CHAPTER 1: CONTINUOUS AND DETERMINISTIC SIGNALS1.1 Continuous Deterministic Signals1.2 Sampling of Continuous Signals-Discrete Signals1.3 Signal conditioning and manipulation1.4 Convolution of analog and discrete signals1.5 MATLAB use for vectors and arrays (matrices)CHAPTER 2: FOURIER ANALYSIS OF CONTINUOUS AND DISCRETE SIGNALS2.1 Introduction2.2 Fourier transform (FT) of deterministic signals2.3 Sampling of signals2.4 Discrete time Fourier transform (DTFT)2.5 DTFT of finite-time sequences2.6 The discrete Fourier transform (DFT)Appendix 2.1 Fourier transform propertiesAppendix 2.2 Fourier transform pairsAppendix 2.3 DTFT propertiesAppendix 2.4 DFT propertiesCHAPTER 3: THE Z-TRANSFORM, DIFFERENCE EQUATIONS AND DISCRETE SYSTEMS3.1 The z-transform3.2 Properties of the z-transform3.3 Inverse z-transform3.4 Transfer function3.5 Frequency response of discrete systems3.6 Z-transform solution of difference equationsCHAPTER 4: DIGITAL FILTER DESIGN4.1 Introduction4.2 Finite impulse response (FIR) filterAppendix 4.1: Window characteristics and performanceCHAPTER 5: RANDOM VARIABLES, SEQUENCIES AND PROBABILITY FUNCTIONS5.1 Random signals and distributions5.2 Averages5.3 Stationary processes5.4 Probability density functions5.5 Transformations of PDF'sCHAPTER 6: LINEAR SYSTEMS WITH RANDOM INPUTS, FILTERING POWER SPECTRAL DENSITY6.1 Spectral representation6.2 Linear systems with random inputs6.3 Autoregressive moving average processes6.4 Autoregressive (AR) process6.5 Parametric representations of stochastic processes: ARMA and ARMAX models CHAPTER 7: LEAST SQUARES-OPTIMUM FILTERING7.1 Introduction7.2 The least squares approach7.3 linear least squares7.3.1 Matrix formulation of linear least squares7.4 Point estimation7.5 Mean square error (MSE)7.6 Finite impulse response (FIR) Wiener filter7.7 Wiener solution----Orthogonal principle7.8 Wiener filtering examplesCHAPTER 8: NONPARAMETRIC (CLASSICAL) SPECTRA ESTIMATION8.1 Periodogram and correlogram spectra estimation8.2 Book proposed method for better resolution using transformation of the random variables8.3 Daniel periodogram8.4 Bartlett periodogram8.5 Blackman-Tukey (BT) method8.6 Welch methodAppendix 8.1: Important window and their spectraCHAPTER 9: PARAMETRIC AND OTHER METHODS FOR SPECTRA ESTIMATION9.1 Introduction9.2 AR, MA and ARMA models9.3 Yule-Walker (YW) equations9.4 Least-squares (LS) method and linear prediction9.5 Minimum variance9.6 Model order9.7 Levinson-Durbin algorithm9.8 Maximum entropy method9.9 spectrums of segmented signals9.10 Eigenvalues and eigenvectors of matrices (see also Appendix 2) CHAPTER 10: NEWTON'S AND STEEPEST DESCENT METHODS10.1 Geometric properties of the error surface10.2 One-dimensional gradient search method10.3 Steepest descent algorithm10.4 Newton's method10.5 Solution of the vector difference equationCHAPTER 11: THE LEAST MEAN-SQUARE (LMS) ALGORITHM11.1 Introduction11.2 The LMS algorithm11.3 Examples using the LMS algorithm11.4 *Performance analysis of the LMS algorithm11.5 *Complex representation of the LMS algorithmCHAPTER 12: VARIANTS OF LEST MEAN-SQUARE ALGORITHM12.1 The Normalized Least Mean-Square Algorithm12.2 Power Normalized LMS12.3 Self-Correction LMS Filter12.4 The Sign-Error LMS Algorithm12.5 The NLMS Sign-Error Algorithm12.6 The Sign-Regressor LMS Algorithm12.7 Self-Correcting Sign-Regressor LMS Algorithm12.8 The Normalized Sign-Regressor LMS Algorithm12.9 The Sign-Sign LMS Algorithm12.10 The normalized Sign-Sign LMS Algorithm12.11 Variable Step-Size LMS Algorithm12.12 The Leaky LMS Algorithm12.13 The Linearly Constrained LMS Algorithm12.14 The Least Mean Fourth Algorithm12.15 The Least Mean Mixed Normal (LMMN) LMS Algorithm12.16 Short-Length Signal of the LMS Algorithm12.17 The Transform Domain LMS Algorithm12.18 The Error Normalized Step-Size LMS Algorithm12.19 The Robust Variable Step-Size LMS Algorithm12.20 The Modified LMS Algorithm12.21 Momentum LMS Algorithm12.22 The Block LMS Algorithm12.23 The Complex LMS Algorithm12.24 The Affine LMS Algorithm12.25 The Complex Affine LMS AlgorithmCHAPTER 13: NONLINEAR FILTERING13.1 Introduction13.2 Statistical Preliminaries 13.3 Mean Filter13.4 Median Filter13.5 Trimmed-Type Mean Filter13.6 L-Filters13.7 Ranked-Order Statistic Filter13.8 Edge-Enhancement Filters13.9 R-FiltersAPPENDICESAppendix 1: Suggestions and explanations for MATLAB useAppendix 2: MATLAB functionsAppendix 3: Mathematical formulasAppendix 4: Langrange multiplier methodAppendix 5: Matrix analysis

The book discusses receiving signals that most electrical engineers detect and study. The vast majority of signals could never be detected due to random additive signals, known as noise, that distorts them or completely overshadows them. Such examples include an audio signal of the pilot communicating with the ground over the engine noise or a bioengineer listening for a fetus’ heartbeat over the mother’s. The text presents the methods for extracting the desired signals from the noise. Each new development includes examples and exercises that use MATLAB to provide the answer in graphic forms for the reader's comprehension and understanding.