Paperback: 432 pages
Publisher: McGraw-Hill Book Companies; 1st edition (August 31, 1998)
Product Dimensions: 8.3 x 0.7 x 10.8 inches
Shipping Weight: 1.8 pounds
Average Customer Review: 4.1 out of 5 stars See all reviews (30 customer reviews)
Best Sellers Rank: #936,883 in Books (See Top 100 in Books) #37 in Books > Computers & Technology > Hardware & DIY > Microprocessors & System Design > DSPs #232 in Books > Textbooks > Engineering > Electrical & Electronic Engineering #261 in Books > Engineering & Transportation > Engineering > Electrical & Electronics > Electronics > Microelectronics
This outline could never stand alone as a DSP tutorial, but it is excellent if you need extra problems to solve or if you need a refresher course in elementary DSP topics. Chapter one starts where any DSP course usually starts - with a quick review of signals and systems. Chapter two is on Fourier analysis and discusses all of the basics including the concept of filtering, interconnection of systems, and finally the discrete time Fourier transform and its properties. Chapter 3 is on sampling, and includes a good discussion of analog to digital conversion and how it can induce aliasing. Next the converse, digital to analog conversion, is discussed as well as discrete time processing of continuous signals and finally sample rate conversion. Chapter 3 is especially useful, since most DSP texts do not go into as much detail on practical A/D and D/A conversion topics as this chapter does. Chapter four finally gets into the z-transform - its definition, its properties, and its inverse. Chapter 5 is about the transform analysis of systems and specifically how the z transform makes the analysis of such systems much simpler than what was done in earlier chapters. Chapter six discusses the discrete Fourier transform, which is a finite-series version of the DTFT, which was discussed in chapter two. Because the Discrete Fourier Transform has a time complexity of NxN, the next chapter discusses its more practical alternative the Fast Fourier Transform, which has an NlogN time complexity. This might seem trivial at first, but if you are filtering 1Kx1K pixel images, the difference becomes significant. Although this chapter is very brief, it does a pretty good job of driving home the main points of the algorithm. Also, it has some pretty good exercises on the FFT, which are usually hard to find in textbooks.
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