Cellular neural networks for real-time DNA microarray analysis.

A CAD tool based on SPICE macromodels to simulate simplified faulty, circuit realizations of a fully programmable, two dimensional cellular neural network (CNN) is presented. The models can be easily adapted to match the electrical parameters of real circuit implementations. Generic macromodels for both current mode and voltage mode CNNs are provided. The macromodels not only simulate the conceptual CNN cell, but also provide the capability to model actual CNN architectures and their nonidealities. Moreover, macromodeling provides the capability to determine the effect of parameter variation on the operation of the CNN efficiently without the need for computationally expensive, exhaustive circuit simulations. We have used the CNN macromodels to develop robust testing strategies for detecting faults in VLSI implementations of CNN arrays. Three fault cases are introduced into a CNN array to provide insight to the usefulness of macromodeling. >

The present of Speckle noise affects the exploitation of useful information in images, especially, in the ultrasound and SAR image. It is desirable to remove speckle noise in real time processing without damaging the image features. In this paper, we propose a new method to suppress the speckle noise in real- time by using Cellular Neural Network (CNN) technology. Section 2, CNN and Cellular Neural Network Universal Machine (CNN-UM) will be briefly introduced, which are the optimized technology for real time image processing applications. Section 3, a few mathematical models of anisotropic diffusion methods for reducing speckle noise on the SAR images will be surveyed and analyzed. In section 4, is also the main part, presented how to design a speckle denoising filter to eliminate the speckle noise and enhance the image features by converting the conventional denoising algorithm to templates, that make CNN chip understandable and processes as a real-time speckle reducing filter. The experiment's results are given in section 5 and some conclusions and future works are summarized in the last section.

As an attempt to introduce interactive, content‐dependent adaptive (ICDA) image processing, a simple but powerful active image sensing and two image enhancement methods are introduced via adaptive CNN‐UM sensor‐computers. Thus, the method ICDA can be used for adaptive control of image sensing and for subsequent on‐line or off‐line image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN‐UM chip. Our first image enhancement method is also executable on this chip, but it is more suitable for the adaptive cellular neural network universal machine (ACNN‐UM) architecture. Some results of simulator and chip experiments and an adaptive extended cell are presented. Our second, dynamical image enhancement method is planned to be executable on a multi‐layer, complex cell CNN architecture. In (Proceedings of the 6th IEEE International Workshop on Cellular Neural Networks and their Applications(CNNA‐2000) Catania, 2000; 213–217) 3‐layer architecture is described which is capable of realizing the main part of the second enhancement method.The main issues of our paper are as follows: the novel outlook of the ICDA framework, three new methods for two key application areas of CNN‐UM, the notion of ‘regional’ adaptive computing, the novelty of application of equilibrium‐computing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. Copyright © 2002 John Wiley & Sons, Ltd.

A two-dimensional (2-D) cellular neural network (CNN) array of resistively coupled Chua circuits which can be designed to implement some elementary aspects of spatial recognition, namely, distinguishing open curves from closed ones and locating the shortest path between two locations, is described. In the latter, two situations are analyzed: flat and wrinkled surfaces. The 2-D CNN array of Chua circuits is shown to be capable of finding the shortest path between two points on a wrinkled labyrinth. The performance of this parallel processing approach was examined using computer simulations, although this method can be implemented in real time via VLSI technology. >

In case of the 2D cellular automata (CA) the whole rule f can be considered the set of sub-junctions grouped due to the number of "ones" in the neighbourhood. Such decomposition enables indication of sub-functions, which are more important for the global dynamics of the automaton then others. The cellular automata can be implemented on a cellular neural network (CNN). The simplest way of such implementation on the CNN universal machine was proposed by Cronuse and Chua (1995). We present the modification of this method due to which the information about sub-functions can be saved; and from this we are able to simplify the architecture of a CNN-universal machine implementing CA. Furthermore, the shortage of the time of a new state evaluation is possible. The advantage of the modified CA implementation is the possibility to receive the new automaton by manipulation of this part of the structure, which is connected with the sub-function.

By exploiting the Cellular Neural Network paradigm, this paper present a new object-oriented segmentation algorithm that takes into account the hardware characteristics of the Cellular Neural Networks Universal Machine. In particular, by using a rigorous model of the image contours, this paper focuses on the edge extraction phase as well as on the performance evaluations. Simulation results, carried out for Stefan, Coast Guard, Car-phone and Football video sequences, show the effectiveness of the approach developed herein.

A new one-chip texture-classifier system is demonstrated with an execution time of about a few /spl mu/secs. Cellular neural networks (CNN) provide a new fast parallel computational method for VLSI image processing. CNN contain one or more layers of 2D cell-arrays with local cell-interconnections and in-cell dynamics. In this paper it is demonstrated that many of the feature mapping early vision effects can be simultaneously executed as convolution/deconvolution, cross-correlation, pattern-shifting, halftoning etc. The parameters of the CNN (a template) are trained through a genetic-like learning algorithm. Several types of Brodatz textures have been examined to test our method for classification and segmentation. Choosing 4 Brodatz textures which are close to each other in their main characteristics, they can be discriminated at a classification error of about 1-5% and segmentation error of about 5-10 pixels by using only one CNN template, even in the case of noisy or nonuniform CNN VLSI parameters. Using a 44*44 CNN array and one template, 16 Brodatz textures can be successfully discriminated.

Cellular neural network (CNN) implementations used in real time image processing are rapidly generated by a formal High-level synthesis (HLS) toolset in this work. Industrial and academic hardware design projects can benefit from the use of the Custom Coprocessors Compilation (CCC) HLS behavioral synthesise, via the massive reduction in design and verification time. The CCC tool is built with formal compiler-compiler, Logic Programming and XML validation techniques, thus the generated RTL VHDL or Verilog models are provably-correct. In this work, we rapidly modeled CNNs in the ADA programming language, compiled and verified along with all the necessary testbenches in GNU ADA. Edge-detection, halftoning and morphological processing applications were prototyped, so as to evaluate the CCC HLS method, which was the main contribution of this work. The combination of Logic Programming and RDF validation techniques, as well as prototyping of CNN applications made this work unique.

In this paper a new strategy for real time detection of plasma instabilities, called MARFEs, is proposed through real-time image processing on plasma video sequences. These sequences are recorded by a vision system based on a charge coupled device (CCD) camera installed at Frascati Tokamak Upgrade (FTU). The strategy used to perform the task is based on a new family of nonlinear analog processors, digitally programmable, implemented into the so-called Cellular Neural Network Universal Machine (CNN-UM). The basic idea deals with a system capable to carry out safer nuclear fusion experiments, preventing the plant from excessive mechanical and thermal stress which occurs during plasma instability phenomena (i.e., disruptions). Experimental results, obtained on the FTU machine, are reckoned fully satisfactory.

A practical system approach for time-multiplexing cellular neural network (CNN) implementations suitable for processing large and complex images using small CNN arrays is presented. For real size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware cells and all the pixels in the image involved. This paper presents a practical solution by processing the input image, block by block, with the number of pixels in a block being the same as the number of CNN cells in the array. Furthermore, unlike other implementations in which the output is observed at the hard-limiting block, the very large scale integrated (VLSI) architecture hereby described monitors the outputs from the state node. While previous implementations are mostly suitable for black and white applications because of the thresholded outputs, our approach is especially suitable for applications in color (gray) image processing due to the analog nature of the state node. Experimental complementary metal-oxide-semiconductor (CMOS) chip results in good agreement with theoretical results are presented.

Cellular neural networks (CNNs) are a powerful analog architecture that can outperform traditional von Neumann architecture for spatio-temporal information processing applications, e.g., image processing and speech recognition. Much existing work reports energy dissipation for CNNs at the chip level, which includes dissipation of sensors, actuators, and other components. As such, the impacts of various system variables, e.g., application templates, characteristics of the resistive element, etc., on the energy profile of a CNN cannot be easily determined. In this work, we propose analytical models to estimate CNN power and performance (measured by settling time). Power dissipations, and settling times obtained via the models for different linear, and non-linear characteristics are verified through circuit simulation. Simulation results show that the proposed models predict power dissipation and settling time with less than 1% and 3% errors, respectively. By using these models, we have also performed case studies for a tactile sensing problem, and a pattern recognition problem to compare power and performance between tunneling field effect transistor (TFET) based non-linear CNN and conventional linear resistor based CNN.

Many complex scientific problems can be formulated with a regular 2-D or 3-D grid. Direct interaction between the signals on various grid points is limited within a finite local neighborhood, which is sometimes called the receptive field. The original cellular neural network (CNN) paradigm was first proposed by Chua and Yang in 1988 [1, 2]. The two most fundamental ingredients of the CNN paradigm are: the use of analog processing cells with continuous dsignal values, and local interaction within a finite radius. Many results on the algorithm development, VLSI implementations of CNN systems are reported in the first three IEEE International Workshops on Cellular Neural Networks and Their Applications (Budapest, Hungary, 1990; Munich, Germany, 1992; Rome, Italy, 1994); the book entitled: Cellular Neural Networks, which was edited by T. Roska, J. Vandewalle; and papers published in IEEE Trans. on Circuits and Systems, and other IEEE journals and conference proceedings.

In this paper a VLSI implementation of a 3 × 3 cell digitally programmable cellular neural networks (CNN) with discrete templates is presented. This chip is the first successfully tested, multichip-oriented CNN chip. In fact, this chip has been designed to be easily interconnected to others to carry out very large CNN arrays. This feature has been verified in a two-chip prototype board. The fully programmable capability covers most of the available one-neighbourhood fixed templates.

As an important tool to study practical problems of biology, engineering and image processing, the cellular neural networks (CNNs) has caused more and more attention. Some interesting results about the existence for cellular neural networks have been obtained. In this paper, by means of iterative analysis, the existence of periodic solution for Hopfield cellular neural networks are considered. Some new results are obtained.

As an important tool to study practical problems of biology, engineering and image processing, the cellular neural networks (CNNs) has caused more and more attention. Some interesting results about the existence of solution for cellular neural networks have been obtained. In this paper, by means of iterative analysis, the existence of periodic solution and the uniform stability of the equilibrium point of impulsive cellular neural networks with distributed delays are considered. Some new results are obtained.