Cellular neural networks for real-time DNA microarray analysis

A Hybrid Bionic Image Sensor Achieving FOV Extension and Foveated Imaging

Based on bionic compound eye and human foveated imaging mechanisms, a hybrid bionic image sensor (HBIS) is proposed in this paper to extend the field of view (FOV) with high resolution. First, the hybrid bionic imaging model was developed and the structure parameters of the HBIS were deduced. Second, the properties of the HBIS were simulated, including FOV extension, super-resolution imaging, foveal ratio and so on. Third, a prototype of the HBIS was developed to validate the theory. Imaging experiments were carried out, and the results are in accordance with the simulations, proving the potential of the HBIS for large FOV and high-resolution imaging with low cost.

Receding Horizon Stabilization and Disturbance Attenuation for Neural Networks With Time-Varying Delay

This paper is concerned with the problems of receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay. New delay-dependent conditions on the terminal weighting matrices of a new finite horizon cost functional for receding horizon stabilization are established for neural networks with time-varying or time-invariant delays using single- and double-integral Wirtinger-type inequalities. Based on the results, delay-dependent sufficient conditions for the receding horizon disturbance attenuation are given to guarantee the infinite horizon H∞ performance of neural networks with time-varying or time-invariant delays. Three numerical examples are provided to illustrate the effectiveness of the proposed approach.

Theoretic design of differential minimax controllers for stochastic cellular neural networks

This paper presents a theoretical design of how a minimax equilibrium of differential game is achieved in stochastic cellular neural networks. In order to realize the equilibrium, two opposing players are selected for the model of stochastic cellular neural networks. One is the vector of external inputs and the other is the vector of internal noises. The design procedure follows the nonlinear H infinity optimal control methodology to accomplish the best rational stabilization in probability for stochastic cellular neural networks, and to attenuate noises to a predefined level with stability margins. Three numerical examples are given to demonstrate the effectiveness of the proposed approach.

Bio-Microfluidics Real-Time Monitoring Using CNN Technology

A new non-invasive real-time system for the monitoring and control of microfluidodynamic phenomena involving transport of particles and two phase fluids is proposed. The general purpose design of such system is suitable for in vitro and in vivo experimental setup and, therefore, for microfluidic applications in the biomedical field, such as lab-on-chip and for research studies in the field of microcirculation. The system consists of an ad hoc optical setup for image magnification providing images suitable for acquisition and processing. The main feature of the optical system is the accessibility of the information at any point of the optical path. It was designed and developed using discrete opto-mechanic components mounted on a breadboard. The optical sensing, acquisition, and processing were all performed using an integrated vision system based on cellular nonlinear networks (CNNs) analogic (analog plus logic) technology called focal plane processor (FPP, Eye-RIS, Anafocus) that was inserted in the optical path. Ad hoc algorithms were implemented for the real-time analysis and extraction of fluidodynamic parameters in micro-channels. They were firstly tested on sequences of images recorded during in vivo microcirculation experiments on hamsters and then applied on images acquired and processed in real-time during in vitro experiments on two-phase fluid flow in a continuous microfluidic device (serpentine mixer, ThinXXS).

Real-time processing and compression of DNA microarray images

In this paper, we present a pipeline architecture specifically designed to process and compress DNA microarray images. Many of the pixilated image generation methods produce one row of the image at a time. This property is fully exploited by the proposed pipeline that takes in one row of the produced image at each clock pulse and performs the necessary image processing steps on it. This will remove the present need for sluggish software routines that are considered a major bottleneck in the microarray technology. Moreover, two different structures are proposed for compressing DNA microarray images. The proposed architecture is proved to be highly modular, scalable, and suited for a standard cell VLSI implementation.

A cellular nonlinear network: real-time technology for the analysis of microfluidic phenomena in blood vessels

A new approach to the observation and analysis of dynamic structural and functional parameters in the microcirculation is described. The new non-invasive optical system is based on cellular nonlinear networks (CNNs), highly integrated analogue processor arrays whose processing elements, the cells, interact directly within a finite local neighbourhood. CNNs, thanks to their parallel processing feature and spatially distributed structure, are widely used to solve high-speed image processing and recognition problems and in the description and modelling of biological dynamics through the solution of time continuous partial differential equations (PDEs). They are therefore considered extremely suitable for spatial-temporal dynamic characterization of fluidic phenomena at micrometric to nanometric scales, such as blood flow in microvessels and its interaction with the cells of the vessel wall. A CNN universal machine (CNN-UM) structure was used to implement, via simulation and hardware (ACE16k), the algorithms to determine the functional capillarity density (FCD) and red blood cell velocity (RBCV) in capillaries obtained by intravital microscopy during in vivo experiments on hamsters. The system exploits the moving particles to distinguish the functional capillaries from the stationary background. This information is used to reconstruct a map and to calculate the velocity of the moving objects.

A Multichannel Order-Statistic Technique for cDNA Microarray Image Processing

This paper introduces an automated image processing procedure capable of processing complementary deoxyribonucleic acid (cDNA) microarray images. Microarray data is contaminated by noise and suffers from broken edges and visual artifacts. Without the utilization of a filter, subsequent tasks such as spot identification and gene expression determination cannot be completed. By employing, in a unique cascade processing cycle, nonlinear filtering solutions based on robust order statistics, the procedure: 1) removes both background and high-frequency corrupting noise and 2) correctly identifies edges and spots in cDNA microarray data. The proposed solution operates directly on the microarray data, does not rely on explicit data normalization or spot separation preprocessing, and operates in a robust manner without using heuristically determined design parameters. Other routine microarray processing operations such as shape manipulations and grid adjustments can be used in conjunction with the developed solution in the processing pipeline. Experimentation reported in this paper indicates that the proposed solution yields excellent performance by removing noise and enhancing spot location determination.