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

Comparison between Oja's and BCM neural networks models in finding useful projections in high-dimensional spaces

This thesis presents the concept of a neural network starting from its corresponding biological model, paying particular attention to the learning algorithms proposed by Oja and Bienenstock Cooper & Munro. A brief introduction to Data Analysis is then performed, with particular reference to the Principal Components Analysis and Singular Value Decomposition. The two previously introduced algorithms are then dealt with more thoroughly, going to study in particular their connections with data analysis. Finally, it is proposed to use the Singular Value Decomposition as a method for obtaining stationary points in the BCM algorithm, in the case of linearly dependent inputs

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging