Robustness of cellular neural networks in image deblurring and texture segmentation

Template parameters of cellular neural networks (CNNs) should be robust enough to random variability of VLSI tolerances and noise. Using the CNN for image processing, one of the main problems is the robustness of a given task in a real VLSI chip. It will be shown that very different tasks such as 2D or 3D deconvolution and texture segmentation can be solved in a real VLSI CNN environment without significant loss of efficiency and accuracy under low precision (about 6–8 bits) and random variability of the VLSI parameters. The CNN turns out to be very robust against template noise, image noise, imperfect estimation of templates and parameter accuracy. The parameters of a template are tuned using genetic learning. These optimized parameters depend on the precision of the architecture. It was found that about 6–8 bits of precision is enough for a complicated multilayer deconvolution, while only 4 bits of precision is enough for difficult texture segmentation in the presence of noise and parameter variances. The tolerance sensitivity of template parameters is considered for VLSI implementation. Theory and examples are demonstrated by many results using real-life microscopic images and natural textures.     

On the universe of stable cellular neural networks

A cellular neural network (CNN) is a novel analogue circuit architecture with many desirable features. This paper extends previous stability results of CNNs to include classes of strictly sign-symmetric and acyclic templates. We show that most of the 3×3 strictly sign-symmetric templates are stable almost everywhere, with the unknown templates reduced to three classes. We also introduce template graphs and CNN graphs and utilize them to obtain results concerning stability and irreducibility of CNN templates.     

Cellular neural networks with opposite‐sign templates for image thinning

This paper presents image thinning algorithms using cellular neural networks (CNNs) with one‐ or two‐dimensional opposite‐sign templates (OSTs) as well as non‐unity gain output functions. Two four‐layer CNN systems with one‐dimensional (1‐D) OSTs are proposed for image thinning with 4‐ or 8‐connectivity, respectively. A CNN system, which consists of an eight‐layer CNN with two‐dimensional (2‐D) OSTs followed by another four‐layer CNN with 2‐D OSTs, is constructed for image thinning with 8‐connectivity, in which designs of B‐ and I‐templates are simpler than in CNNs with 1‐D OSTs. In the aforementioned designs, parameter values of 1‐D OSTs are chosen to make CNNs operate with thinning‐like property 1 (TL‐1), and those of 2‐D OSTs with 2‐D thinning‐like property (2‐DTL). Simulation studies show that these CNN systems have a good image thinning performance. Copyright © 1999 John Wiley & Sons, Ltd.     

A study on WTA cellular neural networks

This paper addresses a number of basic issues concerning the dynamics of a class of winner‐take‐all cellular neural networks (WTA CNNs) proposed by Seiler and Nossek. The main result is an analytical estimate for the settling time, which shows from a theoretical point of view that such CNNs are well suited for on‐line applications requiring a large number of units, fast processing speed and a relatively high resolution. Other results are the determination of the largest parameter set that guarantee a correct WTA functionality for all initial conditions and the solution of a conjecture made by Seiler and Nossek. These results are proved by means of a new Lyapunov function to analyse the global dynamical behaviour of the WTA CNNs. Copyright © 2001 John Wiley & Sons, Ltd.     

Cellular neural networks: Theory and circuit design

Cellular neural networks or CNNs are a novel neural network architecture introduced by Chua and Yang which is very general and flexible, has some important properties desirable for design applications and can be efficiently implemented on custom hardware based on analogue VLSI technology. In this paper an abstract normalized definition of cellular neural networks with arbitrary interconnection topology is given. Instead of stability, the property of convergence is found to be of central importance: large classes of convergent CNNs in practice always asymptotically approach some stable equilibrium where each component of the corresponding output is binary-valued. A highly efficient CMOS-compatible CNN circuit architecture is then presented where a basic cell consists of only two fully differential op amps, two capacitors and several MOSFETs, while a variable interconnection weight is realized with only four MOSFETs. Since all these elements are standard components in the current analogue IC technology and since all network functions are implemented directly on the device level, this architecture promises high cell and interconnection densities and extremely high operating speeds.     

