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. |
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applied assumed autocorrelation function average bandwidth becomes Book called Chapter complex conditional consider continuous correlation corresponds cross-correlation function defined denoted density function determine discrete distribution function effective elements equal event Example expected experiment expression fact Figure filter Find Fourier transform frequency Fx(x gaussian given illustrated impulse response input integral joint joint density jointly linear linear system mean value measure moments noise noise figure noise power noise temperature obtain operating output positive possible power density spectrum power spectrum probability Problem properties prove random process X(t random variables real constants represents resistor respectively result Rxx(t Rxy(t sample function sample space satisfies Show signal sketch statistically independent Sxx(w t₁ t₂ theory transfer function uncorrelated variance voltage waveform white noise wide-sense stationary X and Y X₁