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Titulos:
Advanced Digital Signal Processing and Noise Reduction
Idiomas:
eng
ISBN:
1-280-33984-5; 9786610339846; 0-470-09496-6; 0-470-09495-8
Lugar de Edición:
Hoboken :
Editor:
Wiley,
Fecha de Edición:
2006.
Edición #:
3rd ed.
Notas #:
Description based upon print version of record.
Notas Formateada:
Advanced Digital Signal Processing and Noise Reduction; Contents; Preface; Symbols; Abbreviations; 1 Introduction; 1.1 Signals and Information; 1.2 Signal Processing Methods; 1.2.1 Transform-based Signal Processing; 1.2.2 Model-based Signal Processing; 1.2.3 Bayesian Signal Processing; 1.2.4 Neural Networks; 1.3 Applications of Digital Signal Processing; 1.3.1 Adaptive Noise Cancellation; 1.3.2 Adaptive Noise Reduction; 1.3.3 Blind Channel Equalisation; 1.3.4 Signal Classification and Pattern Recognition; 1.3.5 Linear Prediction Modelling of Speech; 1.3.6 Digital Coding of Audio Signals; 1.3.7 Detection of Signals in Noise1.3.8 Directional Reception of Waves: Beam-forming; 1.3.9 Dolby Noise Reduction; 1.3.10 Radar Signal Processing: Doppler Frequency Shift; 1.4 Sampling and Analogue-to-digital Conversion; 1.4.1 Sampling and Reconstruction of Analogue Signals; 1.4.2 Quantisation; Bibliography; 2 Noise and Distortion; 2.1 Introduction; 2.2 White Noise; 2.2.1 Band-limited White Noise; 2.3 Coloured Noise; 2.4 Impulsive Noise; 2.5 Transient Noise Pulses; 2.6 Thermal Noise; 2.7 Shot Noise; 2.8 Electromagnetic Noise; 2.9 Channel Distortions; 2.10 Echo and Multipath Reflections; 2.11 Modelling Noise2.11.1 Additive White Gaussian Noise Model; 2.11.2 Hidden Markov Model for Noise; Bibliography; 3 Probability and Information Models; 3.1 Introduction; 3.2 Random Signals; 3.2.1 Random and Stochastic Processes; 3.2.2 The Space of a Random Process; 3.3 Probability Models; 3.3.1 Probability and Random Variables; 3.3.2 Probability Mass Function; 3.3.3 Probability Density Function; 3.3.4 Probability Density Functions of Random Processes; 3.4 Information Models; 3.4.1 Entropy; 3.4.2 Mutual Information; 3.4.3 Entropy Coding; 3.5 Stationary and Nonstationary Random Processes; 3.5.1 Strict-sense Stationary Processes3.5.2 Wide-sense Stationary Processes; 3.5.3 Nonstationary Processes; 3.6 Statistics (Expected Values) of a Random Process; 3.6.1 The Mean Value; 3.6.2 Autocorrelation; 3.6.3 Autocovariance; 3.6.4 Power Spectral Density; 3.6.5 Joint Statistical Averages of Two Random Processes; 3.6.6 Cross-correlation and Cross-covariance; 3.6.7 Cross-power Spectral Density and Coherence; 3.6.8 Ergodic Processes and Time-averaged Statistics; 3.6.9 Mean-ergodic Processes; 3.6.10 Correlation-ergodic Processes; 3.7 Some Useful Classes of Random Processes; 3.7.1 Gaussian (Normal) Process3.7.2 Multivariate Gaussian Process; 3.7.3 Mixture Gaussian Process; 3.7.4 A Binary-state Gaussian Process; 3.7.5 Poisson Process; 3.7.6 Shot Noise; 3.7.7 Poisson-Gaussian Model for Clutters and Impulsive Noise; 3.7.8 Markov Processes; 3.7.9 Markov Chain Processes; 3.7.10 Gamma Probability Distribution; 3.7.11 Rayleigh Probability Distribution; 3.7.12 Laplacian Probability Distribution; 3.8 Transformation of a Random Process; 3.8.1 Monotonic Transformation of Random Processes; 3.8.2 Many-to-one Mapping of Random Signals; 3.9 Summary; Bibliography; 4 Bayesian Inference
Nota de contenido:
Signal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of signal processing deals with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and hence, noise reduction and the removal of channel distorti
Palabras clave:
Digital filters (Mathematics).; Electronic noise.; Signal processing.; Electrical & Computer Engineering; Engineering & Applied Sciences; Telecommunications

Leader:
nam
Campo 008:
130418s2006||||||| s|||||||||||eng|d
Campo 020:
^a1-280-33984-5
Campo 020:
^a9786610339846
Campo 020:
^a0-470-09496-6
Campo 020:
^a0-470-09495-8
Campo 035:
^a(CKB)1000000000356507
Campo 035:
^a(EBL)244886
Campo 035:
^a(OCoLC)71517623
Campo 035:
^a(SSID)ssj0000097671
Campo 035:
^a(PQKBManifestationID)11130687
Campo 035:
^a(PQKBTitleCode)TC0000097671
Campo 035:
^a(PQKBWorkID)10120072
Campo 035:
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Campo 035:
^a(EXLCZ)991000000000356507
Campo 040:
^aAU-PeEL^beng^cAU-PeEL^dAU-PeEL
Campo 041:
^aeng
Campo 100:
1 ^aVaseghi, Saeed V.
