Application of Soft Computing Techniques to RADAR Pulse Compression

Soft Computing is a term associated with fields characterized by the use of inexact solutions to computationally-hard tasks for which an exact solution cannot be derived in polynomial time. Almost contrary to conventional (Hard) computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Effectively, it resembles the Human Mind. The Soft Computing Techniques used in this project work are Adaptive Filter Algorithms and Artificial Neural Networks. An adaptive filter is a filter that self-adjusts its transfer function according to an optimizing algorithm. The adaptive filter algorithms used in this project work are the LMS algorithm, the RLS algorithm, and a slight variation of RLS, the Modified RLS algorithm. An Artificial Neural Network (ANN) is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. Several models have been designed to realize an ANN. In this project, Multi-Layer Perceptron (MLP) Network is used. The algorithm used for modeling such a network is Back-Propagation Algorithm (BPA). Through this project, there has been analyzed a possibility for using the Adaptive Filter Algorithms to determine optimum Matched Filter Coefficients and effectively designing Multi-Layer Perceptron Networks with adequate weight and bias parameters for RADAR Pulse Compression. Barker Codes are taken as system inputs for Radar Pulse Compression. In case of Adaptive Filters, a convergence rate analysis has also been performed for System Identification and in case of ANN, Function Approximation using a 1-2-1 neural network has also been dealt with. A comparison of the adaptive filter algorithms has been performed on the basis of Peak Sidelobe Ratio (PSR). Finally, SSRs are obtained using MLPs of varying neurons and hidden layers and are then compared under several criteria like Noise Performance and Doppler Tolerance

Two site self consistent method for front propagation in reaction-diffusion system

We study front propagation in the reaction diffusion process $A\leftrightarrow2A$ on one dimensional lattice with hard core interaction between the particles. We propose a two site self consistent method (TSSCM) to make analytic estimates for the front velocity and are in excellent agreement with the simulation results for all parameter regimes. We expect that the simplicity of the method will allow one to use this technique for estimating the front velocity in other reaction diffusion processes as well.Comment: 6 figure

Morphotypes vis-a-vis genetic parameters of Catla catla (Ham.) and Labeo rohita (Ham.) backcrosses

Backcross generations of Catla catla (Ham.) and Labeo rohita (Ham.) were developed in Central Agricultural Research Institute, Port Blair, South Andaman, India, using the technique of induced breeding for Indian Major Carps. The trend of morphometry through generation mean analysis indicates reduction of head size with respect to standard length, which is considered as a reduction of bone size within whole body biomass. The segregation pattern of dominant head morphometries of rohu and partial dominance of body morphometries of catla was supported by subsequent genetic evaluation through karyotyping, biochemical analysis and PCR-random amplified polymorphic DNA (RAPD) based molecular marker analysis indicating more genetic proximity of rohu with backcrosses than catla. The present study is significant for carp genetics with special reference to catla and rohu.Keywords: Backcross, catla, esterase, karyomorphology, molecular marker, morphometries, rohu.African Journal of Biotechnology Vol. 12(36), pp. 5503-551