LEADER 05459nam 2200661Ia 450 001 9910830737403321 005 20210209181003.0 010 $a1-282-16506-2 010 $a9786612165061 010 $a0-470-61119-7 010 $a0-470-39382-3 035 $a(CKB)2550000000005884 035 $a(EBL)477672 035 $a(SSID)ssj0000335046 035 $a(PQKBManifestationID)11272597 035 $a(PQKBTitleCode)TC0000335046 035 $a(PQKBWorkID)10271369 035 $a(PQKB)10114410 035 $a(MiAaPQ)EBC477672 035 $a(MiAaPQ)EBC4037004 035 $a(PPN)19071316X 035 $a(OCoLC)521032626 035 $a(EXLCZ)992550000000005884 100 $a20071119d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBayesian approach to inverse problems$b[electronic resource] /$fedited by Jerome Idier 210 $aLondon $cISTE ;$aHoboken, NJ $cJohn Wiley$dc2008 215 $a1 online resource (383 p.) 225 1 $aDigital signal and image processing series. ;$vv.35 300 $aDescription based upon print version of record. 311 $a1-84821-032-9 320 $aIncludes bibliographical references and index. 327 $aBayesian Approach to Inverse Problems; Table of Contents; Introduction; Part I. Fundamental Problems and Tools; Chapter 1. Inverse Problems, Ill-posed Problems; 1.1. Introduction; 1.2. Basic example; 1.3. Ill-posed problem; 1.3.1. Case of discrete data; 1.3.2. Continuous case; 1.4. Generalized inversion; 1.4.1. Pseudo-solutions; 1.4.2. Generalized solutions; 1.4.3. Example; 1.5. Discretization and conditioning; 1.6. Conclusion; 1.7. Bibliography; Chapter 2. Main Approaches to the Regularization of Ill-posed Problems; 2.1. Regularization; 2.1.1. Dimensionality control 327 $a2.1.1.1. Truncated singular value decomposition2.1.1.2. Change of discretization; 2.1.1.3. Iterative methods; 2.1.2. Minimization of a composite criterion; 2.1.2.1. Euclidian distances; 2.1.2.2. Roughness measures; 2.1.2.3. Non-quadratic penalization; 2.1.2.4. Kullback pseudo-distance; 2.2. Criterion descent methods; 2.2.1. Criterion minimization for inversion; 2.2.2. The quadratic case; 2.2.2.1. Non-iterative techniques; 2.2.2.2. Iterative techniques; 2.2.3. The convex case; 2.2.4. General case; 2.3. Choice of regularization coefficient; 2.3.1. Residual error energy control 327 $a2.3.2. "L-curve" method2.3.3. Cross-validation; 2.4. Bibliography; Chapter 3. Inversion within the Probabilistic Framework; 3.1. Inversion and inference; 3.2. Statistical inference; 3.2.1. Noise law and direct distribution for data; 3.2.2. Maximum likelihood estimation; 3.3. Bayesian approach to inversion; 3.4. Links with deterministic methods; 3.5. Choice of hyperparameters; 3.6. A priori model; 3.7. Choice of criteria; 3.8. The linear, Gaussian case; 3.8.1. Statistical properties of the solution; 3.8.2. Calculation of marginal likelihood; 3.8.3. Wiener filtering; 3.9. Bibliography 327 $aPart II. DeconvolutionChapter 4. Inverse Filtering and Other Linear Methods; 4.1. Introduction; 4.2. Continuous-time deconvolution; 4.2.1. Inverse filtering; 4.2.2. Wiener filtering; 4.3. Discretization of the problem; 4.3.1. Choice of a quadrature method; 4.3.2. Structure of observation matrix H; 4.3.3. Usual boundary conditions; 4.3.4. Problem conditioning; 4.3.4.1. Case of the circulant matrix; 4.3.4.2. Case of the Toeplitz matrix; 4.3.4.3. Opposition between resolution and conditioning; 4.3.5. Generalized inversion; 4.4. Batch deconvolution; 4.4.1. Preliminary choices 327 $a4.4.2. Matrix form of the estimate4.4.3. Hunt's method (periodic boundary hypothesis); 4.4.4. Exact inversion methods in the stationary case; 4.4.5. Case of non-stationary signals; 4.4.6. Results and discussion on examples; 4.4.6.1. Compromise between bias and variance in 1D deconvolution; 4.4.6.2. Results for 2D processing; 4.5. Recursive deconvolution; 4.5.1. Kalman filtering; 4.5.2. Degenerate state model and recursive least squares; 4.5.3. Autoregressive state model; 4.5.3.1. Initialization; 4.5.3.2. Criterion minimized by Kalman smoother; 4.5.3.3. Example of result 327 $a4.5.4. Fast Kalman filtering 330 $aMany scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise 410 0$aDigital signal and image processing series. 