Campo 245:
10^aAdvanced Digital Signal Processing and Noise Reduction^h[electronic resource].
Campo 246:
Campo 250:
^a3rd ed.
Campo 260:
^aHoboken :^bWiley,^c2006.
Campo 300:
^a1 online resource (481 p.)
Campo 500:
^aDescription based upon print version of record.
Campo 505:
0 ^aAdvanced Digital Signal Processing and Noise Reduction; Contents; Preface; Symbols; Abbreviations; 1 Introduction; 1.1 Signals and Information; 1.2 Signal Processing Methods; 1.2.1 Transform-based Signal Processing; 1.2.2 Model-based Signal Processing; 1.2.3 Bayesian Signal Processing; 1.2.4 Neural Networks; 1.3 Applications of Digital Signal Processing; 1.3.1 Adaptive Noise Cancellation; 1.3.2 Adaptive Noise Reduction; 1.3.3 Blind Channel Equalisation; 1.3.4 Signal Classification and Pattern Recognition; 1.3.5 Linear Prediction Modelling of Speech; 1.3.6 Digital Coding of Audio Signals
Campo 505:
8 ^a1.3.7 Detection of Signals in Noise1.3.8 Directional Reception of Waves: Beam-forming; 1.3.9 Dolby Noise Reduction; 1.3.10 Radar Signal Processing: Doppler Frequency Shift; 1.4 Sampling and Analogue-to-digital Conversion; 1.4.1 Sampling and Reconstruction of Analogue Signals; 1.4.2 Quantisation; Bibliography; 2 Noise and Distortion; 2.1 Introduction; 2.2 White Noise; 2.2.1 Band-limited White Noise; 2.3 Coloured Noise; 2.4 Impulsive Noise; 2.5 Transient Noise Pulses; 2.6 Thermal Noise; 2.7 Shot Noise; 2.8 Electromagnetic Noise; 2.9 Channel Distortions; 2.10 Echo and Multipath Reflections
Campo 505:
8 ^a2.11 Modelling Noise2.11.1 Additive White Gaussian Noise Model; 2.11.2 Hidden Markov Model for Noise; Bibliography; 3 Probability and Information Models; 3.1 Introduction; 3.2 Random Signals; 3.2.1 Random and Stochastic Processes; 3.2.2 The Space of a Random Process; 3.3 Probability Models; 3.3.1 Probability and Random Variables; 3.3.2 Probability Mass Function; 3.3.3 Probability Density Function; 3.3.4 Probability Density Functions of Random Processes; 3.4 Information Models; 3.4.1 Entropy; 3.4.2 Mutual Information; 3.4.3 Entropy Coding; 3.5 Stationary and Nonstationary Random Processes
Campo 505:
8 ^a3.5.1 Strict-sense Stationary Processes3.5.2 Wide-sense Stationary Processes; 3.5.3 Nonstationary Processes; 3.6 Statistics (Expected Values) of a Random Process; 3.6.1 The Mean Value; 3.6.2 Autocorrelation; 3.6.3 Autocovariance; 3.6.4 Power Spectral Density; 3.6.5 Joint Statistical Averages of Two Random Processes; 3.6.6 Cross-correlation and Cross-covariance; 3.6.7 Cross-power Spectral Density and Coherence; 3.6.8 Ergodic Processes and Time-averaged Statistics; 3.6.9 Mean-ergodic Processes; 3.6.10 Correlation-ergodic Processes; 3.7 Some Useful Classes of Random Processes
Campo 505:
8 ^a3.7.1 Gaussian (Normal) Process3.7.2 Multivariate Gaussian Process; 3.7.3 Mixture Gaussian Process; 3.7.4 A Binary-state Gaussian Process; 3.7.5 Poisson Process; 3.7.6 Shot Noise; 3.7.7 Poisson-Gaussian Model for Clutters and Impulsive Noise; 3.7.8 Markov Processes; 3.7.9 Markov Chain Processes; 3.7.10 Gamma Probability Distribution; 3.7.11 Rayleigh Probability Distribution; 3.7.12 Laplacian Probability Distribution; 3.8 Transformation of a Random Process; 3.8.1 Monotonic Transformation of Random Processes; 3.8.2 Many-to-one Mapping of Random Signals; 3.9 Summary; Bibliography
Campo 505:
8 ^a4 Bayesian Inference
Campo 520:
^aSignal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of signal processing deals with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and hence, noise reduction and the removal of channel distorti
Campo 650:
4^aDigital filters (Mathematics).
Campo 650:
4^aElectronic noise.
Campo 650:
4^aSignal processing.
Campo 650:
7^aElectrical & Computer Engineering^2HILCC
Campo 650:
7^aEngineering & Applied Sciences^2HILCC
Campo 650:
7^aTelecommunications^2HILCC
Proveniencia:
^aUniversidad de San Andrés - Biblioteca Max Von Buch
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Institucion:
Universidad de San Andrés
Dependencia:
Biblioteca Max Von Buch

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