In this paper the correlation spectrum of antenna array is introduced. Based on the relationship between the correlation spectrum and space spectrum of MUSIC, we proposed a novel approach to improve the DOA estimation by arranging the linear antenna array elements using genetic algorithm (GA) in optimizing the correlation spectrum. The DOA estimation performance of the optimized array is validated by Monte Carlo simulation and Cramer-Rao bound (CRB), which are improved compared with that of the traditional uniform linear array and the Minimum-Redundancy array (MRA).

Pulse coupled neural network (PCNN) is a new type of artificial neural network specific for image processing applications. It is a single layer, two dimensional network with neurons which have 1:1 correspondence to the pixels of an input image. It is convenient to process the intensities and spatial locations of image pixels simultaneously by applying a PCNN. Therefore, we propose a modified PCNN with anisotropic synaptic weight matrix for image edge detection from the aspect of intensity similarities of pixels to their neighborhoods. By applying the anisotropic synaptic weight matrix, the interconnections are only established between the central neuron and the neighboring neurons corresponding to pixels with similar intensity values in a 3 by 3 neighborhood. Neurons corresponding to edge pixels and non-edge pixels will receive different input signal from the neighboring neurons. By setting appropriate threshold conditions, image step edges can be detected effectively. Comparing with conventional PCNN based edge detection methods, the proposed modified PCNN is much easier to control, and the optimal result can be achieved instantly after all neurons pulsed. Furthermore, the proposed method is shown to be able to distinguish the isolated pixels from step edge pixels better than derivative edge detectors.