Digital Signal Processing By Mitra
Sourn Rose
Digital Signal Processing: A Computer-Based Approach is intended for a two-semester course on digital signal processing for seniors or first-year graduate students. Based on user feedback, a number of new topics have been added to the third edition, while some excess topics from the second edition have been removed. The author has taken great care to organize the chapters more logically by reordering the sections within chapters. More worked-out examples have also been included. The book contains more than 500 problems and 150 MATLAB exercises.
In the late 1960s, I switched my research interest to the emerging field of digital signal processing. The field matured and flourished in the 1960s and 1970s, and digital signal processing became widely used with specialized digital signal processor chips in the 1980s. The impact of signal processing on the society can be seen from the article published in the IEEE Signal Processing Magazine.
During my active teaching career, I have worked on all various problems in digital signal processing. One of the earliest problems being pursued by some researchers was the development of digital filter structures with low coefficient sensitivity on their frequency responses. Published works were based on the analysis of biquadratic structures. With one of my doctoral students, I developed the conditions for realizing low-sensitivity infinite impulse response (IIR) and finite impulse response (FIR) digital filter structures along with several realization methods.
One challenge I faced in the beginning of my teaching career was to convince my professional colleagues that it is important to develop algorithms with the least number of digital multipliers. Even though signal processor chips were commercially available, they consume power and occupy a large amount of space on a practical fully integrated circuit. With my student and a visiting researcher, we developed digital filter structures with the least number of digital multipliers. Our work on the design of finite-impulse response (FIR) and infinite impulse response (IIR) digital filters with the fewest number of multipliers have been incorporated in two commercially available software packages: Signal Processing Toolbox of MATLAB ( ) and LabVIEW ( ) of National Instruments.
Subband DFT ( -dft-part-i-definition-interpretation-and-extensions) is based on a decomposition of the time-domain samples into a set of smaller length subsequences approximately separated in the spectral domain. The frequency-separation property of the subsequences permits elimination of the subsequences with negligible energy contribution from the DFT calculation, thus resulting in a faster approximate DFT computation.
Warped DFT ( ) is the evaluation of frequency samples at nonuniformly spaced points on the unit circle and has been applied to spectral analysis, design of tunable FIR filters, resolving closely spaced sinusoids, frequency estimation of noise-corrupted short-length sinusoid, robust speech recognition, perceptual noise reduction system, perceptual speech enhancement and signal analysis.
Nonuniform DFT ( -uniform_discrete_Fourier_transform) is the frequency domain representation of a finite-length sequence located at unequally spaced points. It has been applied to discrete multitone transmission, antenna array design, magnetic resonance imaging, numerical solution of partial differential equations and spectral analysis.
Many of the projects that my students and I worked on were suggested by the industry and involved the processing of images and videos. For example, my student and I along with a visitor from Italy patented a method for equalization of practical (non-ideal) transmission channels which considerably simplified the decoding of corrupted data sequences. This patent has been licensed by Alcatel/Telettra Corporation and has been incorporated in one of their equipment.
I believe that it is imperative for scientists/engineers to engage with industry and identify the problems related to signal processing that are important to them and work on these problems. It is also important to work on non-traditional problems.
Sanjit K. Mitra is a Professor Emeritus of Electrical & Computer Engineering, University ofCalifornia, Santa Barbara, California and Professor Emeritus, Ming Hsieh Department ofElectrical Engineering, University of Southern California, Los Angeles. He has held short termvisiting appointments at universities in Australia, Austria, Brazil, Croatia, Finland, Germany,India, Japan, Norway, Singapore, Turkey, and the United Kingdom. Dr. Mitra has published 246journal papers and 465 conference papers in the areas of analog and digital signal processing,and image processing. He has authored and co-authored twelve books, and holds six patents.He has presented 31 keynote and/or plenary lectures at conferences held in the United Statesand 17 countries abroad. He has also presented over 580 invited lectures in 43 countries. Hehas served as an external examiner of 17 doctoral dissertations from universities in 5 countries.He has served as an Advisor to the Department of Radio Engineering, Peking University,Beijing, People's Republic of China, August 1983 (under a World Bank Development Program).Dr. Mitra has served IEEE in various capacities including service as the President of the IEEECircuits & Systems Society in 1986.
Advances in integrated circuit technology have had a major impact on the technical areas to which digital signal processing techniques and hardware are being applied. A thorough understanding of digital signal processing fundamentals and techniques is essential for anyone whose work is concerned with signal processing applications.
Digital Signal Processing begins with a discussion of the analysis and representation of discrete-time signal systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. Emphasis is placed on the similarities and distinctions between discrete-time. The course proceeds to cover digital network and nonrecursive (finite impulse response) digital filters. Digital Signal Processing concludes with digital filter design and a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform.
For your colleagues that have a copy of my DSP book,I'll be happy to send them the errata for my book. All they have to do is send me an E-mail telling me (1) The Edition Number, and (2) the Printing Number of their copy of the book. The Printing Number can be found on the page just before the 'Dedication' page.My E-mail address is:R.Lyons [at] ieee.org
For a more informal introduction, I like A Digital Signal Processing Primer by Ken Steiglitz, which is exactly what it says it is. I TAed a class using this text and really liked the style. It's well written, and makes the material pretty interesting.
If you need to pick one of them, pick - Discrete-Time Signal Processing Prentice-Hall Signal Processing Series by Alan V. Oppenheim, Ronald W. Schafer, John R. Buck. Of course, as listed in Hossein's answer Sanjit Mitra might just be easy for beginner.
In addition to the already mentioned books, if you are focused towards algorithm development, Proakis' Digital Signal Processing using MATLAB is an excellent resource for starters. The numerical recipes series is also an excellent resource regarding how to implement some core DSP algorithms (spectral decomposition, convolutions, interpolation and extrapolation etc.) in practical situations.
I found this applet very helpful when understanding the nature of convolution in time. The Joy of Convolution. It lets you "draw" your time signals and convolve them so you get a picture of what's happening in the time domain.
This book will go through different projects that will teach the reader how to write software: to improve their singing, synthesize different guitar sounds, change the human brainwave, break glass, help people to relax and learn about many different sound engineering and DSP tools : DFT, FFT, High pass filter, low pass filter, fundamental frequency, Karplus-strong algorithm. In this book they will learn about: Isochronic tones, Binaural beats, and Monaural beats and how to code them. Then they will be able to come up with their own beats. They will learn about sound waves and a lot more.There are very few books / websites that show people how to code DSP tools. There are a lot that show the theory but not many that show the application, so I think this Book would be very useful for high school students, college students, and inter level employees.
I routinely teach on-line courses on DSP that combine pre-recorded video that you can watch at your own pace with live interactive workshop sessions and tons of examples and material in Python in Jupyter Notebooks. These courses are geared toward wireless communications and software radio (my primary background), but provide general and practical DSP for many fields by covering foundational topics such as the Fourier, Laplace and z-transforms in a way that you weren't taught in school (very intuitive), and applying that to practical FIR filter design, the FFT, multi-rate processing, and control loop implementations commonly used in software radio. I focus on building a very intuitive understanding of practical signal processing implementations and working in the time and frequency domains.
Some people like to focus on DSP as a subject in of itself. I like to think that learning is more of a spiral than a linear progression. I would suggest that you pursue an application that interests you that uses signal processing and there are many and growing. Most of the important breakthroughs in DSP were found by people solving their own problems. All the books suggested above are very good. An interesting problem with a simple solution is typically more appealing to a student to a page of proofs, unless you like a page a proofs and that works too.
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