Cellular Neural/nonlinear Network Research Articles

Robust Design for Generalized Point Extract CNN with Application in Image Processing

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, as well as robotic and biological visions. The designs for CNN templates are one of the important issues for the practical applica- tions of CNNs. This paper first describes and proves the local rules of the binary Point Extract (PE) CNN introduced by Roska et al., then extends the PE CNN to a gray similar neighborhood pixel remover (SNPR) CNN. The robust design theorem of the SNPR CNN has been established, using a PE CNN and a SNPR processes several images. The results agree with theoretical predictions. In particular, combining the SNPR CNN with median filtering approach is able to re- move the salt & pepper noise in images.

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

The Bio-inspired (Bi-i) Cellular Vision System is a computing platform consisting of sensing, array sensing-processing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN) paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy.

Application of the edge of chaos domain of the Zhabotinskii CNN to explore insights to hydrothermal deposit-forming processes

To gain insight into complex processes in hydrothermal deposit-forming systems, we mapped the Zhabotinskii model onto a two-dimensional reaction-diffusion CNN (cellular neural/nonlinear network) of two state variables and two diffusion coefficients. The edge of chaos domain of the Zhabotinskii CNN was numerically determined according to a theory of complexity. The simulation of dynamic systems, with parameters taken from the edge of chaos domain as described in this study, can generate some interesting distribution patterns of component concentrations that plausibly characterize certain complex phenomena involved in hydrothermal mineralization.

ROBUST DESIGNS FOR GRAY-SCALE GLOBAL CONNECTIVITY DETECTION CNN TEMPLATES

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, as well as robotic and biological visions. Practically, an engineer always hopes to design a CNN that has both universality and robustness. Based on research on the designs for the global connectivity detection (GCD) CNN [Chua, 1997] used in binary pattern, this paper establishes a theorem on robust designs for gray-scale global connectivity detection (GGCD) CNN templates. The theorem provides template parameter inequalities for determining parameter intervals for implementing the GCD functions. As a first example, two gray-scale labyrinth patterns with Gaussian noise are constructed. Using the GGCD, CNN designed by the theorem detects the connectivity of the two labyrinth patterns with gray-scales. In the other three examples, using GGCD CNNs simulate the spreads of an infectious diseases at nonuniform speeds.

Dense CMOS implementation of a binary‐programmable cellular neural network

AbstractAn implementation of a cellular neural/non‐linear network (CNN) for processing black‐and‐white (B/W) images is presented in which the template terms are 1‐bit programmable. Such approach leads to a very compact implementation of the coefficient circuits and fast (digital) programming. In this programming scheme, the more complex templates are split into subtasks that are run successively. The structure allows a direct or algorithmic evaluation of the majority of templates proposed for B/W images. The transient mask is utilized in performing the local logic operations as well as in template operations. The proposed architecture is suitable for high‐density implementations. A test structure of a 4 × 4 network has been implemented with a standard digital 0.18‐µm CMOS process. One cell occupies only 155 µm2, making possible the implementations of very large networks on a single chip. The algorithms used for the logic function computations and selected template evaluations are described, and the corresponding measurement results are shown. Copyright © 2006 John Wiley & Sons, Ltd.

Computational auditory scene analysis in cellular wave computing framework

AbstractExtracting substantive auditory objects from an auditory mixture is a widely studied and difficult problem in auditory research. Computational auditory scene analysis (CASA) aims at extracting objects in the frequency domain, based on a set of psychoacoustic grouping procedures. To mimic some aspects of the human auditory system a new cellular neural/non‐linear network (CNN)‐based library of procedures forming an Auditory Wave Computing Toolkit (AWCT) is presented here. Copyright © 2006 John Wiley & Sons, Ltd.

Robustness Design for CNN Templates with Performance of ExtractingClosed Domain

The cellular neural/nonlinear network (CNN) is a powerful tool forimage and video signal processing, robotic and biologicalvisions. This paper introduces a kind of CNNs with performance ofextracting closed domains in binary images, and gives a generalmethod for designing templates of such a kind of CNNs. Onetheorem provides parameter inequalities for determining parameterintervals for implementing prescribed image processing functions,respectively. Examples for extracting closed domains in binaryscale images are given.

Bio-inspired nano-sensor-enhanced CNN visual computer.

