Simulation of Communication Systems: Modeling, Methodology and Techniques (Information Technology: Transmission, Processing and Storage) Review

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(More customer reviews)The second edition is a much-improved version compared to the first one. More details are added -- which makes it easy to follow. Anyone who is doing system simulation or performance analysis should have one around. I would have rated it a 5-star if the authors should have included some of the algorithms in a CD to save reader's time.

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Since the first edition of this book was published seven yearsago, the field of modeling and simulation of communication systems hasgrown and matured in many ways, and the use of simulation as aday-to-day tool is now even more common practice. With the currentinterest in digital mobile communications, a primary area ofapplication of modeling and simulation is now in wireless systems of adifferent flavor from the `traditional' ones. This second edition represents a substantial revision of the first,partly to accommodate the new applications that have arisen. Newchapters include material on modeling and simulation of nonlinearsystems, with a complementary section on related measurementtechniques, channel modeling and three new case studies; aconsolidated set of problems is provided at the end of the book.

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Biomedical Signal Processing and Signal Modeling Review

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(More customer reviews)Good book for looking at the mathematics behind biomedical signal processing. If your going to use Matlab in conjunction with your study of this book, I used "Biosignal and Biomedical Image Processing: Matlab-Based Applications," by John L. Semmlow.

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A biomedical engineering perspective on the theory, methods, and applications of signal processing This book provides a unique framework for understanding signal processing of biomedical signals and what it tells us about signal sources and their behavior in response to perturbation. Using a modeling-based approach, the author shows how to perform signal processing by developing and manipulating a model of the signal source, providing a logical, coherent basis for recognizing signal types and for tackling the special challenges posed by biomedical signals-including the effects of noise on the signal, changes in basic properties, or the fact that these signals contain large stochastic components and may even be fractal or chaotic. Each chapter begins with a detailed biomedical example, illustrating the methods under discussion and highlighting the interconnection between the theoretical concepts and applications. The author has enlisted experts from numerous subspecialties in biomedical engineering to help develop these examples and has made most examples available as Matlab or Simulink files via anonymous ftp. Without the need for a background in electrical engineering, readers will become acquainted with proven techniques for analyzing biomedical signals and learn how to choose the appropriate method for a given application.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the author.

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Statistical Digital Signal Processing and Modeling Review

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(More customer reviews)I used this book to learn nearly all the topics covered in a hurry, in order to take the prelim exam at Berkeley. While it was a humbling experience, it made me truly learn to appreciate and love this book, and its great presentation and organization.
It starts off with a very good introduction to linear algebra and probability theory for engineers, which should give you a taste of the effective way that this book is laid out. The format is excellent, and the important points clearly highlighted. This is a real joy to read!
The magic doesn't wear off into the later chapters, which include topics in signal modeling, least-squares methods, MMSE estimation, Levinson algorithm, spectral estimation, and adaptive filters.
I find this book to be a great source for both learning and reference, and as a bonus it includes Matlab codes for all the algorithms mentioned here.
One complain is that there are certain topics that could be covered more effectively. For example, the relationship between the different signal models and filtering is not mentioned, and this could help understand the motivation of the different signal models in the first place.
Anyway, once you get past Oppenheim/Schafer, Proakis/Manolakis and Lyons' material this can be a great way to start your journey into the more advanced topics in signal processing.

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The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

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