A study on adaptive filtering for noise and echo cancellation.

The objective of this thesis is to investigate the adaptive filtering technique on the application of noise and echo cancellation. As a relatively new area in Digital Signal Processing (DSP), adaptive filters have gained a lot of popularity in the past several decades due to the advantages that they can deal with time-varying digital system and they do not require a priori knowledge of the statistics of the information to be processed. Adaptive filters have been successfully applied in a great many areas such as communications, speech processing, image processing, and noise/echo cancellation. Since Bernard Widrow and his colleagues introduced adaptive filter in the 1960s, many researchers have been working on noise/echo cancellation by using adaptive filters with different algorithms. Among these algorithms, normalized least mean square (NLMS) provides an efficient and robust approach, in which the model parameters are obtained on the base of mean square error (MSE). The choice of a structure for the adaptive filters also plays an important role on the performance of the algorithm as a whole. For this purpose, two different filter structures: finite impulse response (FIR) filter and infinite impulse response (IIR) filter have been studied. The adaptive processes with two kinds of filter structures and the aforementioned algorithm have been implemented and simulated using Matlab.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .J53. Source: Masters Abstracts International, Volume: 44-01, page: 0472. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Genetic algorithms for designing digital filters

This thesis presents a method of adapting IIR filters implemented as lattice structures using a Genetic Algorithm (GA), called ZGA. This method addresses some of the difficulties encountered with existing methods of adaptation, providing guaranteed filter stability and the ability to search multi-modal error surfaces. ZGA mainly focuses on convergence improvement in respects of crossover and mutation operators. Four kinds of crossover methods are used to scan as much as possible the potential solution area, only the best of them will be taken as ZGA crossover offspring. And ZGA mutation takes the best of three mutation results as final mutation offspring. Simulation results are presented, demonstrating the suitability of ZGA to the problem of IIR system identification and comparing with the results of Standard GA, Genitor and NGA

Digital IIR filter design using adaptive simulated annealing

Adaptive infinite-impulse-response (IIR) filtering provides a powerful approach for solving a variety of practical problems. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are required in order to avoid local minima. We apply a global optimisation method, called the adaptive simulated annealing (ASA), to digital IIR filter design. An important advantage of the ASA is the simplicity in software programming. Simulation study involving system identification application shows that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the ASA offers a viable tool to digital IIR filter design

NATURAL ALGORITHMS IN DIGITAL FILTER DESIGN

Digital filters are an important part of Digital Signal Processing (DSP), which plays vital roles within the modern world, but their design is a complex task requiring a great deal of specialised knowledge. An analysis of this design process is presented, which identifies opportunities for the application of optimisation. The Genetic Algorithm (GA) and Simulated Annealing are problem-independent and increasingly popular optimisation techniques. They do not require detailed prior knowledge of the nature of a problem, and are unaffected by a discontinuous search space, unlike traditional methods such as calculus and hill-climbing. Potential applications of these techniques to the filter design process are discussed, and presented with practical results. Investigations into the design of Frequency Sampling (FS) Finite Impulse Response (FIR) filters using a hybrid GA/hill-climber proved especially successful, improving on published results. An analysis of the search space for FS filters provided useful information on the performance of the optimisation technique. The ability of the GA to trade off a filter's performance with respect to several design criteria simultaneously, without intervention by the designer, is also investigated. Methods of simplifying the design process by using this technique are presented, together with an analysis of the difficulty of the non-linear FIR filter design problem from a GA perspective. This gave an insight into the fundamental nature of the optimisation problem, and also suggested future improvements. The results gained from these investigations allowed the framework for a potential 'intelligent' filter design system to be proposed, in which embedded expert knowledge, Artificial Intelligence techniques and traditional design methods work together. This could deliver a single tool capable of designing a wide range of filters with minimal human intervention, and of proposing solutions to incomplete problems. It could also provide the basis for the development of tools for other areas of DSP system design

Monitoring networks optimization with simulated annealing

Tese dout., Ciências de Engenharia, Instituto Superior Técnico, Universidade Técnica de Lisboa, 2003In this work some methods to optimize environmental monitoring networks are proposed. These methods share simulated annealing as the approximation algorithm. Only monitoring networks reduction is treated here. Monitoring network optimization is a very actual problem given the large number of existing networks in many countries operating large numbers of stations, some of which may be redundant, with very high exploitation costs. Difficulties appear when exploitation costs pushes the dimension of a network towards a minimum, and the statistical reliability pushes in the opposite direction. Finding the optimal dimension may be a very difficult optimization problem due to the large number of combinations, even for small network dimensions. Further complications appear when the available data is too incomplete or come from different homogeneous areas. Some practical answers to these problems were sought in this work. Results showed that optimizing a monitoring network dimension and location of stations, without compromising the quality of the collected data, could attain large reductions in exploitation costs. Simulated annealing showed to be a very flexible and efficient algorithm

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide