Cellular neural networks for real-time DNA microarray analysis

A new methodology for real-time processing of DNA chip images is proposed. The idea developed here is to use the cellular neural network (CNN) array to analyze the DNA microarray. A CNN is an analog dynamic processor array that reflects this property: the processing elements interact directly within a finite local neighborhood. Due to its architecture, a two-dimensional CNN array is widely used to solve image processing and pattern recognition problems; moreover, the parallelism characteristic of this structure allows one to perform the most computationally expensive image analysis tasks three orders of magnitude faster than a classical CPU-based computer. This approach, thanks to the supercomputing capabilities of the CNN architecture, makes the whole DNA chip methodology fully parallel and also makes the processing phase, until now very time consuming, a real-time step. We discuss the results of testing an algorithm based on the CNN universal machine (CNN-UM) that has been designed to classify the image data. The algorithm is implemented in an analogic (analog and logic) microprocessor.     

Watermarking on CNN‐UM for image and video authentication

In this paper a new approach to fragile watermarking technique is introduced. This problem is particularly interesting in the field of modern multimedia applications, when image and video authentication are required. The approach exploits the cellular automata suitability to work as pseudorandom pattern generators and extends the related algorithms under the framework of the cellular non‐linear networks (CNNs). The result is a novel way to perform watermarking generation in real time, using the presently available CNN‐universal chip prototypes. In this paper, both the CNN algorithms for fragile watermarking as well as on‐chip experimental results are reported, confirming the suitability of CNNs to successfully act as real‐time watermarking generators. The availability of CNN‐based visual microprocessors allows to have powerful algorithms to watermark in real time images or videos for efficient smart camera applications. Copyright © 2004 John Wiley & Sons, Ltd.     

Locomotion Control of MEMS Microrobot Using Pulse‐Type Hardware Neural Networks

This paper presents the locomotion control of a microelectromechanical system (MEMS) microrobot. The MEMS microrobot demonstrates locomotion control by pulse‐type hardware neural networks (P‐HNN). P‐HNN generate oscillatory patterns of electrical activity like those of living organisms. The basic component of P‐HNN is a pulse‐type hardware neuron model (P‐HNM). The P‐HNM has the same basic features as biological neurons, such as the threshold, the refractory period, and spatiotemporal summation characteristics, and allows the generation of continuous action potentials. P‐HNN has been constructed with MOSFETs and can be integrated by CMOS technology. Like living organisms, P‐HNN has realized robot control without using software programs or A/D converters. The size of the microrobot fabricated by MEMS technology was 4 × 4 × 3.5 mm. The frame of the robot was made of a silicon wafer, equipped with rotary actuators, link mechanisms, and six legs. The MEMS microrobot emulated the locomotion method and the neural networks of an insect by rotary actuators, link mechanisms, and the P‐HNN. We show that the P‐HNN can control the forward and backward locomotion of the fabricated MEMS microrobot, and that it is possible to switch its direction by inputting an external trigger pulse. The locomotion speed was 19.5 mm/min and the step size was 1.3 mm. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 43–50, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22473     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

Preliminary results from an analog implementation of first‐order TDCNN dynamics

This work falls into the category of linear cellular neural network (CNN) implementations. We detail the first investigative attempt on the CMOS analog VLSI implementation of a recently proposed network formalism, which introduces time‐derivative ‘diffusion’ between CNN cells for nonseparable spatiotemporal filtering applications—the temporal‐derivative CNNs (TDCNNs). The reported circuit consists of an array of Gm‐C filters arranged in a regular pattern across space. We show that the state–space coupling between the Gm‐C‐based array elements realizes stable and linear first‐order (temporal) TDCNN dynamics. The implementation is based on linearized operational transconductance amplifiers and Class‐AB current mirrors. Measured results from the investigative prototype chip that confirms the stability and linearity of the realized TDCNN are provided. The prototype chip has been built in the AMS 0.35 µm CMOS technology and occupies a total area of 12.6 mm sq, while consuming 1.2 µW per processing cell. Copyright © 2010 John Wiley & Sons, Ltd.     

Pixel‐level snakes on the CNNUM: algorithm design,on‐chip implementation and applications

In this paper, a new algorithm for the cellular active contour technique called pixel‐level snakes is proposed. The motivation is twofold: on the one hand, a higher efficiency and flexibility in the contour evolution towards the boundaries of interest are pursued. On the other hand, a higher performance and suitability for its hardware implementation onto a cellular neural network (CNN) chip‐set architecture are also required. Based on the analysis of previous schemes the contour evolution is improved and a new approach to manage the topological transformations is incorporated. Furthermore, new capabilities in the contour guiding are introduced by the incorporation of inflating/deflating terms based on the balloon forces for the parametric active contours. The entire algorithm has been implemented on a CNN universal machine (CNNUM) chip set architecture for which the results of the time performance measurements are also given. To illustrate the validity and efficiency of the new scheme several examples are discussed including real applications from medical imaging. Copyright © 2005 John Wiley & Sons, Ltd.     

