Probabilities, Random Variables, and Random Processes: Digital and Analog |
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Page 234
... Waveform | v1 ( t ) v1 ( t1 ) - t = 0 | v2 ( t ) Waveform v2 ( 11 ) t = 0 Waveform Jun ( t ) t = 0 un ( 1 ) Im 12 Im 12 Un ( tm ) Figure 6.1 Typical members of a random process . The random process v ( t ) is said to consist of an ...
... Waveform | v1 ( t ) v1 ( t1 ) - t = 0 | v2 ( t ) Waveform v2 ( 11 ) t = 0 Waveform Jun ( t ) t = 0 un ( 1 ) Im 12 Im 12 Un ( tm ) Figure 6.1 Typical members of a random process . The random process v ( t ) is said to consist of an ...
Page 285
... waveform generated is given a phase shift to the left uniformly distributed from 0 to b seconds . " The sample section of the typical member ... member waveform must change amplitude ENSEMBLE AND TIME AVERAGE AUTOCORRELATION FUNCTIONS 285.
... waveform generated is given a phase shift to the left uniformly distributed from 0 to b seconds . " The sample section of the typical member ... member waveform must change amplitude ENSEMBLE AND TIME AVERAGE AUTOCORRELATION FUNCTIONS 285.
Page 337
... member waveform of a random process f ( n ) and we have another section of a discrete member waveform from a process g ( n ) also extending from M to + N , then if f ( n ) and g ( n ) are jointly ergodic , the formulas for estimations ...
... member waveform of a random process f ( n ) and we have another section of a discrete member waveform from a process g ( n ) also extending from M to + N , then if f ( n ) and g ( n ) are jointly ergodic , the formulas for estimations ...
Contents
1 | 3 |
5 | 54 |
General Formulation and Solution of Problems | 69 |
Copyright | |
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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