000 | 04149nam a22005055i 4500 | ||
---|---|---|---|
001 | 978-3-031-02527-3 | ||
003 | DE-He213 | ||
005 | 20240730163953.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2008 sz | s |||| 0|eng d | ||
020 |
_a9783031025273 _9978-3-031-02527-3 |
||
024 | 7 |
_a10.1007/978-3-031-02527-3 _2doi |
|
050 | 4 | _aT1-995 | |
072 | 7 |
_aTBC _2bicssc |
|
072 | 7 |
_aTEC000000 _2bisacsh |
|
072 | 7 |
_aTBC _2thema |
|
082 | 0 | 4 |
_a620 _223 |
100 | 1 |
_aVaidyanathan, P.P. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _972041 |
|
245 | 1 | 4 |
_aThe Theory of Linear Prediction _h[electronic resource] / _cby P.P. Vaidyanathan. |
250 | _a1st ed. 2008. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2008. |
|
300 |
_aXIV, 183 p. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSynthesis Lectures on Signal Processing, _x1932-1694 |
|
505 | 0 | _aIntroduction -- The Optimal Linear Prediction Problem -- Levinson's Recursion -- Lattice Structures for Linear Prediction -- Autoregressive Modeling -- Prediction Error Bound and Spectral Flatness -- Line Spectral Processes -- Linear Prediction Theory for Vector Processes -- Appendix A: Linear Estimation of Random Variables -- B: Proof of a Property of Autocorrelations -- C: Stability of the Inverse Filter -- Recursion Satisfied by AR Autocorrelations. | |
520 | _aLinear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signal processing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter. Table of Contents: Introduction / The Optimal Linear Prediction Problem / Levinson's Recursion / Lattice Structures for Linear Prediction / Autoregressive Modeling / Prediction Error Bound and Spectral Flatness / Line Spectral Processes / Linear Prediction Theory for Vector Processes / Appendix A: Linear Estimation of Random Variables / B: Proof of a Property of Autocorrelations / C: Stability of the Inverse Filter / Recursion Satisfied by AR Autocorrelations. | ||
650 | 0 |
_aEngineering. _99405 |
|
650 | 0 |
_aElectrical engineering. _981421 |
|
650 | 0 |
_aSignal processing. _94052 |
|
650 | 1 | 4 |
_aTechnology and Engineering. _981422 |
650 | 2 | 4 |
_aElectrical and Electronic Engineering. _981423 |
650 | 2 | 4 |
_aSignal, Speech and Image Processing. _931566 |
710 | 2 |
_aSpringerLink (Online service) _981424 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031013997 |
776 | 0 | 8 |
_iPrinted edition: _z9783031036552 |
830 | 0 |
_aSynthesis Lectures on Signal Processing, _x1932-1694 _981425 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-02527-3 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c85173 _d85173 |