The network behind spatio‐temporal patterns: building low‐complexity retinal models in CNN based on morphology,pharmacology and physiology

In this paper, a vertebrate retina model is described based on a cellular neural network (CNN) architecture. Though largely built on the experience of previous studies, the CNN computational framework is considerably simplified: first‐order RC cells are used with space‐invariant nearest‐neighbour interactions only. All non‐linear synaptic connections are monotonic continuous functions of the pre‐synaptic voltage. Time delays in the interactions are continuous represented by additional first‐order cells. The modelling approach is neuromorphic in its spirit relying on both morphological and pharmacological information. However, the primary motivation lies in fitting the spatio‐temporal output of the model to the data recorded from biological cells (tiger salamander). In order to meet a low‐complexity (VLSI) implementation framework some structural simplifications have been made. Large‐neighbourhood interaction (neurons with large processes), furthermore inter‐layer signal propagation are modelled through diffusion and wave phenomena. This work presents novel CNN models for the outer and some partial models for the inner (light adapted) retina. It describes an approach that focuses on efficient parameter tuning and also makes it possible to discuss adaptation, sensitivity and robustness issues on retinal ‘image processing’ from an engineering point of view. Copyright © 2001 John Wiley & Sons, Ltd.     

A new approach for ME image restoration based on CNN

A new approach for image restoration by cellular neural network (CNN) is developed in this paper. Based on the statistical characteristics of Gibbs image model and the analysis of maximum entropy (ME) image restoration, a reasonable template for binary image restoration is proposed. To process multilevel image, a multi‐layer cellular neural network is employed and an extensive algorithm for multilevel image restoration is proposed. The results of computer simulation prove the effectiveness of this approach and show that we can get the effective template of CNN for some special image questions if we apply the statistical characteristics of Gibbs image model and analyse the physical meaning of the questions. Copyright © 1999 John Wiley & Sons, Ltd.     

CNN‐based architecture for real‐time object‐oriented video coding applications

This paper presents a new CNN‐based architecture for real‐time video coding applications. The proposed approach, by exploiting object‐oriented CNN algorithms and MPEG encoding capabilities, enables low bit‐rate encoder/decoder to be designed. Simulation results using Claire video sequence show the effectiveness of the proposed scheme. Copyright © 2005 John Wiley & Sons, Ltd.     

Compact CMOS implementation of a low‐power,current‐mode programmable cellular neural network

We report on the design and characterization of a full‐analog programmable current‐mode cellular neural network (CNN) in CMOS technology. In the proposed CNN, a novel cell‐core topology, which allows for an easy programming of both feedback and control templates over a wide range of values, including all those required for many signal processing tasks, is employed. The CMOS implementation of this network features both low‐power consumption and small‐area occupation, making it suitable for the realization of large cell‐grid sizes. Device level and Monte Carlo simulations of the network proved that the proposed CNN can be successfully adopted for several applications in both grey‐scale and binary image processing tasks. Results from the characterization of a preliminary CNN test‐chip (8×1 array), intended as a simple demonstrator of the proposed circuit technique, are also reported and discussed. Copyright © 2001 John Wiley & Sons, Ltd.     

Current‐input current‐output analog half center oscillator and central pattern generator circuits with memristors

The comparison of the memristor to the biological synapse has motivated the introduction of memristors to biomimetic circuits such as Central Pattern Generators (CPGs) and Half Center Oscillators (HCOs). The effects of the utilization of memristors in such systems have been investigated in this work. The HCO is a neural oscillator, and the CPG is made up of 4 HCOs producing oscillations corresponding to the locomotion of a 4‐limbed animal. Analog HCO and CPG circuits have been simulated using the Cadence Virtuoso platform and effects of using current‐driven and voltage‐driven memristors in different configurations with different parameters have been analyzed. Improvement in the stability of rhythm and variations in oscillation amplitudes have been observed.     

Guiding a mobile robot with cellular neural networks

We show how cellular neural networks (CNNs) are capable of providing the necessary signal processing needed for visual navigation of an autonomous mobile robot. In this way, even complex feature detection and object recognition can be obtained in real time by analogue hardware, making fully autonomous real‐time operation feasible. An autonomous robot was first simulated and then implemented by simulating the CNN with a DSP. The robot is capable of navigating in a maze following lines painted on the floor. Images are processed entirely by a CNN‐based algorithm, and navigation is controlled by a fuzzy‐rule‐based algorithm. Copyright © 2002 John Wiley & Sons, Ltd.     

