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 197
... real function having a Fourier transform H ( w ) . Show that the power spectrum of Y ( t ) is given by Syy ( @ ) = Sxx ( w ) | H ( w ) | 2 2 7-30 A deterministic signal A cos ( wt ) , where A and wo are real constants , is added to a ...
... real function having a Fourier transform H ( w ) . Show that the power spectrum of Y ( t ) is given by Syy ( @ ) = Sxx ( w ) | H ( w ) | 2 2 7-30 A deterministic signal A cos ( wt ) , where A and wo are real constants , is added to a ...
Page 270
... real constant . 9-23 Work Problem 9-21 for the noise defined in Problem 9-22 ... constants . 0 9-26 A deterministic signal x ( t ) = A cos ( wt ) and white ... real constants . What value of W will cause the ratio of output average signal ...
... real constant . 9-23 Work Problem 9-21 for the noise defined in Problem 9-22 ... constants . 0 9-26 A deterministic signal x ( t ) = A cos ( wt ) and white ... real constants . What value of W will cause the ratio of output average signal ...
Page 326
... real constants and Sa ( · ) is the sampling function defined by ( E - 3 ) . D - 13 The transfer function H ( w ) of a lowpass filter can be approximated by N Σ Ko + 2 Σ K , cos ( ηπω / W ) H ( ∞ ) = 0 n = 1 n -W < @ < W elsewhere Here ...
... real constants and Sa ( · ) is the sampling function defined by ( E - 3 ) . D - 13 The transfer function H ( w ) of a lowpass filter can be approximated by N Σ Ko + 2 Σ K , cos ( ηπω / W ) H ( ∞ ) = 0 n = 1 n -W < @ < W elsewhere Here ...
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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