Handbook of Formulas and Tables for Signal Processing 1st edition by Alexander Poularikas ISBN 0367400316 978-0367400316

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ISBN 10: 0367400316
ISBN 13: 978-0367400316
Author:  Alexander Poularikas

Signal processing is a broad and timeless area. The term “signal” includes audio, video, speech, image, communication, geophysical, sonar, radar, medical, and more. Signal processing applies to the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
Handbook of Formulas and Tables for Signal Processing a must-have reference for all engineering professionals involved in signal and image processing. Collecting the most useful formulas and tables – such as integral tables, formulas of algebra, formulas of trigonometry – the text includes:

• Material for the deterministic and statistical signal processing areas
  
• Examples explaining the use of the given formula
  
• Numerous definitions
  
• Many figures that have been added to special chapters
  Handbook of Formulas and Tables for Signal Processing brings together – in one textbook – all the equations necessary for signal and image processing for professionals transforming anything from a physical to a manipulated form, creating a new standard for any person starting a future in the broad, extensive area of research.

Handbook of Formulas and Tables for Signal Processing 1st Table of contents:

Preface

• Introduction to Signal Processing
• Purpose of the Handbook
• How to Use This Book
• Acknowledgments

Chapter 1: Fundamentals of Signal Processing
1.1. Definition of Signals and Systems
1.2. Types of Signals: Continuous and Discrete
1.3. Time and Frequency Domain Analysis
1.4. Analog vs. Digital Signal Processing
1.5. The Sampling Theorem
1.6. Nyquist Rate and Aliasing

Chapter 2: Fourier Transform and Spectral Analysis
2.1. Fourier Series
2.2. Continuous Fourier Transform (CFT)
2.3. Discrete Fourier Transform (DFT)
2.4. Fast Fourier Transform (FFT)
2.5. Spectral Density
2.6. Windowing Techniques
2.7. Spectrogram and Time-Frequency Analysis

Chapter 3: Filters and Filter Design
3.1. Types of Filters: Low-pass, High-pass, Band-pass, Band-stop
3.2. Analog vs. Digital Filters
3.3. Filter Specifications and Design Methods
3.4. FIR Filters: Properties and Design
3.5. IIR Filters: Properties and Design
3.6. Butterworth, Chebyshev, and Elliptic Filters
3.7. Filter Implementations and Optimizations

Chapter 4: Digital Signal Processing (DSP) Algorithms
4.1. Digital Signal Processing Basics
4.2. Z-Transform and Its Applications
4.3. Linear Time-Invariant Systems (LTI)
4.4. Convolution and Correlation
4.5. Recursive and Non-recursive Filters
4.6. Fast Convolution Algorithms
4.7. Multirate Signal Processing

Chapter 5: Signal Modulation and Demodulation
5.1. Amplitude Modulation (AM)
5.2. Frequency Modulation (FM)
5.3. Phase Modulation (PM)
5.4. Quadrature Amplitude Modulation (QAM)
5.5. Pulse Code Modulation (PCM)
5.6. Pulse Amplitude Modulation (PAM)
5.7. Demodulation Techniques

Chapter 6: Signal Compression
6.1. Overview of Signal Compression
6.2. Lossy vs. Lossless Compression
6.3. Huffman Coding
6.4. Run-Length Encoding
6.5. Transform Coding Techniques: DCT, Wavelet Transform
6.6. Audio and Image Compression Algorithms (JPEG, MP3)
6.7. Video Compression (MPEG, H.264)

Chapter 7: Signal Detection and Estimation
7.1. Detection Theory
7.2. Maximum Likelihood Estimation (MLE)
7.3. Least Squares Estimation
7.4. Bayesian Estimation
7.5. Kalman Filter
7.6. Wiener Filter
7.7. Detection of Signals in Noise

Chapter 8: Time-Frequency and Wavelet Transforms
8.1. Time-Frequency Analysis Overview
8.2. Short-Time Fourier Transform (STFT)
8.3. Wavelet Transform: Theory and Applications
8.4. Continuous Wavelet Transform (CWT)
8.5. Discrete Wavelet Transform (DWT)
8.6. Multiresolution Analysis

Chapter 9: Statistical Signal Processing
9.1. Random Signals and Processes
9.2. Mean, Variance, and Covariance of Signals
9.3. Autocorrelation and Cross-correlation
9.4. Power Spectral Density
9.5. Wiener-Khinchin Theorem
9.6. Random Signal Detection and Estimation

Chapter 10: Signal Reconstruction and Interpolation
10.1. Signal Reconstruction from Samples
10.2. Interpolation Techniques
10.3. Sinc Function and Ideal Reconstruction
10.4. Practical Reconstruction Algorithms
10.5. Multidimensional Signal Interpolation

Chapter 11: Communication Systems and Signal Processing
11.1. Introduction to Communication Systems
11.2. Modulation and Demodulation in Communication
11.3. Signal Processing for Data Transmission
11.4. Error Detection and Correction Techniques
11.5. OFDM and MIMO Systems
11.6. Signal Processing in Wireless Communication

Appendices
A.1. Common Signal Processing Formulas
A.2. Key Tables for Fourier Transforms and Convolution
A.3. Filter Design Tables (Butterworth, Chebyshev, etc.)
A.4. Sampling and Quantization Tables
A.5. Useful MATLAB and Python Functions for Signal Processing

Index

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