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  • (1)Samuel D. Stearns, Ruth A. David, Signal Processing Algorithms in MATLAB, Prentice Hall, 1996

    This book is fantastice. It starts basic and moves rapidly along to practical filter design. 100's of examples are given on the enclosed disk (in Matlab .m files). This means the reader can tinker around with each example until he understands the details. You can use the .m files to build your own simulations and filters. This is an excellent reference for the working engineer, or as a tutorial for the working engineer who needs to come up to speed on DSP.

  • (2)Bernard Widrow, Samuel D. Stearns, Adaptive Signal Processing, Prentice Hall, 1985.
  • Before I read this book, adaptive filtering was a mystery and the LMS algorithm looked like a programming nightmare. It looked like more Kalman filtering. My eyes have been opened. The LMS algorithm for adaptive filtering is almost as simple as Tit for Tat is for game theory. This is the gateway text to understand adaptive filtering, adaptive arrays (help the navy find the rogue submarine), adaptive equalization (design the next generation of modem), adaptive control (say goodbye to overshoot), adaptive prediction (beat the stock market). Furthermore, it leads naturally into the artificial intelligence techniques. If you are an engineer or a programmer, you should have this tool in your toolbox.

  • (3)Tamal Bose, Digital Signal and Image Processing, Wiley, 2004

    This book offers a good introduction to both digital signal processing and image processing all in the same book. This allows engineering students to "think outside the box" pushing them from 1 to 2 dimensions. Bose does a good job by including a two-dimensional processing section at the end of almost every chapter. The Matlab help included in each chapter is also priceless.

  • (4)Gilbert Strang, Truong Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996
    think the book is very good compared to other texts that I have perused on the subject. I am presently teaching myself the material so as to use wavelets for my research project. It is one of the most readable texts that I have encountered. I was looking for a very applied book and this seems a good one. There are some proofs but the text is not flooded with functional analysis theorems. There is one thing that I found disappointing. I found that in order to understand some things I had to jump to sections further into the book. Ideas are introduced and are not fully explained until later in the book. This leaves the reader puzzled and sometimes very confused when given the first exposure to a topic .(It is easy to get the wrong idea when you are given a superficial development). I think the development of ideas could have been more cohesive. Other than that, it is a good book.

  • (5)Khalid Sayood, Introduction to Data Compression, Morgan Kaufman Publishers, Inc, 1996
    This book has all the ingredients for a great textbook. It provides good theoratical background without going into unnecessary details, gives lot of discussion about applications, provides great exercise problems, and above all it has outstanding examples that makes some of the difficult concepts easy to understand.
    Data compression needs a lot of background in information theory and other areas specific to speech, image processing etc. It is impossible to give a rigourous theoratical treatment of all of those in one volume. A strong point of this book is that it gives you just enough background on a variety of topics - without making the whole book obscure. In that respect, it is very application and implementation oriented. It is in fact what it says it is: A very good "INTRODUCTION to Data Compression"

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