The analogic cellular neural network as a bionic eye

The conception of the CNN universal machine has led quite naturally to the invention of the analogic CNN bionic eye (henceforth referred to simply as the bionic eye). the basic idea is to combine the elementary functions, the building blocks, of the retina and other 2 1/2 D sensory organs, algorithmically, in a stored programme of a CNN universal machine, through the use of artificial analogic programmes. the term bionic is defined in a rigorous way: it is a nonlinear, dynamic, spatiotemporal biological model implemented in a stored programme electronic (optoelectronic) device; this device is in our case the analogic CNN universal machine (or chip). The aim of this paper is to report on this new invention, particularly to electronic and computer engineers, in a tutorial way. We begin by summarizing (1) the biological aspects of the range of retinal function (the retinal universe), (2) the CNN paradigm and the CNN universal machine architecture and (3) the general principles of retinal modelling in CNN. Next we describe new CNN circuit and template design innovations that can be used to implement physiological functions in the retina and other sensory organs using the CNN universal machine. Finally we show how to combine given retinal functional elements implemented in the CNN universal machine with analogic algorithms to form the bionic retina. the resulting system can be used not only for simulating biological retinal function but also for generating functions that go far beyond biological capabilities. Several bionic retina functions, different topographic modalities and analogic CNN algorithms can then be combined to form the analogic CNN bionic eye. the qualitative aspects of the models, especially the range of dynamics and accuracy considerations in VLSI optoelectronic implementations, are outlined. Finally, application areas of the bionic eye and possibilities of constructing innovative devices based on this invention (such as the bionic eyeglass or the visual mouse) are described.     

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.     

Estimating optical flow with cellular neural networks

The cellular neural network is a locally interconnected neural network capable of high-speed computation when implemented in analog VLSI. This work describes a CNN algorithm for estimating the optical flow from an image sequence. The algorithm is based on the spatio-temporal filtering approach to image motion analysis and is shown to estimate the optical flow more accurately than a comparable approach proposed previously. Two innovative features of the algorithm are the exploitation of a biological model for hyperacuity and the development of a new class of spatio-temporal filter better suited for image motion analysis than the commonly used space–time Gabor filter. © 1998 John Wiley & Sons, Ltd.     

A digital multiprocessor hardware accelerator board for cellular neural networks: CNN-HAC

Analogue realizations of neural networks are superior in speed. the hardware accelerator boards using catalogue programmable VLSI ICs represent a trade-off having higher reconfigurability and lower cost. This paper presents such a solution for a cellular neural network (CNN). The architecture of the present design (CNN-HAC) using four standard DSPs to calculate the transient response of a one-layer CNN containing (0.25–0.75) × 10⁶ analogue neural cells (depending on the type of template) is presented. the architecture and also the design principles are independent of the number of processors. the actual design was made in the form of a PC add-on board. The global control unit, which connects the board to the host firmware and communicates control signals to/from the local control units of the DSPs, was realized mainly with EPLDs. A special correspondence between the virtual processing elements—calculating the time-discrete models of the analogue neural cells—and the physical ones is discussed in detail. It is realized in an architecture with a simple, two-directional interprocessor communication. This architecture can be ‘scaled down’ using faster processors, EPLDs and memories. the present version runs with 2 μs/cell/iteration speed.     

Real‐time chaotic circuit stabilization via inverse optimal control

In this paper, an efficient approach is developed for real‐time global asymptotic stabilization of the chaotic Chen's circuit, as a typical example for chaotic circuit control. Based on a recently introduced methodology of inverse optimal control for nonlinear systems, a very simple stabilization control law, a linear state feedback, is electronically implemented for the desired global asymptotic stabilization. Both Chen's chaotic system and the designed controller are synthesized and realized by analog electronic components, with the aim of evaluating the physical performance of the real‐time control law and demonstrating the practicality of the control method, which is robust to some input uncertainties. Copyright © 2008 John Wiley & Sons, Ltd.     

Pattern classification of time‐series EMG signals using neural networks

This paper proposes a pattern classification method of time‐series EMG signals for prosthetic control. To achieve successful classification for non‐stationary EMG signals, a new neural network structure that combines a common back‐propagation neural network with recurrent neural filters is used. A convergence time of the network learning can be regulated by a new learning method based on dynamics of a terminal attractor. The experiments of pattern classification and prosthetic control are carried out for several subjects including an amputee. It is shown from the results that the proposed method improves learning/classification ability for stationary and non‐stationary EMG signals during a series of continuous motions. Copyright © 2000 John Wiley & Sons, Ltd.     

Programmable analogue vlsi cnn chip with local digital logic

A new integrated circuit cellular neural network implementation with digitally or continuously selectable template coefficients is presented. Local logic and memory are added into each cell, providing a simple dual (analogue and digital) computing structure. Variable gain OTAs are used as the voltage-controlled current sources to programme the template element values. the cells have local switched feedback (both analogue and digital) to feed from the output to the input or state capacitor. Therefore this analogue array processor can be applied to solve problems with a sequence of different templates. A 4 × 4 CNN circuit is realized using the 2 μm analogue CMOS process.     

Neural network based terminal iterative learning control for uncertain nonlinear non‐affine systems

In this paper, a novel neural network based terminal iterative learning control method is proposed for a class of uncertain nonlinear non‐affine systems to track run‐varying reference point with initial state variance. In this new control scheme, the non‐affine terminal dynamics are converted affine, and the unrealisable recurrent network is simplified into realisable static network. As a result, the effect of initial state and control signal on terminal output can be estimated by neural network. With this estimation, the proposed control scheme can drive nonlinear non‐affine systems to track run‐varying reference point in the presence of initial state variance. Stability and convergence of this approach are proven, and numerical simulation results are provided to verify its effectiveness.