Probability, Random Variables, and Random Signal PrinciplesToday, any well-designed electrical engineering curriculum must train engineers to account for noise and random signals in systems. The best approach is to emphasize fundamental principles since systems can vary greatly. Professor Peebles's book specifically has this emphasis, offering clear and concise coverage of the theories of probability, random variables, and random signals, including the response of linear networks to random waveforms. By careful organization, the book allows learning to flow naturally from the most elementary to the most advanced subjects. Time domain descriptions of the concepts are first introduced, followed by a thorough description of random signals using frequency domain. Practical applications are not forgotten, and the book includes discussions of practical noises (noise figures and noise temperatures) and an entire special chapter on applications of the theory. Another chapter is devoted to optimum networks when noise is present (matched filters and Wiener filters). This third edition differs from earlier editions mainly in making the book more useful for classroom use. Beside the addition of new topics (Poisson random processes, measurement of power spectra, and computer generation of random variables), the main change involves adding many new end-of-chapter exercises (180 were added for a total of over 800 exercises). The new exercises are all clearly identified for instructors who have used the previous edition. |
From inside the book
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Page 187
... noise . White and Colored Noise A sample function n ( t ) of a wide - sense stationary noise random process N ( t ) is called white noise if the power density spectrum of N ( t ) is a constant at all fre- quencies . Thus , we define SNN ...
... noise . White and Colored Noise A sample function n ( t ) of a wide - sense stationary noise random process N ( t ) is called white noise if the power density spectrum of N ( t ) is a constant at all fre- quencies . Thus , we define SNN ...
Page 260
... noise . In general , this noise has been assumed nonwhite ; that is , colored . Because of these facts , an optimum filter given by ( 9.1-13 ) is called a matched filter for colored noise . Hopt ( ) is ... Noise / Matched Filter White Noise.
... noise . In general , this noise has been assumed nonwhite ; that is , colored . Because of these facts , an optimum filter given by ( 9.1-13 ) is called a matched filter for colored noise . Hopt ( ) is ... Noise / Matched Filter White Noise.
Page 268
... noise N ( t ) are applied to the network of Figure P9-5 . We select | H , ( w ) | 2 = 1 / 8NN ( w ) so that the noise N , ( t ) is white . We also make H2 ( w ) a matched filter for the signal x1 ( t ) in the white noise N1 ( t ) . Show ...
... noise N ( t ) are applied to the network of Figure P9-5 . We select | H , ( w ) | 2 = 1 / 8NN ( w ) so that the noise N , ( t ) is white . We also make H2 ( w ) a matched filter for the signal x1 ( t ) in the white noise N1 ( t ) . Show ...
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Probability, Random Variables, and Random Signal Principles Peyton Z. Peebles,Bertram Emil Shi No preview available - 2015 |
Common terms and phrases
Advanced Book Program applied assumed autocorrelation function available power gain average power B₁ B₂ bandpass bandwidth characteristic function conditional density correlation covariance cross-correlation cross-correlation function denoted discrete random variable distribution function Example expected value Find and sketch find the probability Fourier transform frequency Fx(x fy(y gaussian random variable given illustrate impulse response integral joint density function jointly wide-sense stationary lowpass matched filter mean value noise figure noise power noise temperature output noise power Peebles power density spectrum power spectrum Problem properties random process X(t random signal random vari real constants real number resistor Rxx(t Rxy(t Ryy(t S₁ sample function sample space shown in Figure signal x(t SNN(W stationary process statistically independent Sxx(w Sxy(w t₁ t₂ transfer function uncorrelated variance voltage W₁ waveform white noise wide-sense stationary X₁ Y₁ Y₂ zero-mean