| 000 | 07270nam a2201153 i 4500 | ||
|---|---|---|---|
| 001 | 5238083 | ||
| 003 | IEEE | ||
| 005 | 20230927112344.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151221s2005 njua ob 001 eng d | ||
| 020 |
_a9780471732679 _qebook |
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| 020 |
_z9780471236320 _qprint |
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| 020 |
_z0471236322 _qpaper |
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| 020 |
_z0471732664 _qelectronic |
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| 020 |
_z9780471732662 _qelectronic |
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| 020 |
_z0471732672 _qelectronic |
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| 024 | 7 |
_a10.1002/0471732672 _2doi |
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| 035 | _a(CaBNVSL)mat05238083 | ||
| 035 | _a(IDAMS)0b00006481096014 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 082 | 0 | 4 | _a621.382/2 |
| 100 | 1 |
_aCandy, J. V., _eauthor. |
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| 245 | 1 | 0 |
_aModel-based signal processing / _cJames V. Candy. |
| 264 | 1 |
_aHoboken, New Jersey : _bIEEE Press, _cc2006. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2005] |
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| 300 |
_a1 PDF (xxi, 677 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aAdaptive and learning systems for signal processing, communications and control series ; _v36 |
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| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aPreface. -- Acknowledgments. -- 1. Introduction. -- 2. Discrete Random Signals ans Systems. -- 3. Estimation Theory. -- 4. AR, MA, ARMAX, Lattice, Exponential, Wave Model-Based Processors. -- 5. Linear State-Space Model-Based Processors. -- 6. Nonlinear State-Space Model-Based Processors. -- 7. Adaptive AR, MA, ARMAX, Exponential Model-Based Processors. -- 8. Adaptive State-Space Model-Based Processors. -- 9. Applied Physics-Based Processors. -- Appendix A: Probability and Statistics Overview. -- Appendix B: Sequential MBP and UD-Factorization. -- Appendix C: SSpack_PC: An Interactive Model-Based Processing Software Package. -- Index. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aA unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing. The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems. * Unified treatment of well-known signal processing models including physics-based model sets * Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis * Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed * References lead to more in-depth coverage of specialized topics * Problem sets test readers' knowledge and help them put their new skills into practice The author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/21/2015. | ||
| 650 | 0 |
_aSignal processing _xDigital techniques _vTextbooks. |
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| 655 | 0 | _aElectronic books. | |
| 695 | _aAdaptation model | ||
| 695 | _aAdaptive control | ||
| 695 | _aAdaptive filters | ||
| 695 | _aAlgorithm design and analysis | ||
| 695 | _aAnalytical models | ||
| 695 | _aApproximation algorithms | ||
| 695 | _aArtificial neural networks | ||
| 695 | _aAtmospheric modeling | ||
| 695 | _aComputational modeling | ||
| 695 | _aConvergence | ||
| 695 | _aCooperative systems | ||
| 695 | _aCoordinate measuring machines | ||
| 695 | _aCovariance matrix | ||
| 695 | _aData mining | ||
| 695 | _aData models | ||
| 695 | _aDistribution functions | ||
| 695 | _aEquations | ||
| 695 | _aEstimation | ||
| 695 | _aEstimation theory | ||
| 695 | _aFourier series | ||
| 695 | _aFrequency domain analysis | ||
| 695 | _aIndexes | ||
| 695 | _aJacobian matrices | ||
| 695 | _aJoints | ||
| 695 | _aKalman filters | ||
| 695 | _aLaplace equations | ||
| 695 | _aLattices | ||
| 695 | _aMathematical model | ||
| 695 | _aMeasurement uncertainty | ||
| 695 | _aMeasurement units | ||
| 695 | _aNoise measurement | ||
| 695 | _aPollution measurement | ||
| 695 | _aPrediction algorithms | ||
| 695 | _aPredictive models | ||
| 695 | _aProbabilistic logic | ||
| 695 | _aProgram processors | ||
| 695 | _aRadar cross section | ||
| 695 | _aRadar tracking | ||
| 695 | _aRandom variables | ||
| 695 | _aSignal processing | ||
| 695 | _aSignal processing algorithms | ||
| 695 | _aSignal to noise ratio | ||
| 695 | _aStochastic processes | ||
| 695 | _aTaylor series | ||
| 695 | _aTechnological innovation | ||
| 695 | _aTime measurement | ||
| 695 | _aTrajectory | ||
| 695 | _aTransforms | ||
| 695 | _aVectors | ||
| 695 | _aVehicle dynamics | ||
| 695 | _aVehicles | ||
| 695 | _aYttrium | ||
| 710 | 2 |
_aJohn Wiley & Sons, _epublisher. |
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| 710 | 2 |
_aIEEE Xplore (Online service), _edistributor. |
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| 776 | 0 | 8 |
_iPrint version: _z9780471236320 |
| 830 | 0 |
_aAdaptive and learning systems for signal processing, communications, and control ; _v36 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5238083 |
| 999 |
_c40062 _d40062 |
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