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This work presents a new architecture of analog four-quadrant multiplier in CMOS technology based on the behavior of MOSFET in the linear region from weak to strong inversion. The proposed multiplier has voltage and current inputs and a current output, thus being adequate for the implementation of compact synapses in analog Cellular Neural Network (CNN). Simulation results exhibit low power consumption and low distortion. [ABSTRACT FROM PUBLISHER]

Image processing plays an important role in medical diagnosis. In this paper, a brain tumor detection method based on cellular neural networks (CNNs) is proposed. Brain tumor is an abnormal growth of cells inside the skull. To examine the location of tumor in the brain, Magnetic Resonance Imaging (MRI) is used. Radiologists will evaluate the grey scale MRI images. This procedure is really time and energy consuming. To overcome this problem, an automated detection method for brain tumor using CNN is developed. By using the template in the CNN simulator, output of the desired image can be performed. Therefore, many templates were combined in order to obtain an accurate result that will help radiologists detecting the tumor in brain images easily. [ABSTRACT FROM PUBLISHER]

Cellular neural network (CNN) is a large-scale nonlinear analog circuit suitable for real-time signal and image processing. In this paper, the CNN is applied in order to detect edges of gray-scale video traffic images. Edges are an important character to feature extraction of the images. Based on Matlab 7.0, the experimental results, compared with the traditional edge detection algorithm Canny, show that CNN can not only be available but quickly detect the complete image edges. This paper also proposes an effective way to eliminate the false edges and deduces the range of CNN template parameters. The results presented show that this approach is valid. [ABSTRACT FROM AUTHOR]