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In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These algorithms can achieve high network utilization by performing CAC in real-time, which is imperative in supporting quality of service (QoS) communication over packet-switched networks. The proposed solutions are of basic significance in access technology where a subscriber population (connected to the Internet via an access module) needs to receive services. In this case, QoS can only be preserved by admitting those user configurations which will not overload the access module. The paper treats CAC as a set separation problem where the separation surface is approximated based on a training set. This casts CAC as an image processing task in which a complex admission pattern is to be recognized from a couple of initial points belonging to the training set. Since CNNs can implement any propagation models to explore complex patterns, CAC can then be carried out by a CNN. The major challenge is to find the proper template matrix which yields high network utilization. On the other hand, the proposed method is also capable of handling three-dimensional separation surfaces, as in a typical access scenario there are three traffic classes (e.g., two type of Internet access and one voice over asymmetric digital subscriber line. [ABSTRACT FROM AUTHOR]

In this paper, the paradigm of emergent computation is applied to locomotion control in legged robots: the locomotion gait is the result of self-organization of a network of locally coupled nonlinear oscillators. This means to adopt the biological paradigm of central pattern generator (CPG), implemented by using cellular neural networks (CNNs). The whole control strategy is hybrid in the sense that the gait generation is accomplished by a fully analog CNN, while a simple logic unit modulates the behavior of the CNN-based CPG,so that the strategy is suitable to eventually include sensory feedback. The design of a VLSI chip implementing the CNN-based CPG and some experimental results on the chip are presented. The chip is designed using a switched-capacitor technique, fundamental to obtain in a simple and direct way some key features of the hybrid control discussed. The experimental results confirm the suitability of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we show that any N × M-dimensional delayed cellular neural network described using cloning templates can have no more than 3N×M isolated equilibrium points and 2 N×M of these equilibrium points located in saturation regions are locally exponentially stable. In addition, we give the conditions for the equilibrium points to be locally exponentially stable when the equilibrium points locate the designated saturation region. These conditions improve and extend the existing stability results in the literature. The conditions are also very easy to be verified and can be checked by direct examination of the templates, regardless of the number of cells. Finally, the validity and performance of the results are illustrated by use of two numerical examples. [ABSTRACT FROM AUTHOR]

This paper proposes a new image-descreening technique based on texture classification using a cellular neural network (CNN) with template trained by genetic algorithm (GA), called GA-CNN. Instead of using the fixed filters for image descreening, we are equipped with a more pliable mechanism for classifications in screening patterns. Using CNN makes it possible to get an accurate texture classification result in a faster speed by its superiority of implementable hardware and the flexible choices of templates. The use of the GA here helps us to look for the most appropriate template for CNNs more adaptively and methodically. The evolved parameters in the template for CNNs can not only provide a quicker classification mechanism but also help us with a better texture classification for screening patterns. After the class of screening patterns in the querying images is determined by the trained GA-CNN-based texture classification system, the recommendatory filters are induced to solve the screening problems. The induction of the classification in screening patterns has simplified the choke of filters and made it valueless to determine a new structured filter. Eventually, our comprehensive methodology is going to be topped off with more desirable results and the indication for the decrease in time complexity. [ABSTRACT FROM AUTHOR]

This paper introduces the processing core of a full-custom mixed-signal CMOS chip intended for an active-contour-based technique, the so-called pixel-level snakes (PLS). Among the different parameters to optimize on the top-down design flow our methodology is focused on area. This approach results in a single-instruction-multiple-data chip implemented by a discrete-time cellular neural network with a correspondence between pixel and processing element. This is the first prototype for PLS; an integrated circuit with a 9 × 9 resolution manufactured in a 0.25-μm CMOS ST Microelectronics technology process. Awaiting for experimental results, HSPICE simulations prove the validity of the approach introduced here. [ABSTRACT FROM AUTHOR]

Reaction-diffusion dynamics have been proposed to explain pattern-formation behavior: in a variety of systems, e.g., chemical and biological. This paper describes a cellular neural network chip that exhibits spatially organized patterns of activity, which are formed by reaction diffusion. The chip contains four 32 × 32 cell arrays of transistors which are locally coupled and operate in weak inversion. Typical patterns consist of alternating regions of high- and low-drain currents with a preferred width, but no preferred orientation. Experimental, theoretical, and simulation results for this chip are in complete concordance, demonstrating that conventional very large-scale integration technology can be an ideal substrate for studying the spatio-temporal dynamics and applications of reaction-diffusion. The chip, which was fabricated in a 0.5-pm process, settles to steady-state patterns within several hundred microseconds and dissipates 10.55 mW. [ABSTRACT FROM AUTHOR]

A very-high-performance Viterbi decoder with a circularly connected two-dimensional analog cellular neural network (CNN) cell array is disclosed. In the proposed Viterbi decoder, the CNN cells with nonlinear unilateral connections are implemented with electronic circuits at nodes on a trellis diagram. The circuits are circularly connected, forming a cylindrical shape so that the cells of the last stage are connected to those of the first stage. Unilateral connections guide the information to flow circularly around the cylindrical surface. Such configuration enables the conceptually infinite length of the trellis diagram to be reduced to a circuit of limited size. The analog circuits does not require any analog-digital converters, which is the major cause of high power consumption and the quantization error. With the parallel analog processing structure, its decoding speed becomes very high. Also, the decoding mechanism using triggering wave of the CNN circuit does not require the path memory. Circuits for the proposed structure have been designed with HSPICE. Features of the proposed Viterbi decoder are compared with those of the conventional digital Viterbi decoder. [ABSTRACT FROM AUTHOR]