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 193
... derived one class of frequency - sampling structures , based on sampling the frequency response of an FIR system at ... derive that result . To derive this relation we begin by expressing ATab in Problems 193.
... derived one class of frequency - sampling structures , based on sampling the frequency response of an FIR system at ... derive that result . To derive this relation we begin by expressing ATab in Problems 193.
Page 195
... derived from an analog signal by means of periodic sampling . The specifications for both analog and digital filters are often ( but not always ) given in the frequency domain , as , for example , in the case of frequency selective ...
... derived from an analog signal by means of periodic sampling . The specifications for both analog and digital filters are often ( but not always ) given in the frequency domain , as , for example , in the case of frequency selective ...
Page 403
... Derive an expression for the autocorrelation sequence ( 1 , n2 ) of the output . ( c ) Show that for large n the formulas derived in parts ( a ) and ( b ) approach the results for stationary inputs . ( d ) Assume that h ( n ) = au ( n ) ...
... Derive an expression for the autocorrelation sequence ( 1 , n2 ) of the output . ( c ) Show that for large n the formulas derived in parts ( a ) and ( b ) approach the results for stationary inputs . ( d ) Assume that h ( n ) = au ( n ) ...
Contents
INTRODUCTION | 1 |
THE ZTRANSFORM | 45 |
FLOW GRAPH AND MATRIX REPRESENTA | 136 |
Copyright | |
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analog filter applied approximation arithmetic assume 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 discrete-time discussed error example expressed FFT algorithm finite finite-duration sequence fixed-point floating-point flow graph frequency response 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 theorem truncation two-dimensional unit circle unit-sample response variance window x₁(n z-plane z-transform