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
... Antenna as a Noise Source In practice , all antennas produce noise at their output because of reception of electromagnetic radiation from noise sources external to the antenna . † The amount of available noise power dNas in an ...
... Antenna as a Noise Source In practice , all antennas produce noise at their output because of reception of electromagnetic radiation from noise sources external to the antenna . † The amount of available noise power dNas in an ...
Page 255
... antenna with effective noise temperature T 90 K is connected to an attenuator that is at a physical temperature of 270 K and has a loss of 1.9 . What is the effective spot noise temperature of the antenna - attenuator cascade if its ...
... antenna with effective noise temperature T 90 K is connected to an attenuator that is at a physical temperature of 270 K and has a loss of 1.9 . What is the effective spot noise temperature of the antenna - attenuator cascade if its ...
Page 256
... antenna is the source ? 8-108 An antenna with average noise temperature 120 K connects to a receiver through an impedance - matched attenuator having a loss of 1.5 and physical tem- perature 75 K. For the overall system Ty , = 500 K ...
... antenna is the source ? 8-108 An antenna with average noise temperature 120 K connects to a receiver through an impedance - matched attenuator having a loss of 1.5 and physical tem- perature 75 K. For the overall system Ty , = 500 K ...
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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