A Course in Digital Signal Processing by Boaz Porat

By Boaz Porat

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A accomplished, functional and updated exposition on electronic sign processing. either mathematical and worthwhile, this publication makes use of a rigorous method of aid readers research the speculation and perform of DSP. It discusses useful spectral research, together with using home windows for spectral research, sinusoidal sign research, and the impression of noise. It additionally covers FIR and IIR filters, together with precise layout strategies and MATLAB instruments.

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Chapter 7 reviews the z-transform, difference equations, and transfer functions. Like Chapter 2, it contains only material needed as a prerequisite for later chapters. Three chapters are devoted to digital filtering. Chapter 8 introduces the concept of filtering, filter specifications, magnitude and phase properties of digital filters, and review of digital filter design. Chapters 9 and 10 discuss the two classes of digital filters: finite impulse response (FIR)and infinite impulse response (IIR),respectively.

1) does not exist as a standard integral when x(t) is a WSSrandom signal. 5T], does possess a standard Fourier transform. The Fourier transform of a finite segment of a random signal appears random as well. 5. As we see, this figure is difficult to interpret and its usefulness is limited. 5. 1 Summary and Complements Summary In this chapter we reviewed frequency-domain analysis and its relationships to linear system theory. 3). The Fourier transform is a mathematical operation that (1) detects sinusoidal components in a signal and enables the computation of their amplitudes and phases and (2) provides the amplitude and phase density of nonperiodic signals as a function of the frequency.

67) Yx' 2. Kx (T) is a symmetric function of T, since Kx(T) = E{[x(t = E{[x(t) + T) - Jix][x(t) - Jix][x(t + T) - Jix]} - Jix]} = Kx( -T). 6. CONTINUOUS-TIME RANDOM SIGNALS 23 3. By the Cauchy-Schwarz inequality we have IKx(T)1 ~ UxUx = ;)Ix, for all T. 5 is wide-sense stationary. As we see, a WSS signal looks more or less the same at different time intervals. Although its detailed form varies, its overall (or macroscopic) shape does not. An example of a random signal that is not stationary is a seismic wave during an earthquake.

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