73 Information Theory 73. 1 Signal Detection General Considerations. Detection of Known Signals. Detection of Parametrized Signals. Detection of Random Signals. Deciding Among Multiple Signals. Detection of Signals in More General Noise Processes.Robust and Nonparametric Detection. Distributed and equential Detection. Detection with Continuous-Time Measurements 3.2 Noise Statistics of noise. Noise power Effect of linear transformations on Autocorrelation and Power Spectral Density.White, Gaussian and pink noise models Thermal noise as gaussian white noise Some Examples. Measuring Thermal Noise. Effective Noise and Antenna Noise. Noise Factor and Noise Ratio. Equivalent Input Noise.Other Electrical Noise. Measurement and Quantization Noise. Coping with Noise 73.3 Stochastic Processes Introduction to Random variables Stochastic Processes Classifications of Stochastic Processes. Stationarity of Processes H. Vincent poor Gaussian and Markov Processes. Examples of Stochastic Processes Linear Filtering of Weakly Stationary Processes. Cross-Correlation of Processes. Coherence. Ergodicity Carl G. Looney 3.4 The Sampling Theore niversity of Nevada The Cardinal Series. Proof of the Sampling Theorem. The Time- Bandwidth Product Sources of Error Generalizations of the J. Marks II Sampling Theorem niversity of washington 73.5 Channel Capacity Sergio Verdu Information Rates Communication Channels Reliable Information Transmission: Shannon,s Theorem Bandwidth and Capacity. Channel Coding Theorems Joy A. Thomas 73.6 Data Compression ntropy. The Huffman Algorithm. Entropy Rate. Arithmetic Thomas m. Cover Quantization and Vector Quantization. Kolmogorov Complexity Stanford University Data Compression in Practice 73.1 Signal Detection H. Vincent poe The field of signal detection and estimation is concerned with the processing of information-bearing signals for the purpose of extracting the information they contain. The applications of this methodology are quite broad, ranging from areas of electrical engineering such as automatic control, digital communications, image processing, and remote sensing, into other engineering disciplines and the physical, biological, and social c 2000 by CRC Press LLC© 2000 by CRC Press LLC 73 Information Theory 73.1 Signal Detection General Considerations • Detection of Known Signals • Detection of Parametrized Signals • Detection of Random Signals • Deciding Among Multiple Signals • Detection of Signals in More General Noise Processes • Robust and Nonparametric Detection • Distributed and Sequential Detection • Detection with Continuous-Time Measurements 73.2 Noise Statistics of Noise • Noise Power • Effect of Linear Transformations on Autocorrelation and Power Spectral Density • White, Gaussian, and Pink Noise Models • Thermal Noise as Gaussian White Noise • Some Examples • Measuring Thermal Noise • Effective Noise and Antenna Noise • Noise Factor and Noise Ratio • Equivalent Input Noise • Other Electrical Noise • Measurement and Quantization Noise • Coping with Noise 73.3 Stochastic Processes Introduction to Random Variables • Stochastic Processes • Classifications of Stochastic Processes • Stationarity of Processes • Gaussian and Markov Processes • Examples of Stochastic Processes • Linear Filtering of Weakly Stationary Processes • Cross-Correlation of Processes • Coherence • Ergodicity 73.4 The Sampling Theorem The Cardinal Series • Proof of the Sampling Theorem • The Time￾Bandwidth Product • Sources of Error • Generalizations of the Sampling Theorem 73.5 Channel Capacity Information Rates • Communication Channels • Reliable Information Transmission: Shannon’s Theorem • Bandwidth and Capacity • Channel Coding Theorems 73.6 Data Compression Entropy • The Huffman Algorithm • Entropy Rate • Arithmetic Coding • Lempel–Ziv Coding • Rate Distortion Theory • Quantization and Vector Quantization • Kolmogorov Complexity • Data Compression in Practice 73.1 Signal Detection H. Vincent Poor The field of signal detection and estimation is concerned with the processing of information-bearing signals for the purpose of extracting the information they contain. The applications of this methodology are quite broad, ranging from areas of electrical engineering such as automatic control, digital communications, image processing, and remote sensing, into other engineering disciplines and the physical, biological, and social sciences. H. Vincent Poor Princeton University Carl G. Looney University of Nevada R. J. Marks II University of Washington Sergio Verdú Princeton University Joy A. Thomas IBM Thomas M. Cover Stanford University