A rigorous design method for binary cellular neural networks

In order to be able to take full advantage of the great application potential that lies in cellular neural networks (CNNs) we need to have successful design and learning techniques as well. In almost any analogic CNN algorithm that performs an image processing task, binary CNNs play an important role. We observed that all binary CNNs reported in the literature, except for a connected component detector, exhibit monotonic dynamics. In the paper we show that the local stability of a monotonic binary CNN represents sufficient condition for its functionality, i.e. convergence of all initial states to the prescribed global stable equilibria. Based on this finding, we propose a rigorous design method, which results in a set of design constraints in the form of linear inequalities. These are obtained from simple local rules similar to that in elementary cellular automata without having to worry about continuous dynamics of a CNN. In the end we utilize our method to design a new CNN template for detecting holes in a 2D object. © 1998 John Wiley Sons, Ltd.     

Design of high-speed,high-density CNNS in cmos technology

An analogue VLSI circuit architecture for the CMOS implementation of cellular neural networks (CNNs) is presented. It is based exclusively on the use of small capacitors and operational transconductance amplifiers operating in continuous time. Integrated circuit implementations of this architecture are very well suited for processing applications requiring large array size and high speed. We describe a systematic design approach for those circuits and present the design, fabrication and testing of two chips. These chips are used for connected component detection applications and are the first working integrated circuit implementation of a CNN. They contain 2000 transistors and have been fabricated using 2 μm CMOS technology. the density is 32 cells per square millimetre of silicon and the time constant of the processing is of the order of 10^?7 s. Experimental results of static and dynamic tests are given, including a complete image-processing example.     

Robot vision with cellular neural networks: a practical implementation of new algorithms

Cellular neural networks (CNNs) are well suited for image processing due to the possibility of a parallel computation. In this paper, we present two algorithms for tracking and obstacle avoidance using CNNs. Furthermore, we show the implementation of an autonomous robot guided using only real‐time visual feedback; the image processing is performed entirely by a CNN system embedded in a digital signal processor (DSP). We successfully tested the two algorithms on this robot. Copyright © 2006 John Wiley & Sons, Ltd.     

Membrain: A cellular neural network model based on a vibrating membrane

This paper introduces the Membrain model describing a neural network architecture which is similar to the architecture underlying the class of cellular neural networks (CNNs). the main difference pertains to the characteristic processing equation, which is based on a wave equation instead of a heat equation. Within the CNN framework, a cellular Membrain model may be obtained by replacing the neuron output function by a first-order state equation. Furthermore, the network-cloning templates are chosen such that the CNN behaves like a system of coupled harmonical oscillators. Since the energy of such a system is bounded, the piecewise linear neuron characteristic function may be chosen such that it always operates in the linear regime. Our starting point is the analytical and general solution for forced vibrations with damping. This solution applies to a Membrain neural network whose functional architecture is based on the specialized solution for a network of coupled harmonic oscillators. In particular, we present a Membrain CNN (MCNN) having a toroidal connection structure such that the natural modes of vibration of the net are translation-invariant. Moreover, depending on the point group of the network, some rotation invariance can also be obtained. Identifying the input of such a network with the initial state of the oscillators gives rise to an output which is in essence a transversally travelling wave made up of components which are coupled harmonic neuronal oscillators; that is, the wave is a superposition of natural modes of vibration of the network. the temporal wave pattern may be transformed into a one-dimensional temporal signal which is invariant under translation of the initial deflection pattern of the MCNN. the amplitudes of the components in the temporal signal correspond to the power spectrum of the natural vibration modes in the MCNN. Interpreting the initial deflection pattern as a grey-level image, the temporal signal can be viewed as a modulation of a translation-invariant ‘fingerprint’ of the image. the signal may be sampled such that the modulated ‘fingerprint’ can be classified using some of the traditional neural network models. In particular we show that (1) a self-organizing feature map clusters correlated images and (2) a back-propagation neural network extracts position-invariant features.     

