory Cosenza Italy

The functions and software structure of the higher level of intelligent distributed sensor network (IDSN) are considered. The main purpose of software functions is reaching high accuracy of the data acquisition and processing using neural networks. The proposed client-server software architecture provides effective use of computing resources of the IDSN central computer.

关键词： Intelligent sensors Intelligent networks Sensor phenomena and characterization neural networks Computer networks artificial intelligence Concurrent computing Electronic mail Data acquisition Distributed computing

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A neuro-chip for real-time learning, processing and control

A neuro-chip for real-time learning, processing and control

引用

IEEE International Symposium on circuits and systems (ISCAS)

作者： F.M. Salam Circuits Systems and Artificial Neural Networks Laboratory Department of Electrical Engineering Michigan State University East Lansing MI USA

We present results of several experiments of a neural network with temporal learning on a single chip. The single chip configuration is a 2-D scalable array with learning constructed via simple modular CMOS building blocks. We report on learning prototype periodic temporal signals which may be displayed as a closed curve, a figure-8 shape, or an arbitrary periodic or aperiodic shapes in the state space. The experiments demonstrate the instantaneous learning by the chip of these shapes.

关键词： Process control Neurons Testing Shape Frequency Displays Recurrent neural networks Differential equations Time measurement Limit-cycles

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A neuro-chip with temporal learning: test results for signal/shape generation

A neuro-chip with temporal learning: test results for signal...

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Asilomar Conference on Signals, systems & Computers

作者： F.M. Salam Circuits Systems and Artificial Neural Networks Laboratory Department of Electrical Engineering Michigan State University East Lansing MI USA

We briefly describe a recently designed neural network with temporal learning. The developed learning rule extends the temporal learning to realize a forward instantaneous update scheme, suitable for complete analog hardware implementation. The single chip configuration is a two-dimensional scalable array with learning constructed via simple scalable CMOS building blocks. We report on extensive test results of learning prototype periodic temporal signals which may be displayed as a closed curve or a figure-8 shape in the state space. Several experimental tests demonstrating the instantaneous learning by the chip of these shapes are reported.

关键词： Testing Shape Signal generators Recurrent neural networks artificial neural networks Hardware Signal processing algorithms State-space methods Signal processing Control systems

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Algorithms for blind signal separation and recovery in static and dynamic environments

Algorithms for blind signal separation and recovery in stati...

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IEEE International Symposium on circuits and systems (ISCAS)

作者： A.B. Gharbi F.M. Salam Circuits Systems and Artificial Neural Networks LaboratoryDepartment of Electrical Engineering Michigan State University East Lansing MI USA

We propose update laws for the problem of blind separation in static and dynamic environments. The energy function is based on an approximation of the mutual information as a measure of independence. Both feedforward and feedback structures of the neural network are considered. A general framework to develop the update law for the dynamic model is proposed. Computer simulations to support the analytical work are provided.

关键词： Blind source separation Mutual information Energy measurement State-space methods State feedback IIR filters Filtering Linear systems Intelligent networks circuits

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Formulation and algorithms for blind signal recovery

Formulation and algorithms for blind signal recovery

引用

Midwest Symposium on circuits and systems (MWSCAS)

作者： F.M. Salam A.B. Gharbi G. Erten Circuits Systems and Artificial Neural Networks Laboratory Department of Electrical Engineering Michigan State University East Lansing MI USA Michigan State University East Lansing MI US Dept. of Electr. Eng. Michigan State Univ. East Lansing MI USA IC Tech Inc. Okemos MI USA

We review some recent approximations of the averaged mutual information criterion and its use as a measure of signal independence. We describe an update law and its comparison with previous work in the literature. We also identify the link between the minimization of the mutual information and the information-maximization of the output entropy function of a (nonlinear) neural network. Example simulations demonstrate the performance of our recently developed algorithm in static and dynamic environments.

关键词： Mutual information neural networks Finite impulse response filter Density measurement circuits artificial neural networks Laboratories Aging Electric variables measurement Minimization

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