001452390 000__ 04760cam\a22005057i\4500
001452390 001__ 1452390
001452390 003__ OCoLC
001452390 005__ 20230310003353.0
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001452390 008__ 230125s2022\\\\sz\a\\\\ob\\\\001\0\eng\d
001452390 020__ $$a9783031133312$$qelectronic book
001452390 020__ $$a3031133315$$qelectronic book
001452390 020__ $$z9783031133305
001452390 020__ $$z3031133307
001452390 0247_ $$a10.1007/978-3-031-13331-2$$2doi
001452390 035__ $$aSP(OCoLC)1365395366
001452390 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dUKAHL
001452390 049__ $$aISEA
001452390 050_4 $$aTK5102.9$$b.R36 2022
001452390 08204 $$a621.382/2$$223/eng/20230125
001452390 1001_ $$aRamirez, David,$$eauthor.
001452390 24510 $$aCoherence :$$bin signal processing and machine learning /$$cDavid Ramírez, Ignacio Santamaría, Louis Scharf.
001452390 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001452390 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and colour).
001452390 336__ $$atext$$btxt$$2rdacontent
001452390 337__ $$acomputer$$bc$$2rdamedia
001452390 338__ $$aonline resource$$bcr$$2rdacarrier
001452390 504__ $$aIncludes bibliographical references (pages 467-482) and index.
001452390 5050_ $$aIntroduction -- Historical perspective, motivating problems, and preview of what is to come -- Least Squares and related -- Classical correlations and coherence -- Coherence in the multivariate normal (MVN) model -- Classical tests for correlation -- One-channel matched subspace detectors -- Adaptive subspace detectors -- Two channel matched subspace detectors -- Detection of spatially-correlated time series -- Coherence and the detection of cyclostationarity -- Partial coherence for testing causality -- Subspace averaging -- Coherence and performance bounds -- Variations on coherence -- Conclusion.
001452390 506__ $$aAccess limited to authorized users.
001452390 520__ $$aThis book organizes principles and methods of signal processing and machine learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor array processing, spectrum analysis, hyperspectral imaging, subspace clustering, and related. The reader will find new results for model fitting; for dimension reduction in models and ambient spaces; for detection, estimation, and space-time series analysis; for subspace averaging; and for uncertainty quantification. Throughout, the transformation invariances of statistics are clarified, geometries are illuminated, and null distributions are given where tractable. Stochastic representations are emphasized, as these are central to Monte Carlo simulations. The appendices contain a comprehensive account of matrix theory, the SVD, the multivariate normal distribution, and many of the important distributions for coherence statistics. The book begins with a review of classical results in the physical and engineering sciences where coherence plays a fundamental role. Then least squares theory and the theory of minimum mean-squared error estimation are developed, with special attention paid to statistics that may be interpreted as coherence statistics. A chapter on classical hypothesis tests for covariance structure introduces the next three chapters on matched and adaptive subspace detectors. These detectors are derived from likelihood reasoning, but it is their geometries and invariances that qualify them as coherence statistics. A chapter on independence testing in space-time data sets leads to a definition of broadband coherence, and contains novel applications to cognitive radio and the analysis of cyclostationarity. The chapter on subspace averaging reviews basic results and derives an order-fitting rule for determining the dimension of an average subspace. These results are used to enumerate sources of acoustic and electromagnetic radiation and to cluster subspaces into similarity classes. The chapter on performance bounds and uncertainty quantification emphasizes the geometry of the Cramer-Rao bound and its related information geometry.
001452390 588__ $$aDescription based on print version record.
001452390 650_0 $$aSignal processing.
001452390 650_0 $$aMachine learning.
001452390 655_0 $$aElectronic books.
001452390 7001_ $$aSantamaría, Ignacio,$$eauthor.
001452390 7001_ $$aScharf, Louis L.,$$eauthor.
001452390 77608 $$iPrint version:$$aRamirez, David.$$tCoherence.$$dCham : Springer, 2022$$z9783031133305$$w(OCoLC)1346321202
001452390 852__ $$bebk
001452390 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-13331-2$$zOnline Access$$91397441.1
001452390 909CO $$ooai:library.usi.edu:1452390$$pGLOBAL_SET
001452390 980__ $$aBIB
001452390 980__ $$aEBOOK
001452390 982__ $$aEbook
001452390 983__ $$aOnline
001452390 994__ $$a92$$bISE