Nanotechnology opens new ways to utilize recent discoveries in biological image processing by translating the underlying functional concepts into the design of CNN (cellular neural/nonlinear network)-based systems incorporating nanoelectronic devices. There is a natural intersection joining studies of retinal processing, spatio-temporal nonlinear dynamics embodied in CNN, and the possibility of miniaturizing the technology through nanotechnology. This intersection serves as the springboard for our multidisciplinary project. Biological feature and motion detectors map directly into the spatio-temporal dynamics of CNN for target recognition, image stabilization, and tracking. The neural interactions underlying color processing will drive the development of nanoscale multispectral sensor arrays for image fusion. Implementing such nanoscale sensors on a CNN platform will allow the implementation of device feedback control, a hallmark of biological sensory systems. These biologically inspired CNN subroutines are incorporated into the new world of analog-and-logic algorithms and software, containing also many other active-wave computing mechanisms, including nature-inspired (physics and chemistry) as well as PDE-based sophisticated spatio-temporal algorithms. Our goal is to design and develop several miniature prototype devices for target detection, navigation, tracking, and robotics. This paper presents an example illustrating the synergies emerging from the convergence of nanotechnology, biotechnology, and information and cognitive science.

Design for CNN Templates with Performance of Global Connectivity Detection**The project supported by National Natural Science Foundation of China under Grant Nos. 60074034, 70271068, the Foundation for University Key Teachers, and the Research Fund for the Doctoral Program of Higher Education by the Ministry of Education of China under Grant No. 200200080004

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions. This paper discusses a general method for designing template of the global connectivity detection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing the corresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binary patterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns is given.

An object oriented segmentation on analog CNN chip

This paper introduces a real-time object oriented segmentation algorithm, designed and implemented on a new type of mixed analog/digital chip based on the cellular neural/nonlinear network (CNN) paradigm. The fully parallel architecture of the CNN processes all the pixels of an image at the same time, so the time spent for the image segmentation is independent of the number of objects in the image. This implementation of the segmentation algorithm is shown to well satisfy the real-time requirements both as a stand-alone processing procedure, and as a module inside the MPEG-4 video coding standard. Finally, the general purpose characteristics of the CNN universal chip allow to use the algorithm introduced as an efficient pre-processing procedure for many interesting image/video stand-alone applications.

A CNN framework for modeling parallel processing in a mammalian retina

AbstractWe present here a simple multi‐layer cellular neural/non‐linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN‐UM) chips. The basis of the model is a simple multi‐layer cellular neural/non‐linear Network (IEEE Trans. Circuits Systems 1988; 35:1257; IEEE Trans. Circuits Systems 1993; 40:147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different ‘feature detecting’ parallel representations (Nature 2001; 410:583–587). Our CNN model is capable of reproducing qualitatively the same full set of space–time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models. Copyright © 2002 John Wiley & Sons, Ltd.

An 0.5-μm CMOS analog random access memory chip for TeraOPS speed multimedia video processing

Data compressing, data coding, and communications in object-oriented multimedia applications like telepresence, computer-aided medical diagnosis, or telesurgery require an enormous computing power-in the order of trillions of operations per second (TeraOPS). Compared with conventional digital technology, cellular neural/nonlinear network (CNN)-based computing is capable of realizing these TeraOPS-range image processing tasks in a cost-effective implementation. To exploit the computing power of the CNN Universal Machine (CNN-UM), the CNN chipset architecture has been developed-a mixed-signal hardware platform for CNN-based image processing. One of the nonstandard components of the chipset is the cache memory of the analog array processor, the analog random access memory (ARAM). This paper reports on an ARAM chip that has been designed and fabricated in a 0.5-/spl mu/m CMOS technology. This chip consists of a fully addressable array of 32/spl times/256 analog memory registers and has a packing density of 637 analog-memory-cells/mm/sup 2/. Random and nondestructive access of the memory contents is available. Bottom-plate sampling techniques have been employed to eliminate harmonic distortion introduced by signal-dependent feedthrough. Signal coupling and interaction have been minimized by proper layout measures, including the use of protection rings and separate power supplies for the analog and the digital circuitry. This prototype features an equivalent resolution of up to 7 bits-measured by comparing the reconstructed waveform with the original input signal. Measured access times for writing/reading to/from the memory registers are of 200 ns. I/O rates via the l6-line-wide I/O bus exceed 10 Msamples/s. Storage time at room temperature is in the 80 to 100 ms range, without accuracy loss.

