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 231
... frequency . However , in many applications T , can be considered constant ( with respect to w ) because its varia- tion with frequency over a frequency band comparable to that of the desired signal being received is often small ...
... frequency . However , in many applications T , can be considered constant ( with respect to w ) because its varia- tion with frequency over a frequency band comparable to that of the desired signal being received is often small ...
Page 273
... frequency with time during the pulse's dura- tion T. The nominal frequency is wo ( rad / s ) . The matched filter for white noise has the impulse response of ( 9.1-15 ) which , for t , = 0 , is hopi ( t ) = K rect ( t / T ) exp ( jæ ...
... frequency with time during the pulse's dura- tion T. The nominal frequency is wo ( rad / s ) . The matched filter for white noise has the impulse response of ( 9.1-15 ) which , for t , = 0 , is hopi ( t ) = K rect ( t / T ) exp ( jæ ...
Page 342
... frequency allocations , 275-276 , 280 frequency modulation ( FM ) , 280–284 three - symbol , 33 Commutative law of sets , 7 Complement of set , 6 Complex random process , 160-161 ( See also Random process ) Complex random variable , 133 ...
... frequency allocations , 275-276 , 280 frequency modulation ( FM ) , 280–284 three - symbol , 33 Commutative law of sets , 7 Complement of set , 6 Complex random process , 160-161 ( See also Random process ) Complex random variable , 133 ...
Other editions - View all
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