Probabilities, Random Variables, and Random Processes: Digital and Analog |
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Page 357
... signal and the output signal - and - noise autocorrela- tion function . ( b ) Find the input and output signal - to - noise ratios and the noise figure . SOLUTION ( a ) To find the output signal , g ( k ) = h ( k ) * x ( k ) = { 0.5 , 1 ...
... signal and the output signal - and - noise autocorrela- tion function . ( b ) Find the input and output signal - to - noise ratios and the noise figure . SOLUTION ( a ) To find the output signal , g ( k ) = h ( k ) * x ( k ) = { 0.5 , 1 ...
Page 440
... signal and a random noise wave- form . Case 2 gives the results when the signal is deterministic . Case 3 describes a purely random signal input . One of our main applications in practice of a random signal plus random noise input is ...
... signal and a random noise wave- form . Case 2 gives the results when the signal is deterministic . Case 3 describes a purely random signal input . One of our main applications in practice of a random signal plus random noise input is ...
Page 472
... signals . lems : Initially we will simplistically list some typical signal processing prob- 1. The recovery of a " known signal " from data . 2. The interpretation of a signal from a pattern of signals . 3. The investigation of ...
... signals . lems : Initially we will simplistically list some typical signal processing prob- 1. The recovery of a " known signal " from data . 2. The interpretation of a signal from a pattern of signals . 3. The investigation of ...
Contents
1 | 3 |
5 | 54 |
General Formulation and Solution of Problems | 69 |
Copyright | |
12 other sections not shown
Common terms and phrases
A₁ autocorrelation function average axiom B₁ balls Chapter convolution correlation integrals cross-correlation cross-correlation function cumulative distribution function dadß defined definition delta function denoted derivation deterministic discrete Fourier transform discrete probability theory discrete random process DRILL SET ensemble ergodic evaluate event space Example finite first-order stationary formula fundamental theorem fxy(a fy(B fz(y given impulse response joint density function joint mass function Laplace transform linear system M₁ member waveform notation obtained otherwise output periodic function periodic waveform permutations plotted in Figure points power spectral density probability measure probability theory problem properties pulse px(a random input random phenomenon range Rxx(T sample description space sampled values second-order stationary set theory shown in Figure shown plotted signal sketch SOLUTION solved statistics Sxx(w Ticket Pays two-sided Laplace transform two-sided z transform typical member X₁ z transform zero-mean