Image compression by orthogonal decomposition using cellular neural network chips

In the paper a new hardware architecture for the implementation of a high-speed, low bit-rate image coding system is outlined. Our proposed algorithm is based on the Cellular Neural/Nonlinear Network (CNN) chip-set. A simple and fast method is introduced to generate basis functions of 2 dimensional (2D) orthogonal transformations. Using these 2D basis functions of the Hadamard or Cosine functions, the transformation coefficients of the basic blocks of the image are measured by the CNN. Meanwhile, the CNN can produce the inverse transformation of the measured coefficients and the actual distortion-rate can be computed. If a required distortion-rate is reached, the coding process could be stopped (the use of even more coefficients would increase bit-rate needlessly). Effects of noise and VLSI computing accuracy are also considered to optimise the architecture. Hardware architecture and operational scheme of the CNN-based coding/decoding system. The CNN is the basic processor to measure the coefficients of the orthogonal transformation, while it calculates the inverse transformation as well. Error-rate and bit-rate are measured in-flight, as the coefficients of the spatial frequencies are estimated sequentially.

Passivity and complexity

Nature abounds with complex patterns and structures emerging from homogeneous media operating far from thermodynamic equilibrium. Such phenomena, which are widely observed in both inanimate (nonbiological) and biological media, can be modeled and studied via the CNN (cellular neural/nonlinear network) paradigm in an in-depth and unified way. Whether a homogeneous medium is capable of exhibiting depends on whether the CNN cells, or its couplings, are locally active in a precise circuit-theoretic sense. This local activity principle is of universal generality and is responsible for all symmetry breaking phenomena observed in a great variety of nonequilibrium media ranging from the nucleation of domain oscillations in bulk semiconductor materials (e.g., gallium arsenide in Gunn diodes) to the emergence of artificial life itself. The long forgotten yet classic P. R. (positive real) criteria is resurrected and given new prominence in this paper by invoking its negative version and deriving a set of analytical inequalities for calculating the parameter range necessary for the emergence of a nonhomogeneous static or dynamic pattern in a homogeneous medium operating under an influx of energy and/or matter. The resulting complexity related inequalities are applicable to all media, continuous or discrete, which have been mapped into a CNN paradigm.

CNN-based models for color vision and visual illusions

Cellular neural/nonlinear network (CNN) models are introduced to analyze and explain human early vision color perception and some of the visual illusions. A CNN model of Horn's algorithm is shown and a new lightness-determination method is introduced. CNN models for the brightness, the Mach-band, the Zollner, the cafe-wall, and the Poggendorff illusions are also introduced.

UNIVERSAL CNN CELLS

A cellular neural/nonlinear network (CNN) [Chua, 1998] is a biologically inspired system where computation emerges from a collection of simple nonlinear locally coupled cells. This paper reviews our recent research results beginning from the standard uncoupled CNN cell which can realize only linearly separable local Boolean functions, to a generalized universal CNN cell capable of realizing arbitrary Boolean functions. The key element in this evolutionary process is the replacement of the linear discriminant (offset) function w(σ)=σ in the "standard" CNN cell in [Chua, 1998] by a piecewise-linear function defined in terms of only absolute value functions. As in the case of the standard CNN cells, the excitation σ evaluates the correlation between a given input vector u formed by the outputs of the neighboring cells, and a template vector b, which is interpreted in this paper as an orientation vector. Using the theory of canonical piecewise-linear functions [Chua & Kang, 1977], the discriminant function [Formula: see text] is found to guarantee universality and its parameters can be easily determined. In this case, the number of additional parameters and absolute value functions m is bounded by m<2n-1, where n is the number of all inputs (n=9 for a 3×3 template). An even more compact representation where m<n-1 is also presented which is based on a special form of a piecewise-linear function; namely, a multi-nested discriminant: w (σ) =s (zm +| zm -1 +⋯ | z1 +| z0 +σ |||). Using this formula, the "benchmark" Parity function with an arbitrary number of inputs n is found to have an analytical solution with a complexity of only m =O ( log 2 (n)).

The computational infrastructure of analogic CNN computing. I. The CNN-UM chip prototyping system

A new computational paradigm is emerging for spatio-temporal problems: analogic cellular neural/nonlinear network (CNN) array-computing. It has all the software and hardware ingredients of the stored programmable computer (highlevel language, compiler, operating system, assembly and machine code, analogic central processing unit (CPU), analog and digital memory, peripherals, etc.). The elementary instructions and programming techniques, however, are drastically different from any other computers. These elementary instructions represent complex spatio-temporal nonlinear dynamic phenomena, including all the standard and exotic properties of simple image processing operators, as well as waves, patterns, and evolving systems. Meanwhile, a computational infrastructure has also emerged interfacing this revolutionary computing technology to digital systems as well as enabling the development of high-level software. The main goal was to provide easy-to-use tools and hardware interfacing elements and putting most of the sophistication in the chips and chip sets.