Digital Signal Processing

Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of digital signal processing (DSP) while also providing a working knowledge that they take with them into their engineering careers. Many instructive, worked examples are used to illustrate the material, and the use of mathematics is minimized for an easier grasp of concepts. As such, this title is also useful as a reference for non-engineering students and practicing engineers.This book goes beyond DSP theory, showing the implementation of algorithms in hardware and software. Additional topics covered include DSP for artificial intelligence, adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as pulse-code modulation, µ-law, adaptive differential pulse-code modulation, multi-rate DSP, oversampling analog-to-digital conversion, sub-band coding, wavelet transform, and neural networks.
- Covers DSP principles with various examples of real-world DSP applications on noise cancellation, communications, control applications, and artificial intelligence - Includes application examples using DSP techniques for deep learning neural networks to solve real-world problems - Provides a new chapter to cover principles of artificial neural networks and convolution neural networks with back-propagation algorithms - Provides hands-on practice, with MATLAB code for worked examples and C programs for real-time DSP for students at elsevier.com/books-and-journals/book-companion/9780443273353 - Offers teaching support, including an image bank, full solutions manual, and MATLAB projects for qualified instructors, available for request at educate.elsevier.com/9780443273353

Lizhe Tan is a professor in the Department of Electrical and Computer Engineering at Purdue University Northwest. He received his Ph.D. degree in Electrical Engineering from the University of New Mexico, Albuquerque, in 1992. Dr. Tan has extensively taught signals and systems, digital signal processing, analog and digital control systems, and communication systems for many years. He has published a number of refereed technical articles in journals, conference papers and book chapters in the areas of digital signal processing. He has authored and co-authored 4 textbooks, and holds a US patent. Dr. Tan is a senior member of the IEEE and has served as an associate editor for several engineering journals.Jean Jiang is an associate professor in the Department of Engineering Technology at Purdue University Northwest. She received her Ph.D. degree in Electrical Engineering from the University of New Mexico, Albuquerque, in 1992. Dr. Jiang has taught digital signal processing, control systems and communication systems for many years. She has published a number of refereed technical articles in journals, conference papers and book chapters in the area of digital signal processing, and co-authored 4 textbooks. Dr. Jiang is a senior member of the IEEE.

1. Introduction to Digital Signal Processing2. Signal Sampling and Quantization3. Digital Signals and Systems4. Discrete Fourier Transform and Signal Spectra5. The z-Transform6. Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations7. Finite Impulse Response Filter Design8. Infinite Impulse Response Filter Design9. Adaptive Filters and Applications10. Waveform Quantization and Compression11. Multirate Digital Signal Processing, Oversampling of Analog-to-Digital Conversion, and Undersampling of Bandpass Signals12. Subband and Wavelet-Based Coding13. Image Processing Basics14. Digital Signal Processing for Artificial Intelligence15. Hardware and Software for Digital Signal Processors