Cellular neural network design for solving specific image-processing problems

This paper is concerned with determining simple CNN-paradigm-based circuits for solving specific binary image-processing problems. Single-layer CNN templates for performing specified shape extraction, extraction of shapes which contain a desired feature and modified feature detection are derived using a formulated design strategy. the concept of CNNs composed of modified cells is developed and circuits designed for realizing simultaneous detection of distinct features and for extracting horizontal line midpoints are presented.     

Stability analysis of generalized cellular neural networks

A rather general class of neural networks, called generalized cellular neural networks (CNNs), is introduced. the new model covers most of the known neural network architectures, including cellular neural networks, Hopfield networks and multilayer perceptrons. Several sets of conditions ensuring the input-output stability and global asymptotic stability of generalized CNNs have been obtained. the conditions for the stability of individual cells are checked in the frequency domain, while the stability of the overall network is analysed in terms of the stability of individual cells and the connectivity characteristics. the results on the global asymptotic stability are useful for the design of a generalized CNN such that the orbit of each state converges to a globally asymptotically stable equilibrium point which depends only on the input and not on the initial state. Such a network defines an algebraic map from the space of external inputs to the space of steady state values of the outputs and hence can accomplish cognitive and computational tasks.     

Cellular neural networks based on resonant tunnelling diodes

Resonant tunnelling diodes (RTDs) have intriguing properties which make them a primary nanoelectronic device for both analogue and digital applications. We propose two different types of RTD‐based cells for the cellular neural network (CNN) which exhibit superior performance in terms of complexity, functionality, or processing speed compared to standard cells. In the first cell model, the resistor of the standard cell is replaced by an RTD, which results in a more compact and versatile cell which requires neither self‐feedback nor a non‐linear output function, and allows three stable equilibrium points. If a resonant tunnelling transistor (RTT) is used instead of the RTD, the dynamics can be controlled through its gate voltage as an additional network parameter. In a majority of CNN applications, bistable cells are sufficient. Utilizing RTD‐based bistable logic elements to store the state of the cell, switching occurs almost instantaneously as virtually no charge transfer is necessary, and it is possible to implement non‐linear connections in a straightforward manner. Hence, it turns out that RTD‐based CNNs are tailor‐made for the implementation of extremely fast bipolar operations and non‐linear templates. The ideas presented in this paper may also be beneficially applied to other types of circuits and systems such as A/D converters or sigma‐delta modulators. Copyright © 2001 John Wiley & Sons, Ltd.     

Long transient oscillations in a class of cooperative cellular neural networks

The real‐time processing capabilities of cellular neural networks (CNNs) are inherently related to the fast convergence time of the solutions toward the asymptotically stable equilibrium points. A typical requirement is that the settling time should not exceed a few (or at most 10) cell time constants. This paper introduces a class of completely stable nonsymmetric cooperative CNN rings whose solutions display unexpectedly long transient oscillations for a wide set of initial conditions and for a wide set of interconnection parameters. Numerical simulations show that the oscillations can easily last hundreds of cycles, and thousands of cell time constants, before settling to a steady state, thus possibly impairing their real‐time processing capabilities. Goal of the paper is also to show, by means of laboratory experiments on a discrete component prototype of the CNN ring, that the long oscillation phenomenon is physically robust with respect to the non‐idealities of the circuit implementation. The experiments show some other peculiar features of the long lasting oscillations as the metamorphosis between different periodic behaviors during the transient. Finally, analytic asymptotic estimates on the duration of the transient oscillations are provided. Copyright © 2013 John Wiley & Sons, Ltd.     

