MARC Record from marc_columbia

LEADER: 04656cam a2200757Mi 4500
001 15128487
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006 m o d
007 cr |||||||||||
008 171223s2017 xx a eo 000 0 eng d
035 $a(OCoLC)on1017895026
035 $a(NNC)15128487
040 $aNLE$beng$erda$epn$cNLE$dNLE$dEBLCP$dOCLCO$dOCL$dOCLCO$dOCLCQ$dOCLCO$dAU@$dUKMGB$dOCLCO$dUWO$dOTZ$dOCLCO$dOCLCQ$dNLW$dOCLCO$dOCL
015 $aGBB835840$2bnb
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019 $a1018163533$a1018251302$a1058497256$a1069686551
020 $a9781351837835$q(EPUB)
020 $a1351837834$q(EPUB)
020 $z9781138417939
020 $a9781420006384$q(e-book: PDF)$q(e-book : PDF)
020 $a142000638X
020 $a9781351829144$q(e-book: Mobi)
020 $a1351829149
020 $z9780849370434$q(paperback)
024 7 $a10.1201/9781315221946$2doi
035 $a(OCoLC)1017895026$z(OCoLC)1018163533$z(OCoLC)1018251302$z(OCoLC)1058497256$z(OCoLC)1069686551
037 $a9781351837835$bIngram Content Group
050 4 $aTK7872.F5$bP685 2017
072 7 $aTEC$x007000$2bisacsh
082 04 $a621.3815/324$222
049 $aZCUA
100 1 $aPoularikas, Alexander D.,$eauthor$uThe University of Alabama, Huntsville, USA
245 10 $aAdaptive Filtering Primer with MATLAB /$cAlexander D. Poularikas, Zayed M. Ramadan.
250 $a1st.
264 1 $bCRC Press,$c2017.
300 $a1 online resource (240 pages :$b11 illustrations.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aElectrical Engineering Primer Series
505 0 $a1. Introduction -- 2. Discrete-time signal processing -- 3. Random variables, sequences, and stochastic processes -- 4. Wiener filters -- 5. Eigenvalues of Rx -- properties of the error surface -- 6. Newton and steepest-descent method -- 7. The last mean-square (LMS) algorithm -- 8. Variations of LMS algorithms -- 9. Least squares and recursive least-squares signal processing -- Appendix: Matrix analysis.
504 $aIncludes bibliographical references and index.
520 3 $aBecause of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level.Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage.With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB® is an ideal companion for quick reference and a perfect, concise introduction to the field.
630 00 $aMATLAB.
630 06 $aMATLAB (Logiciel)
630 07 $aMATLAB$2fast$0(OCoLC)fst01365096
650 0 $aAdaptive filters.
650 0 $aFiltres adaptatifs.
650 0 $aFiltres adaptatifs - Informatique.
650 0 $aTraitement du signal.
650 0 $aAdaptive filters$xData processing.
650 0 $aSignal processing.
650 6 $aFiltres adaptatifs.
650 6 $aFiltres adaptatifs$xInformatique.
650 6 $aTraitement du signal.
650 7 $aSignal processing.$2fast$0(OCoLC)fst01118281
650 7 $aAdaptive filters.$2fast$0(OCoLC)fst00796492
650 7 $aAdaptives Filter$2gnd
650 7 $aMATLAB$2gnd
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aRamadan, Zayed M.,$eauthor.
776 08 $iPrint version:$aPoularikas, Alexander D.$tAdaptive Filtering Primer with MATLAB.$dMilton : CRC Press, ©2017$z9781138417939
830 0 $aElectrical engineering primer series.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15128487$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS