| 000 | 05990nam a2201117 i 4500 | ||
|---|---|---|---|
| 001 | 5271182 | ||
| 003 | IEEE | ||
| 005 | 20220712205711.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151221s2003 njua ob 001 eng d | ||
| 020 |
_a9780470547199 _qelectronic |
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| 020 |
_z9780471259756 _qprint |
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| 020 |
_z0470547197 _qelectronic |
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| 024 | 7 |
_a10.1109/9780470547199 _2doi |
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| 035 | _a(CaBNVSL)mat05271182 | ||
| 035 | _a(IDAMS)0b000064810cc91d | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA274 _b.L88 2003eb |
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| 082 | 0 | 4 |
_a621.38223 _222 |
| 100 | 1 |
_aLudeman, Lonnie C., _eauthor. _927118 |
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| 245 | 1 | 0 |
_aRandom processes : _bfiltering, estimation, and detection / _cLonnie C. Ludeman. |
| 264 | 1 |
_aHoboken, New Jersey : _bWiley-Interscience, _cc2003. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2003] |
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| 300 |
_a1 PDF (xvii, 608 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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| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aPreface. -- Experiments and Probability. -- Random Variables. -- Estimation of Random Variables. -- Random Processes. -- Linear Systems: Random Processes. -- Nonlinear Systems: Random Processes. -- Optimum Linear Filters: The Wiener Approach. -- Optimum Linear Systems: The Kalman Approach. -- Detection Theory: Discrete Observation. -- Detection Theory: Continuous Observation. -- Appendix A. The Bilateral Laplace Transform. -- Appendix B. Table of Binomial Probabilities. -- Appendix C. Table of Discrete Random Variables and Properties. -- Appendix D. Table of Continuous Random Variables and Properties. -- Appendix E. Table of Gaussian Cumulative Distribution Function. -- Index. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aAn understanding of random processes is crucial to many engineering fields-including communication theory, computer vision, and digital signal processing in electrical and computer engineering, and vibrational theory and stress analysis in mechanical engineering. The filtering, estimation, and detection of random processes in noisy environments are critical tasks necessary in the analysis and design of new communications systems and useful signal processing algorithms. Random Processes: Filtering, Estimation, and Detection clearly explains the basics of probability and random processes and details modern detection and estimation theory to accomplish these tasks. In this book, Lonnie Ludeman, an award-winning authority in digital signal processing, joins the fundamentals of random processes with the standard techniques of linear and nonlinear systems analysis and hypothesis testing to give signal estimation techniques, specify optimum estimation procedures, provide optimum decision rules for classification purposes, and describe performance evaluation definitions and procedures for the resulting methods. The text covers four main, interrelated topics: * Probability and characterizations of random variables and random processes * Linear and nonlinear systems with random excitations * Optimum estimation theory including both the Wiener and Kalman Filters * Detection theory for both discrete and continuous time measurements Lucid, thorough, and well-stocked with numerous examples and practice problems that emphasize the concepts discussed, Random Processes: Filtering, Estimation, and Detection is an understandable and useful text ideal as both a self-study guide for professionals in the field and as a core text for graduate students. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/21/2015. | ||
| 650 | 0 |
_aStochastic processes. _93246 |
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| 650 | 0 |
_aSignal processing. _94052 |
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| 650 | 0 |
_aImage processing. _97417 |
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| 651 | 7 |
_aProcessos Estocasticos. _2larpcal _927119 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 695 | _aAWGN | ||
| 695 | _aAdditive white noise | ||
| 695 | _aApproximation methods | ||
| 695 | _aArtificial intelligence | ||
| 695 | _aConcrete | ||
| 695 | _aContinuous time systems | ||
| 695 | _aConvergence | ||
| 695 | _aConvolution | ||
| 695 | _aCorrelation | ||
| 695 | _aCost function | ||
| 695 | _aCovariance matrix | ||
| 695 | _aDensity functional theory | ||
| 695 | _aDistribution functions | ||
| 695 | _aEquations | ||
| 695 | _aEstimation | ||
| 695 | _aExtraterrestrial measurements | ||
| 695 | _aFinite element methods | ||
| 695 | _aFourier transforms | ||
| 695 | _aFrequency domain analysis | ||
| 695 | _aGaussian distribution | ||
| 695 | _aGaussian processes | ||
| 695 | _aIndexes | ||
| 695 | _aIndexing | ||
| 695 | _aInformation filters | ||
| 695 | _aIntegral equations | ||
| 695 | _aJoints | ||
| 695 | _aKalman filters | ||
| 695 | _aKernel | ||
| 695 | _aLaplace equations | ||
| 695 | _aLinear systems | ||
| 695 | _aMathematical model | ||
| 695 | _aMaximum likelihood detection | ||
| 695 | _aMeasurement uncertainty | ||
| 695 | _aNonlinear filters | ||
| 695 | _aNonlinear systems | ||
| 695 | _aPattern recognition | ||
| 695 | _aPower measurement | ||
| 695 | _aProbability | ||
| 695 | _aProbability density function | ||
| 695 | _aRadar | ||
| 695 | _aRandom processes | ||
| 695 | _aRandom variables | ||
| 695 | _aStrips | ||
| 695 | _aSupport vector machine classification | ||
| 695 | _aTesting | ||
| 695 | _aTime domain analysis | ||
| 695 | _aTime measurement | ||
| 695 | _aTime varying systems | ||
| 695 | _aTransforms | ||
| 695 | _aVectors | ||
| 710 | 2 |
_aJohn Wiley & Sons, _epublisher. _96902 |
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| 710 | 2 |
_aIEEE Xplore (Online service), _edistributor. _927120 |
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| 776 | 0 | 8 |
_iPrint version: _z9780471259756 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5271182 |
| 942 | _cEBK | ||
| 999 |
_c73971 _d73971 |
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