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140122 ||| eng |
| 020 |
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|a 9781461388593
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| 100 |
1 |
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|a Wegman, Edward J.
|e [editor]
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| 245 |
0 |
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|a Topics in Non-Gaussian Signal Processing
|h Elektronische Ressource
|c edited by Edward J. Wegman, Stuart C. Schwartz, John B. Thomas
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| 250 |
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|a 1st ed. 1989
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| 260 |
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|a New York, NY
|b Springer New York
|c 1989, 1989
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| 300 |
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|a XII, 235 p
|b online resource
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| 505 |
0 |
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|a I: Modeling and Characterization -- 1. Bispectral Characterization of Ocean Acoustic Time Series: Nonlinearity and Non-Gaussianity -- 2. Class A Modeling of Ocean Acoustic Noise Processes -- 3. Statistical Characteristics of Ocean Acoustic Noise Processes -- 4. Conditionally Linear and Non-Gaussian Processes -- 5. A Graphical Tool for Distribution and Correlation Analysis of Multiple Time Series -- II: Filtering, Estimation and Regression -- 6. Comments on Structure and Estimation for NonGaussian Linear Processes -- 7.Harmonizable Signal Extraction, Filtering and Sampling -- 8. Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners -- 9. Bayes Least Squares Linear Regression is Asymptotically Full Bayes: Estimation of Spectral Densities -- III: Detection and Signal Extraction -- 10. Signal Detection for Spherically Exchangeable (SE) Stochastic Processes -- 11. Contributions to Non-Gaussian Signal Processing -- 12. Detection of Signals in the Presence of Strong, Signal-Like Interference and Impulse Noise -- 13. On NonGaussian Signal Detection and Channel Capacity -- 14. Detection in a Non-Gaussian Environment: Weak and Fading Narrowband Signals -- 15. Energy Detection in the Ocean Acoustic Environment
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| 653 |
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|a Telecommunication
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| 653 |
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|a Communications Engineering, Networks
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| 700 |
1 |
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|a Schwartz, Stuart C.
|e [editor]
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| 700 |
1 |
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|a Thomas, John B.
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 028 |
5 |
0 |
|a 10.1007/978-1-4613-8859-3
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4613-8859-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621.382
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| 520 |
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|a Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir cuits to one in which digital processing systems, in a combined hardware-software configura. tion, are quite capable of implementing advanced mathematical and statistical procedures. Moreover, as these processing techniques become more sophisticated and powerful, the sharper resolution of the resulting system brings into question the classic distributional assumptions of Gaussianity for both noise and signal processes. This in turn opens the door to a fundamental reexamination of structure and inference methods for non-Gaussian sto chastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Based on the premise that such a fun damental reexamination was timely, in 1981 the Office of Naval Research initiated a research effort in Non-Gaussian Signal Processing under the Selected Research Opportunities Program
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