Abstract: The system can be used for the automatic analysis of images, including a matrix of spots, such as images of DNA microarrays after hybridization. The system can be associated—and preferably integrated in a single monolithic component implementing VLSI CMOS technology—to a sensor for acquiring the images. The system includes a circuit for processing the signals corresponding to the images, configured according to a cellular neural network (CNN) architecture for the parallel analogue processing of signals.

Abstract: An integrated cellular network structure that is programmable to solve partial derivative differential equations in order to control a phenomenon of diffusion or a propagation of electric drive pulses for robot actuators. Such structure includes analog and digital portions interconnected with each other; the analog portion having a matrix array of analog cells arranged to receive data from an I/O interface, and the digital portion having first and second memory arrays for storing a desired configuration and the initial state of such analog matrix array, respectively.

Abstract: A method of controlling the movements of a multi-actuator electro-mechanical system having a matrix of locally interconnected analog cells associated therewith is provided. Each cell represents a hardware implementation of a model of fuzzy inference rules. The model includes a fuzzy circuit architecture which may be implemented in an integrated circuit with VLSI CMOS technology that generates and controls a reaction diffusion mechanism typical of auto-waves using a fuzzy neural network. The fuzzy neural network defines the functional relationships that may duplicate simultaneous reaction diffusion equations. The duplication of the simultaneous reaction diffusion equations is provided using two sets of fuzzy rules processing, in a linguistic manner, the state variables of the cells. An oscillatory type dynamic is imposed on each cell where two dynamic processes having different kinetic characteristics coexist.

Abstract: A method of controlling the movements of a multi-actuator electromechanical system having a matrix of locally interconnected analog cells associated therewith is provided. Each cell represents a hardware implementation of a model of fuzzy inference rules. The model includes a fuzzy circuit architecture which may be implemented in an integrated circuit with VLSI CMOS technology that generates and controls a reaction diffusion mechanism typical of auto-waves using a fuzzy neural network. The fuzzy neural network defines the functional relationships that may duplicate simultaneous reaction diffusion equations. The duplication of the simultaneous reaction diffusion equations is provided using two sets of fuzzy rules processing, in a linguistic manner, the state variables of the cells. An oscillatory type dynamic is imposed on each cell where two dynamic processes having different kinetic characteristics coexist.

Abstract: The system can be used for the automatic analysis of images (I), comprising a matrix of spots, such as images of DNA microarrays after hybridisation. The system can be associated—and preferably integrated in a single monolithic component implementing VLSI CMOS technology—to a sensor (10) for acquiring said images (I). The system comprises a circuit (20) for processing the signals corresponding to the images (I), configured according to a cellular neural network (CNN) architecture for the parallel analogue processing of signals.

Abstract: An integrated cellular network structure that is programmable to solve partial derivative differential equations in order to control a phenomenon of diffusion or a propagation of electric drive pulses for robot actuators. Such structure includes analog and digital portions interconnected with each other; the analog portion having a matrix array of analog cells arranged to receive data from an I/O interface, and the digital portion having first and second memory arrays for storing a desired configuration and the initial state of such analog matrix array, respectively.