Probability, Random Variables, and Random Signal PrinciplesThere are now 134 examples and nearly 900 homework problems; and other topics expanded or added include discussion of probability as a relative frequency, permutations, combinations, transformations of random variables, ergodicity of random processes, laws of large numbers, estimation, various inequalities, properties of impulses, and chapter-end summaries. This new material will prove most useful for students concerned with modern digital systems."--BOOK JACKET. |
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Page 72
... zero is decided . If it is given that a binary zero is truly being received , find the probabilities that ( a ) a binary one ( mistake ) will be decided , and ( b ) a binary zero is decided ( correct decision ) . 2.4-11 . A gaussian ...
... zero is decided . If it is given that a binary zero is truly being received , find the probabilities that ( a ) a binary one ( mistake ) will be decided , and ( b ) a binary zero is decided ( correct decision ) . 2.4-11 . A gaussian ...
Page 191
... zero . We next show conditions under which the variance is zero . Begin with ZAX X = 1 - lim E [ ( Ax − Ãx ) 2 ] = E [ X ( 1 ) - X ] dt T → ∞ 2T - [ X ( 1 ) – X ] [ X ( t1 ) – X ] dt dt } ( 6.2-35 ) = E { Fim ( 7 ) T , L GLL = lim 2T ...
... zero . We next show conditions under which the variance is zero . Begin with ZAX X = 1 - lim E [ ( Ax − Ãx ) 2 ] = E [ X ( 1 ) - X ] dt T → ∞ 2T - [ X ( 1 ) – X ] [ X ( t1 ) – X ] dt dt } ( 6.2-35 ) = E { Fim ( 7 ) T , L GLL = lim 2T ...
Page 406
... zero - mean process , and ( b ) the power spectrum is Απ 2 SAM ( W ) = - [ 8 ( w — wo ) + 8 ( w + wo ) ] - + { [ S xx ( w− wo ) + S xx ( w + wo ) ] where xx ( ) is the power spectrum of X ( 1 ) . 10.1-2 . Define transmitter efficiency ...
... zero - mean process , and ( b ) the power spectrum is Απ 2 SAM ( W ) = - [ 8 ( w — wo ) + 8 ( w + wo ) ] - + { [ S xx ( w− wo ) + S xx ( w + wo ) ] where xx ( ) is the power spectrum of X ( 1 ) . 10.1-2 . Define transmitter efficiency ...
Contents
Venn Diagram Equality and Difference Union | 7 |
Joint Probability Conditional Probability Total | 18 |
The Random Variable | 107 |
Copyright | |
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Probability, Random Variables, and Random Signal Principles Peyton Z. Peebles No preview available - 2001 |
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amplitude applied assume autocorrelation function available power gain average power band-limited bandpass bandwidth CHAPTER characteristic function cos(wpt covariance cross-correlation cross-correlation function cross-power defined denoted discrete random variables discrete-time DT system ergodic event example expected value Fourier transform frequency fx(x fy(y gaussian random variables given impulse response independent random variables integral joint density function jointly wide-sense stationary k₁ Linear Systems lowpass mean value Multiple Random Variables noise figure noise power noise temperature Peebles power density spectrum power spectrum Problem properties random process random process X(t Random Signal Principles random variables X1 real constants resistor Rxy(t Ryy(t sample function sample space sequence signal x(t spectral stationary process statistically independent statistically independent random Systems with Random t₁ transfer function uncorrelated variance voltage W₁ W₂ waveform white noise wide-sense stationary X₁ xx(w Y₁ Y₂ zero zero-mean