System And Signal Analysis

Digital signal processing lies at the heart of the communications revolution and is an essential element of key technologies such as mobile phones and the Internet. This book covers all the major topics in digital signal processing (DSP) design and analysis, supported by MatLab examples and other modelling techniques. The authors explain clearly and concisely why and how to use digital signal processing systems; how to approximate a desired transfer function characteristic using polynomials and ratio of polynomials; why an appropriate mapping of a transfer function on to a suitable structure is important for practical applications; and how to analyse, represent and explore the trade-off between time and frequency representation of signals. An ideal textbook for students, it will also be a useful reference for engineers working on the development of signal processing systems.

The book is written for an undergraduate course on the Signals and Systems. It provides comprehensive explanation of continuous time signals and systems , analogous systems, Fourier transform, Laplace transform, state variable analysis and z-transform analysis of systems. The book starts with the various types of signals and operations on signals. It explains the classification of continuous time signals and systems. Then it includes the discussion of analogous systems. The book provides detailed discussion of Fourier transform representation, properties of Fourier transform and its applications to network analysis. The book also covers the Laplace transform, its properties and network analysis using Laplace transform with and without initial conditions. The book provides the detailed explanation of modern approach of system analysis called the state variable analysis. It includes various methods of state space representation of systems, finding the state transition matrix and solution of state equation. The discussion of network topology is also included in the book. The chapter on z-transform includes the properties of ROC, properties of z-transform, inverse z-transform, z-transform analysis of LTI systems and pulse transfer function. The state space representation of discrete systems is also incorporated in the book. The book uses plain, simple and lucid language to explain each topic. The book provides the logical method of explaining the various complicated topics and stepwise methods to make the understanding easy. The variety of solved examples is the feature of this book. The book explains the philosophy of the subject which makes the understanding of the concepts very clear and makes the subject more interesting.

Handbook of Signal Processing Systems is organized in three parts. The first part motivates representative applications that drive and apply state-of-the art methods for design and implementation of signal processing systems; the second part discusses architectures for implementing these applications; the third part focuses on compilers and simulation tools, describes models of computation and their associated design tools and methodologies. This handbook is an essential tool for professionals in many fields and researchers of all levels.

This book is intended for use in teaching undergraduate courses on continuous-time and/or discrete-time signals and systems in engineering (and related) disciplines. It provides a detailed introduction to continuous-time and discrete-time signals and systems, with a focus on both theory and applications. The mathematics underlying signals and systems is presented, including topics such as: signal properties, elementary signals, system properties, continuous-time and discrete-time linear time-invariant systems, convolution, continuous-time and discrete-time Fourier series, the continuous-time and discrete-time Fourier transforms, frequency spectra, and the bilateral and unilateral Laplace and z transforms. Applications of the theory are also explored, including: filtering, equalization, amplitude modulation, sampling, feedback control systems, circuit analysis, Laplace-domain techniques for solving differential equations, and z-domain techniques for solving difference equations. Other supplemental material is also included, such as: a detailed introduction to MATLAB, a review of complex analysis, an introduction to partial fraction expansions, an exploration of time-domain techniques for solving differential equations, and information on online video-lecture content for material covered in the book. Throughout the book, many worked-through examples are provided. Problem sets are also provided for each major topic covered.

Signals and Transforms in Linear Systems Analysis covers the subject of signals and transforms, particularly in the context of linear systems theory. Chapter 2 provides the theoretical background for the remainder of the text. Chapter 3 treats Fourier series and integrals. Particular attention is paid to convergence properties at step discontinuities. This includes the Gibbs phenomenon and its amelioration via the Fejer summation techniques. Special topics include modulation and analytic signal representation, Fourier transforms and analytic function theory, time-frequency analysis and frequency dispersion. Fundamentals of linear system theory for LTI analogue systems, with a brief account of time-varying systems, are covered in Chapter 4 . Discrete systems are covered in Chapters 6 and 7. The Laplace transform treatment in Chapter 5 relies heavily on analytic function theory as does Chapter 8 on Z -transforms. The necessary background on complex variables is provided in Appendix A. This book is intended to serve as a text on signals and transforms for a first year one semester graduate course, primarily for electrical engineers.

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.

This highly experienced author sets out to build a bridge between two inter-disciplinary power engineering practices. The book looks into two major fields used in modern power systems: intelligent systems and the signal processing. The intelligent systems section comprises fuzzy logic, neural network and support vector machine. The author looks at relevant theories on the topics without assuming much particular background. Following the theoretical basics, he studies their applications in various problems in power engineering, like, load forecasting, phase balancing, or disturbance analysis.

Signal and System Analysis using MATLAB(R) is a textbook for Electronic Engineering Students and Design Engineers that introduces the main Digital Signal Processing (DSP) techniques required to perform Signal and System Analysis MATLAB(R). The primary aim of this book is to provide the analytical knowledge and practical techniques required for signal and system analysis by extensive use of the MATLAB(R) program, which is necessary for studying Digital Signal Processing to degree level and higher. The concept behind the book is to combine both the theory of Digital Signal Processing and the practical implementation of the theory using MATLAB(R). The goal is that students will gain an understanding of both the underlying theoretical concepts and how to apply them to real world problems using MATLAB(R). The chapters have been designed to enable students to develop their skills further by applying MATLAB(R) to all (50) problems, (161) examples, (290) equations and (449) figures. Worked examples of problems are shown in the book, followed by problems for students for practice. According to Fourier theory, a periodic signal can be represented by a Fourier series that contains the sum of a series of sine or cosine functions (harmonics) plus a Direct-Current (DC) term. The Continuous-Time Fourier Transform (CT-FT) can be used for non-periodic signal and is the way to express in the frequency domain a signal that is given in the time domain. The Laplace Transform is used to analyse the LTIC (Linear Time Inversion Continuous) systems and simplifies algebraic operations. The theories discussed in detail include; Continuous Time Convolution, Sampling, Quantizing, Reconstruction, Fourier analysis of Discrete-Time Signal, Discrete-Time convolution, circle convolution and the Fast Fourier Transform (FFT). The Z-Transform is an operation that transfers a discrete-time signal from the time domain (t) into the complex frequency domain (Z), and is a valuable tool in the digital signal processing field. Finally we discuss the Road to Wavelet Theory and its principles. Wavelet transform is a reversible transform, that is, it allows to go backwards and forwards between the time-domain and frequency-domain.