Digital Signal Processing: An Introduction with MATLAB and by Amin Z. Sadik, Peter O'Shea, Zahir M. Hussain

By Amin Z. Sadik, Peter O'Shea, Zahir M. Hussain

In 3 elements, this ebook contributes to the development of engineering schooling and that serves as a basic reference on electronic sign processing. half I offers the fundamentals of analog and electronic signs and structures within the time and frequency area. It covers the center issues: convolution, transforms, filters, and random sign research. It additionally treats very important functions together with sign detection in noise, radar diversity estimation for airborne goals, binary communique structures, channel estimation, banking and monetary functions, and audio results creation. half II considers chosen sign processing structures and methods. center issues coated are the Hilbert transformer, binary sign transmission, phase-locked loops, sigma-delta modulation, noise shaping, quantization, adaptive filters, and non-stationary sign research. half III offers a few chosen complex DSP themes.

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Extra info for Digital Signal Processing: An Introduction with MATLAB and Applications

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1 Probability and Sample Space Probability Probability is a mathematical construct that describes the statistical regularity of the outcomes of a repeating situation or experiment. Sample space The sample space is the set of all possible outcomes of a repeating experiment. Example 1 In a coin-tossing experiment, outcomes are either ‘‘heads’’ or ‘‘tails’’, hence the sample space is S = {h, t}. If tossing is repeated a large number of times, then there will be approximately 50% heads and 50% tails.

It can be shown that the auto-correlation function has the following properties (see Tutorial 26): P1: Rx(s) is even (or symmetric about time-delay axis), Rx(s) = Rx(-s). P2: Rx(s) always has its absolute maximum at the origin, s = 0. (see, for example, Fig. 20, which shows the autocorrelation function of a nonperiodic signal). The correlation integral between two signals tends to have a large maximum value at s = 0 if the signals are exactly the same (auto-correlation), and has a finite non-zero value over some range of s when the two signal are somewhat similar.

Now assume a white noise input n(t) with constant PSD is entering an ideal LPF as shown in Fig. 24. The output noise PSD is then given by: g Gno ðf Þ ¼ jHðf Þj2 Gn ðf Þ ¼ P2B ðf Þ: 2 Hence, using the WKT, the autocorrelation function of the output noise is: g Rno ðsÞ ¼ F À1 fGno ðf Þg ¼ 2B sincð2B sÞ ðsee Fig. 25Þ: 2 Therefore, the values of the output noise are no longer uncorrelated, except when s = k/2B,k being an integer. LPF n ( t ), Input Noise n ( t ), o Output Noise H(f) G (f) η /2 o η /2 1 f 0 Gn ( f ) H(f) n −B 0 B f −B 0 B f Fig.

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