Principles of Random Signal Analysis and Low Noise Design: The Power Spectral Density and its Applications (Google eBook)

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Contents
1  
3  
3 The Power Spectral Density  59 
4 Power Spectral Density Analysis  92 
5 Power Spectral Density of Standard Random ProcessesPart 1  138 
6 Power Spectral Density of Standard Random ProcessesPart 2  179 
7 Memoryless Transformations of Random Processes  206 
8 Linear System Theory  229 
9 Principles of Low Noise Electronic Design  256 
Notation  300 
302  
307  
Common terms and phrases
1/f noise absolutely continuous amplifier amplitude analysis associated autocorrelation function average power bounded variation Champeney circuit common emitter Consider consistent countable cross power spectral defined deﬁned according deﬁnition denoted Dirichlet point disjoint signaling electronic equation evaluated example exists ﬁnite ﬁrst ﬁxed following theorem Fourier series Fourier transform Frequency Hz function f gG(t given in Appendix Hence ieZ+ illustrated in Figure implies impulsive components independent infinite inﬁnite interval input equivalent noise Laplace transform Lebesgue integrable measure memoryless node noise sources output signal periodic signal piecewise continuous piecewise smooth power spectral density PROOF OF THEOREM Proof The proof pulse function quadrature amplitude modulation random walk real numbers relationship required result result follows sample shot noise shot noise process shown in Figure signaling intervals signaling random process signaling set signaling waveforms sinusoidal components spectral density function summation Taco transfer function uncountable voltage yields zero mean
Popular passages
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Page 302  Proceedings of the 14th International Conference on Noise in Physical Systems and 1/f Fluctuations, Leuven, Belgium, Eds.
Page 305  A Theory of 1/f current noise based on a random walk model' , Journal of Statistical Physics, 15, pp.
Page 304  Papoulis, A. and Pillai, SU (2002). Probability, Random Variables, and Stochastic Processes, 4th ed.