科学技术奖励信息系统

本系统由项目管理、专家管理、评审系统等22个子系统组成,采用C／S和B／S相结合的多层开放式体系架构,具有友好的用户界面,易于维护和升级。本系统的开发不但提高了科技奖励管理工作的效率,而且提高了工作质量,使科技奖励管理水平上升了一个台阶。     

Complete stability of feedback CNNs with dynamic memristors and second‐order cells

The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that is CNNs using a dynamic (D) memristor (M). After giving a foundation to the DM‐CNN model, the paper establishes a fundamental result on complete stability, that is convergence of solutions toward equilibrium points, when the DM‐CNN has symmetric interconnections. Because of the presence of dynamic memristors, a DM‐CNN displays peculiar and basically different dynamic properties with respect to standard CNNs. First of all a DM‐CNN computes during the time evolution of the memristor fluxes, instead of the capacitor voltages as for a standard CNN. Furthermore, when a steady state is reached, the memristors keep in memory the result of the computation, that is the limiting values of the fluxes, while all memristor currents and voltages, as well as all currents, voltages, and power in the DM‐CNN vanish. Instead, for standard CNNs, currents, voltages, and power do not drop off when a steady state is reached. Copyright © 2016 John Wiley & Sons, Ltd.     

CIRCULANT MATRICES AND THE STABILITY OF A CLASS OF CNNs

In this paper we show that feedback matrices of ring CNNs are block circulants; as special cases, for example, feedback matrices of one-dimensional ring CNNs are circulant matrices. Circulants and their close relations the block circulants possess many pleasant properties which allow one to describe their spectrum completely. After deriving the spectrum of the feedback operator, we discuss conditions for a CNN to be contractive, ensuring global asymptotic stability.     

Templating and Replication of Spiral Photonic Crystals for Silicon Photonics

This paper describes femtosecond laser lithography of 3-D photonic crystal templates in commercial photoresist SU-8 and replication of these templates with silicon. Using this approach, silicon-based photonic crystals having 3-D square spiral architecture and exhibiting photonic stop gaps near the 2.5- mum wavelength were fabricated. Possibilities to use a multiple-beam interference technique for two-photon absorption templating of photonic crystals are explored.     

信息系统在线状态检修部署架构研究

为实现信息系统在线状态检修,减少系统检修时长,增强系统对外业务应用连续性,安徽省电力公司与中间件和负载均衡器技术支持厂商合作,以现有核心信息系统为研究对象,通过数个月的研究和试验,最终完成信息系统在线状态检修部署架构的可行性论证,并成功摸索出一套行之有效的信息系统在线状态检修部署架构适用性通用测试方法。目前该架构在安徽省电力公司应用效果良好,已完成对多套信息系统的改造,增强了系统检修的灵活度,提升了系统运行管理水平。     

基于消息中间件的用电信息采集系统远程停复电可靠性方案*

通过用电信息采集系统实现快速、精准的远程停复电对于提升电网企业精益化管理水平、提高客户服务满意度至关重要。文中详细分析了现有用电信息采集系统远程停复电业务处理流程,揭示了系统在高负载、多业务协同处理时出现的处理延迟、调度缓慢、主站与电能表停复电服务程序操作及数据不一致等影响停复电业务执行成功率的不利因素;针对上述不可靠因素,提出了一种基于消息中间件技术的远程停复电事务处理架构,该架构的对现有用采系统改动较少;并且基于该架构,文中设计了一种满足远程停复电可靠性需求的分布式事务处理方案;通过仿真分析可知,该方案可以有效提高用电信息采集系统执行远程停复电业务的可靠性。     

Flexible open architecture design for power system control centers

That the new generation power system control centers need a paradigm shift in their architectural design to meet new challenges, is an established conclusion of many researchers. However, literature on comprehensive architectural design towards this direction is scarce. In this paper, an information architecture design is proposed for the power system control centers. Appropriate adoption of open standards such as the common information model (CIM) is endorsed. Further, initial development of an event information model for modeling high level power system events is mentioned. Proposed architecture focuses on the desired features such as openness, interoperability, flexibility, scalability, vendor independence and event orientation. The architecture comprises services arranged in layers, viz., data integration and access, event processing and plug-in services. The details of this architecture along with the intricate relationships between various layers and services depicting how the technologies could be integrated into a coherent whole are described in this paper. An illustrative example, from the Indian power sector domain has been used to demonstrate the usefulness of the proposed architecture.