LEADER 01793nam 2200565 a 450 001 9910454874103321 005 20200520144314.0 010 $a1-280-20368-4 010 $a9786610203680 010 $a0-309-54389-4 010 $a0-585-02886-9 035 $a(CKB)111004366660608 035 $a(SSID)ssj0000196232 035 $a(PQKBManifestationID)11180254 035 $a(PQKBTitleCode)TC0000196232 035 $a(PQKBWorkID)10142777 035 $a(PQKB)11616528 035 $a(MiAaPQ)EBC3376959 035 $a(Au-PeEL)EBL3376959 035 $a(CaPaEBR)ebr10062839 035 $a(OCoLC)923267225 035 $a(EXLCZ)99111004366660608 100 $a19910807d1991 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMalaria$b[electronic resource] $eobstacles and opportunities : a report of the Committee for the Study on Malaria Prevention and Control: Status Review and Alternative Strategies, Division of International Health, Institute of Medicine /$fStanley C. Oaks, Jr. ... [et al.], editors 210 $aWashington, D.C. $cNational Academy Press$d1991 215 $axv, 309 p. $cill 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-309-04527-4 320 $aIncludes bibliographical references and index. 606 $aMalaria 606 $aMalaria$xPrevention 606 $aMalaria$xResearch 608 $aElectronic books. 615 0$aMalaria. 615 0$aMalaria$xPrevention. 615 0$aMalaria$xResearch. 676 $a614.5/32 701 $aOaks$b S. 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Garello 210 $aLondon $cISTE$d[2008] 215 $a1 online resource (312 pages) 225 1 $aISTE ;$vv.21 300 $aDescription based upon print version of record. 311 $a1-84821-018-3 320 $aIncludes bibliographical references and index. 327 $aTwo-Dimensional Signal Analysis; Table of Contents; Introduction; Chapter 1. Basic Elements of 2-D Signal Processing; 1.1. Introduction; 1.2. Deterministic 2-D signals; 1.2.1. Definition; 1.2.2. Particular 2-D signals; 1.3. Random 2-D signals; 1.3.1. Definition; 1.3.2. Characterization up to the second order; 1.3.3. Stationarity; 1.3.4. Characterization of orders higher than two; 1.3.5. Ergodicity; 1.3.6. Specificities of random 2-D signals; 1.3.7. Particular random signals; 1.3.7.1. White noise; 1.3.7.2. Gaussian process; 1.4. 2-D systems; 1.4.1. Definition; 1.4.2. Main 2-D operators 327 $a1.4.3. Main properties1.4.4. Linear time-invariant (LTI) system; 1.4.5. Example; 1.4.6. Separable system; 1.4.7. Stability of 2-D systems; 1.4.8. Support of the impulse response - causality; 1.5. Characterization of 2-D signals and systems; 1.5.1. Frequency response of an LTI system; 1.5.2. 2-D Fourier transform; 1.5.2.1. Definition; 1.5.2.2. Properties; 1.5.3. Discrete 2-D Fourier transform; 1.5.3.1. Definition; 1.5.3.2. Properties; 1.5.3.3. Calculation of the 2-D DFT; 1.5.4. 2-D z transform; 1.5.4.1. Definition; 1.5.4.2. Region of convergence; 1.5.4.3. Properties 327 $a1.5.4.4. Transfer function of a 2-D system1.5.4.5. 2-D inverse ZT; 1.5.4.6. Application to the study of stability of LTI systems; 1.5.4.7. Minimum or non-minimum phase LTI system; 1.5.5. Frequency characterization of a random 2-D signal.; 1.5.6. Output of a 2-D system with random input; 1.6. 2-D Wold decomposition; 1.6.1. Innovation, determinism and regularity in the 2-D case; 1.6.2. Total decomposition of three fields; 1.6.3. Example of an outcome; 1.7. Conclusion; 1.8. Bibliography; Chapter 2. 2-D Linear Stochastic Modeling; 2.1. Introduction; 2.2. 2-D ARMA models; 2.2.1. Definition 327 $a2.2.2. 2-D ARMA models and prediction supports2.2.2.1. Causal models; 2.2.2.2. Causal quarter plane model; 2.2.2.3. Causal model whose support is delimited by any two NSHPs; 2.2.2.4. Semi-causal model; 2.2.2.5. Non-causal model; 2.3. L-Markovian fields; 2.3.1. 2-D Markov fields and L-Markovian fields; 2.3.2. 2-D L-Markovian fields and Gibbs fields; 2.4. "Global" estimation methods; 2.4.1. Maximum likelihood; 2.4.1.1. Estimation criteria by supposing the fixed order; 2.4.1.2. Probability criteria "penalized" to estimate the order of the model; 2.4.2. Yule-Walker equations 327 $a2.4.2.1. Representation of minimum variance and formulation2.4.2.2. Non-causal support and L-Markovian fields; 2.4.2.3. Causal support and 2-D AR model; 2.4.2.4. Extension to the 2-D AR non-causal model; 2.4.2.5. Extension to the 2-D ARMA model; 2.4.3. 2-D Levinson algorithm (for the parametric 2-D AR estimation); 2.4.3.1. Recalling the 1-D case; 2.4.3.2. Approach for 2-D causal and non-causal prediction models; 2.4.3.3. Multichannel approach and 2-D QP AR model; 2.4.3.4. Other approaches; 2.5. "Adaptive" or "recursive" estimation methods 327 $a2.5.1. Connectivity hypotheses for adaptive or recursive algorithms 330 $aThis title sets out to show that 2-D signal analysis has its own role to play alongside signal processing and image processing.Concentrating its coverage on those 2-D signals coming from physical sensors (such as radars and sonars), the discussion explores a 2-D spectral approach but develops the modeling of 2-D signals and proposes several data-oriented analysis techniques for dealing with them. 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