## Digital Signal ProcessingCovers the analysis and representation of discrete-time signals and systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. Emphasis is placed on the similarities and distinctions between discrete-time and continuous-time signals and systems. Also covers digital network structures for implementation fo both recursive (infinite impulse response) and nonrecursive (finite impulse response) digital filters with four videocassettes devoted to digital filter design for recursive and nonrecursive filters. Concludes with a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform. |

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Page 2

As another

generally perturbed in a variety of ways, including channel distortion, fading, and

the insertion of background noise. One of the objectives at the receiver is to ...

As another

**example**, a signal transmitted over a communications channel isgenerally perturbed in a variety of ways, including channel distortion, fading, and

the insertion of background noise. One of the objectives at the receiver is to ...

Page 70

As a simple

equation y(n) = ay(n - 1) + x(n) The system function is Hiz) = j-Lp (2.56) and, by

the causality assumption, the region of convergence is \z\ > \a\, from which we

note ...

As a simple

**example**consider a causal system characterized by the differenceequation y(n) = ay(n - 1) + x(n) The system function is Hiz) = j-Lp (2.56) and, by

the causality assumption, the region of convergence is \z\ > \a\, from which we

note ...

Page 379

In this

is p. Similarly, since the probability of tails is 1 - p, so is the probability that xn = -

1 . The set of random variables {*„} for - oo < n < oo, together with the probabilistic

...

In this

**example**the probability of heads is p and thus the probability that x„ = + 1is p. Similarly, since the probability of tails is 1 - p, so is the probability that xn = -

1 . The set of random variables {*„} for - oo < n < oo, together with the probabilistic

...

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### Contents

DISCRETETIME SIGNALS AND SYSTEMS | 6 |

FLOW GRAPH AND MATRIX REPRESENTA | 136 |

DIGITAL FILTER DESIGN TECHNIQUES | 195 |

Copyright | |

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### Common terms and phrases

analog filter applied approximation arithmetic assume autocorrelation autocovariance causal cepstrum chapter circular convolution coefficients complex cepstrum complex logarithm computation consider continuous-time corresponding defined denote depicted in Fig derived determine difference equation digital filter digital signal processing discrete Fourier transform discussed error example exponential expressed FFT algorithm finite finite-duration sequence finite-length sequence FIR system first-order fixed-point fixed-point arithmetic floating-point frequency response H(eia Hilbert transform implementation impulse response input integral inverse length linear phase linear shift-invariant system linear system lowpass filter magnitude minimum-phase multiplication node noise sources noise-to-signal ratio obtain output noise parameters passband periodic sequence periodogram poles and zeros polynomial power spectrum Problem properties quantization random process random variables realization region of convergence representation represented result samples second-order sequence x(n Show shown in Fig spectrum estimate stopband system function techniques theorem truncation two-dimensional unit circle unit-sample response variance z-plane z-transform