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 362
... signal and noise is denoted W ( t ) : W ( t ) = X ( t ) + N ( t ) ( 9.2-1 ) The system is assumed to be linear and time - invariant with a real impulse response h ( t ) and a transfer function H ( w ) . The output of the system is denoted Y ...
... signal and noise is denoted W ( t ) : W ( t ) = X ( t ) + N ( t ) ( 9.2-1 ) The system is assumed to be linear and time - invariant with a real impulse response h ( t ) and a transfer function H ( w ) . The output of the system is denoted Y ...
Page 367
... ( t ) . Plot hopt ( t ) . ( c ) Is there a value of t , for which the filter is causal ? If so , find it . ( d ) ... signal 367 CHAPTER 9 : Optimum Linear Systems x ( t ) = u ( t ) [ e ̄W2 ! — e - αW2 ! ] - е if a 1 is a real constant . 9.1-3 .
... ( t ) . Plot hopt ( t ) . ( c ) Is there a value of t , for which the filter is causal ? If so , find it . ( d ) ... signal 367 CHAPTER 9 : Optimum Linear Systems x ( t ) = u ( t ) [ e ̄W2 ! — e - αW2 ! ] - е if a 1 is a real constant . 9.1-3 .
Page 375
Peyton Peebles. 9.2-11 . A random signal X ( 1 ) plus uncorrelated noise N ( t ) , having respective power spectrums and Sxx ( w ) = 2PxxWx / ( W } + w2 ) INN ( w ) = 4PNN W } / { W } + w2 ) 2 375 CHAPTER 9 : Optimum Linear Systems is ...
Peyton Peebles. 9.2-11 . A random signal X ( 1 ) plus uncorrelated noise N ( t ) , having respective power spectrums and Sxx ( w ) = 2PxxWx / ( W } + w2 ) INN ( w ) = 4PNN W } / { W } + w2 ) 2 375 CHAPTER 9 : Optimum Linear Systems is ...
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