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quinquis digital signal processing using matlab 2007

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Textbook in PDF format Sometimes it is easier to say what a book is not than what it exactly represents. It may be also better to resume the authors’ motivations than to explain the book content itself. From this point of view, our book is certainly not a traditional course, although it recalls many theoretical signal processing concepts. Indeed, we emphasize a limited number of important ideas instead of making a detailed description of the involved concepts. Intuitive manners have been used to link these concepts to physical aspects. Hence, we hope that reading this book will be much more exciting than studying a traditional signal processing course. This book is also not a physics course, although a major purpose of most proposed exercises is to link abstract signal processing concepts to real-life problems. These connections are illustrated in a simple and comprehensive manner through MATLAB simulations. The main topics of this book cover the usual program of an undergraduate signal processing course. It is especially written for language and computer science students, but also for a much larger scientific community who may wish to have a comprehensive signal processing overview. Students will certainly find here what they are looking for, while others will probably find new and interesting knowledge. This book is also intended to illustrate our pedagogical approach, which is based on three major reasons: 1. Students need to know how the teaching provided can be useful for them; it is their customer attitude. 2. Students have good potential for doing independent work; their interest and curiosity should be continuously stimulated by: – using a diversified pedagogical approach that combines the two sides of a complete presentation methodology: from components to the system and vice versa; – encouraging them to take advantage of their creativity through interactive educational tools; they should be allowed to make changes and even contribute to their development. 3. Students have to improve and validate their knowledge through written work; writing is still the best way to focus someone’s concentration. The role of simulations is becoming more and more important in the framework of a scientific education because it is an effective way to understand many physical phenomena, some of them less known or mastered, and to take into account their complexity. Simulations may be thus very useful for: understanding working principles and deriving behavior laws; learning about processing methods and systems running using algorithms to reproduce them off-line; evaluating the performance and robustness of various algorithms and estimating the influence of different parameters. Simulations in signal processing education enable students to learn faster and facilitate the comprehension of the involved physical principles. From a teaching point of view, simulation tools lead to lower costs and time efficiency. This book is based on a signal processing course, which has been successfully given for many years in several universities. According to our experience, signal theory abstract concepts and signal processing practical potentialities can be linked only through tutorial classes and simulation projects. In this framework, simulations appear to be the necessary complement for the classical tripod theory – modeling – experimentation. This book brings together into a clear and concise presentation the main signal processing methods and the related results, using MATLAB software as a simulation tool. Why MATLAB? Because it is: – simple to learn and to use; – powerful and flexible; – accurate, robust and fast; – widespread in both academic and industrial environments; – continuously updated by professionals. The word signal stands for a physical entity, most often of an electrical nature, like that observed at a microphone output. It is submitted to various transformations when it goes through a system. Thus, in a communication chain, the signal is subject to some changes (distortion, attenuation, etc.), which can make it unrecognizable. The aim is to understand this evolution in order to properly recover the initial message. In other words, a signal is a physical support of information. It may carry the orders in a control and command equipment or multimedia (speech and image) over a network. It is generally very weak and it has to be handled with much caution in order to reach the signal processing final goal, i.e. information extraction and understanding. Signal processing is widely used in many industrial applications such as: telecommunications, audio and speech signal processing, radar, sonar, nondestructive control, vibrations, biomedicine, imagery, etc. Standard signal processing functions include signal analysis, improvement, synthesis, compression, detection, classification, etc., which depend on and interact with each other in an integrated information processing chain. The digital signal processing methods provide noteworthy capabilities: accurate system design, excellent equipment reproducibility, high stability of their exploitation characteristics and an outstanding supervision facility. The digital signal processing boom is related to the development of fast algorithms to calculate the discrete Fourier transform. Indeed, this is the equivalent of the Fourier transform in the discrete domain and so it is a basic tool to study discrete systems. However, related concepts are generally considered highly theoretical and accessible to scientific researchers rather than to most engineers. This book aims to overcome this difficulty by putting the most useful results of this domain within the understanding of the engineer. Chapter 1 briefly describes essential concepts of MATLAB software, which is an interactive software tailored for digital signal processing. Language rules, elementary operations as well as basic functions are presented. Chapter 2 illustrates the generation of 1D or 2D (image) digital signals as data vectors and matrices respectively. Finding the solution of a signal processing problem involves several distinct phases. The first phase is the modeling: the designer chooses a representation model for an observed data. When it can be done very accurately the signals are said to be deterministic. A powerful tool for analyzing them is provided by the Fourier transform, also called frequency representation, which is presented in Chapter 5 . Its equivalent in the discrete domain is represented by the z-transform, which is developed in Chapter 6. There are many other processes, which give different and apparently unpredictable results, although they are observed using identical experimental conditions. They are known as random processes, such as the receiver’s thermal noise. The wide sense stationary random processes, which form a particularly interesting class of these signals, are presented in Chapter 3 . Some useful statistical tools for testing different hypothesis about their parameters behavior are provided in Chapter 4. From a very general point of view, digital signal processing covers all the operations, arithmetical calculations and number handling performed on the signal to be processed, defined by a number series, in order to obtain a transformed number series representing the processed signal. Very different functions can be carried out in this way, such as classical spectral analysis (Chapter 10), time-frequency analysis (Chapters 11 and 12), linear filtering (Chapters 7 and 8), detection and estimation (Chapter 9), and feature extraction for information classification or compression (Chapters 13 and 14). Theoretical developments have been reduced to the necessary elements for a good understanding and an appropriate application of provided results. A lot of MATLAB programs, solved examples and proposed exercises make it possible to directly approach many practical applications. The reader interested in some more complementary information will find this in the references cited at the end of this book. Introduction Discrete-Time Signals Discrete-Time Random Signals Statistical Tests and High Order Moments Discrete Fourier Transform of Discrete-Time Signals Linear and Invariant Discrete-Time Systems Infinite Impulse Response Filters Finite Impulse Response Filters Detection and Estimation Power Spectrum Density Estimation Time-Frequency Analysis Parametrical Time-Frequency Methods Supervised Statistical Classification Data Compression

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quinquis digital signal processing using matlab 2007