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172
Analog VLSI signal processing: why, where, and how
 Analog Integrated Circuits and Signal Processing 6
, 1994
"... Analog VLSI signal processing is most effective when precision is not required, and is therefore an ideal solution for the implementation of perception systems. The possibility to choose the physical variable that represents each signal allows all the features of the transistor to be exploited oppor ..."
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Cited by 40 (1 self)
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Analog VLSI signal processing is most effective when precision is not required, and is therefore an ideal solution for the implementation of perception systems. The possibility to choose the physical variable that represents each signal allows all the features of the transistor to be exploited opportunistically to implement very dense time and amplitudecontinuous processing cells. This paper describes a simple model that captures all the essential features of the transistor. This symmetrical model also supports the concept of pseudoconductance which facilitates the implementation f linear networks of transistors. Basic combinations of transistors in the current mirror, the differential pair and the translinear loop are revisited as support material for the description of a variety of building blocks. These examples illustrate the rich catalogue of linear and nonlinear operators that are available for local and collective analog processing. The difficult problem of analog storage is addressed briefly, as well as various means for implementing the necessary intrachip and interchip communication.
A Survey on Cellular Automata
, 2003
"... A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. Researchers, scientists and practitioners from different fields have exploited the CA paradigm of local information, decentralized contr ..."
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Cited by 35 (0 self)
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A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. Researchers, scientists and practitioners from different fields have exploited the CA paradigm of local information, decentralized control and universal computation for modeling different applications. This article provides a survey of available literature of some of the methodologies employed by researchers to utilize cellular automata for modeling purposes. The survey introduces the different types of cellular automata being used for modeling and the analytical methods used to predict its global behavior from its local configurations. It further gives a detailed sketch of the efforts undertaken to configure the local settings of CA from a given global situation; the problem which has been traditionally termed as the inverse problem. Finally, it presents the different fields in which CA have been applied. The extensive bibliography provided with the article will be of help to the new entrant as well as researchers working in this field.
Pattern Formation and Spatial Chaos in Lattice Dynamical Systems: II
"... We survey a class of continuoustime lattice dynamical systems, with an idealized nonlinear. We introduce a class of equilibria called mosaic solutions, which are composed of the elements 1, \Gamma1, and 0, placed at each lattice point. A stability criterion for such solutions is given. The spatial ..."
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Cited by 31 (6 self)
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We survey a class of continuoustime lattice dynamical systems, with an idealized nonlinear. We introduce a class of equilibria called mosaic solutions, which are composed of the elements 1, \Gamma1, and 0, placed at each lattice point. A stability criterion for such solutions is given. The spatial entropy h of the set of all such stable solutions is defined, and we study how this quantity varies with parameters. Systems are qualitatively distinguished according to whether h = 0 (termed pattern formatio), or h ? 0 (termed spatial chaos). Numerical techniques for calculating h are described. 1. Mosaic Solutions As described in the companion paper [4], we study the phenomenon of pattern formation and spatial chaos in lattice dynamical systems. In order for us to see these phenomena globally, we consider a special class of equilibrium solutions, called mosaic solutions, introduced in [5], and studied there and in [6]. We work here with the system (1:1) u i;j = \Gammafi + \Delta + ...
AER Image Filtering Architecture for VisionProcessing Systems
 IEEE Trans. Circuits Syst. I, Fundam. Theory Appl
, 1999
"... A VLSI architecture is proposed for the realization of realtime twodimensional (2D) image filtering in an addressevent representation (AER) vision system. The architecture is capable of implementing any convolutional kernel F (x; y) as long as it is decomposable into xaxis and yaxis components ..."
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Cited by 30 (8 self)
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A VLSI architecture is proposed for the realization of realtime twodimensional (2D) image filtering in an addressevent representation (AER) vision system. The architecture is capable of implementing any convolutional kernel F (x; y) as long as it is decomposable into xaxis and yaxis components, i.e., F (x; y)=H(x)V (y), for some rotated coordinate system fx; yg and if this product can be approximated safely by a signed minimum operation. The proposed architecture is intended to be used in a complete vision system, known as the boundary contour system and feature contour system (BCSFCS) vision model, proposed by Grossberg and collaborators. The present paper proposes the architecture, provides a circuit implementation using MOS transistors operated in weak inversion, and shows behavioral simulation results at the system level operation and some electrical simulations. Index TermsAnalog integrated circuits, communication systems, convolution circuits, Gabor filters, image anal...
Asymptotic and periodic boundary values problems of mixed PDEs and wave solutions of lattice differential equations
, 1997
"... We discuss the existence and approximation of solutions of asymptotic or periodic boundary value problems of mixed functional differential equations. Our approach is via monotone iteration and nonstandard ordering in the profile set for asymptotic boundary value problems and via S 1degree and equi ..."
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Cited by 30 (3 self)
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We discuss the existence and approximation of solutions of asymptotic or periodic boundary value problems of mixed functional differential equations. Our approach is via monotone iteration and nonstandard ordering in the profile set for asymptotic boundary value problems and via S 1degree and equivariant bifurcation theory for periodic boundary value problems. Applications will be given to wave fronts and to slowly oscillatory spatially periodic traveling waves of lattice delay differential equations arising from population genetics, population dynamics, and neural networks. 1997 Academic Press 1.
Training cellular automata for image processing
 IEEE Transactions on Image Processing
"... Abstract. Experiments were carried out to investigate the possibility of training cellular automata to to perform processing. Currently, only binary images are considered, but the space of rule sets is still very large. Various objective functions were considered, and sequential floating forward sea ..."
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Cited by 27 (6 self)
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Abstract. Experiments were carried out to investigate the possibility of training cellular automata to to perform processing. Currently, only binary images are considered, but the space of rule sets is still very large. Various objective functions were considered, and sequential floating forward search used to select good rule sets for a range of tasks, namely: noise filtering, thinning, and convex hulls. Several modifications to the standard CA formulation were made (the Brule and 2cycle CAs) which were found to improve performance. 1
CnnBased DifferenceControlled Adaptive Nonlinear Image Filters
 International Journal of Circuit Theory and Applications
, 1998
"... : In this paper, we develop a common cellular neural network framework for various adaptive nonlinear filters based on robust statistic and geometrydriven diffusion paradigms. The base models of both approaches are defined as differencecontrolled nonlinear CNN templates while the selfadjusting ..."
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Cited by 22 (1 self)
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: In this paper, we develop a common cellular neural network framework for various adaptive nonlinear filters based on robust statistic and geometrydriven diffusion paradigms. The base models of both approaches are defined as differencecontrolled nonlinear CNN templates while the selfadjusting property is ensured by simple analogic (analog and logic) CNN algorithms. Two adaptive strategies are shown for the order statistic class. When applied to the images distorted by impulse noise both give more visually pleasing results with lower frequency weighted mean square error than the median base model. Generalizing a variational approach we derive the constrained anisotropic diffusion, where the output of the geometrydriven diffusion model is forced to stay close to a predefined morphological constraint. We propose a coarsegrid CNN approach that is capable of calculating an acceptable noiselevel estimate (proportional to the variance of the Gaussian noise) and controlling t...
Dynamics Of Lattice Differential Equations
"... . In this paper recent work on the dynamics of lattice differential equations is surveyed. In particular, results on propagation failure and lattice induced anisotropy for traveling wave or plane wave solutions in higher space dimensions spatially discrete bistable reactiondiffusion systems are con ..."
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Cited by 18 (6 self)
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. In this paper recent work on the dynamics of lattice differential equations is surveyed. In particular, results on propagation failure and lattice induced anisotropy for traveling wave or plane wave solutions in higher space dimensions spatially discrete bistable reactiondiffusion systems are considered. In addition, analysis of and spatial chaos in the equilibrium states of spatially discrete reactiondiffusion systems are discussed. Key words. lattice differential equations, traveling wave solutions, propogation failure, lattice anisotropy, equilibrium solutions, stability, spatial entropy Abbreviated title. Lattice Differential Equations 1 School of Mathematics, Georgia Institute of Technology, Atlanta, Georgia 30332 USA. The work of this author was supported in part by ARO Contract DAAH0493G0199 and by NSF Grant DMS9005420. 2 Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912 USA. The work of this author was supported in part by NSF Grant D...
Robust multicellular developmental design
 In GECCO ’07: Proc. of the 9th Annual Conference on Genetic and Evolutionary Computation
, 2007
"... This paper introduces a continuous model for Multicellular Developmental Design. The cells are fixed on a 2D grid and exchange ”chemicals ” with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenoty ..."
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Cited by 17 (6 self)
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This paper introduces a continuous model for Multicellular Developmental Design. The cells are fixed on a 2D grid and exchange ”chemicals ” with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not predetermined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the ’flags ’ problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multicellular organisms exhibit almost perfect selfhealing characteristics.
Restoration and Enhancement of Fingerprint Images Using MLattice  A Novel NonLinear Dynamical System
 Novel NonLinear Dynamical System, Proc. 12th ICPRB, Jerusalem
, 1994
"... In this paper we develop a method for the simultaneous restoration and halftoning of scanned fingerprint images using a novel nonlinear dynamical system, called the "Mlattice system". This system is rooted in the reactiondiffusion model, first proposed by Turing in 1952 to explain the fo ..."
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Cited by 15 (2 self)
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In this paper we develop a method for the simultaneous restoration and halftoning of scanned fingerprint images using a novel nonlinear dynamical system, called the "Mlattice system". This system is rooted in the reactiondiffusion model, first proposed by Turing in 1952 to explain the formation of animal patterns such as zebra stripes and leopard spots. A typical reactiondiffusion system is a set of heat equations, coupled by nonlinear reaction terms. The new Mlattice system is closely related to the analog Hopfield network and the cellular neural network, but has more flexibility in how its variables interact. Furthermore, the state variables of an Mlattice system are guaranteed to be bounded, which is not the case with many reactiondiffusion systems. Due to this largesignal boundedness, the Mlattice system possesses desirable numerical properties that make it useful in engineering applications. Our new method for enhancing fingerprints explores the ability of the Mlattice ...