606 $aInverse problems (Differential equations) 606 $aBayesian statistical decision theory 615 0$aInverse problems (Differential equations) 615 0$aBayesian statistical decision theory. 676 $a515/.357 676 $a519.542 701 $aIdier$b Je?ro?me$01593281 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830737403321 996 $aBayesian approach to inverse problems$93913340 997 $aUNINA LEADER 04618nam 22007695 450 001 9910349426803321 005 20251225203548.0 010 $a9783319942742 010 $a3319942743 024 7 $a10.1007/978-3-319-94274-2 035 $a(CKB)3850000000033242 035 $a(DE-He213)978-3-319-94274-2 035 $a(MiAaPQ)EBC6298662 035 $a(PPN)22950230X 035 $a(EXLCZ)993850000000033242 100 $a20180625d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputers Helping People with Special Needs $e16th International Conference, ICCHP 2018, Linz, Austria, July 11-13, 2018, Proceedings, Part II /$fedited by Klaus Miesenberger, Georgios Kouroupetroglou 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XXX, 568 p. 180 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v10897 300 $aIncludes index. 311 08$a9783319942735 311 08$a3319942735 330 $aThe two-volume set LNCS 10896 and 10897 constitutes the refereed proceedings of the 16th International Conference on Computers Helping People with Special Needs, ICCHP 2018, held in Linz, Austria, in July 2018. The 101 revised full papers and 78 short papers presented were carefully reviewed and selected from 356 submissions. The papers are organized in the following topical sections: Web accessibility in the connected world; accessibility and usability of mobile platforms for people with disabilities and elderly persons: design, development and engineering; accessible system/information/document design; accessible e-learning - e-learning for accessibility/AT; personalized access to TV, film, theatre, and music; digital games accessibility; accessibility and usability of self-service terminals, technologies and systems; universal learning design; motor and mobility disabilities: AT, HCI, care; empowerment of people with cognitive disabilities using digital technologies; augmented and alternative communication (AAC), supported speech; Art Karshmer lectures in access to mathematics, science and engineering; environmental sensing technologies for visual impairment; 3D printing in the domain of assistive technologies (AT) and do it yourselves (DIY) AT; tactile graphics and models for blind people and recognition of shapes by touch; access to artworks and its mediation by and for visually impaired people; digital navigation for people with visual impairments; low vision and blindness: human computer interaction; future perspectives for ageing well: AAL tools, products, services; mobile healthcare and m-health apps for people with disabilities; and service and information provision. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v10897 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aApplication software 606 $aComputers, Special purpose 606 $aComputer networks 606 $aArtificial intelligence 606 $aEducation$xData processing 606 $aUser Interfaces and Human Computer Interaction 606 $aComputer and Information Systems Applications 606 $aSpecial Purpose and Application-Based Systems 606 $aComputer Communication Networks 606 $aArtificial Intelligence 606 $aComputers and Education 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 0$aApplication software. 615 0$aComputers, Special purpose. 615 0$aComputer networks. 615 0$aArtificial intelligence. 615 0$aEducation$xData processing. 615 14$aUser Interfaces and Human Computer Interaction. 615 24$aComputer and Information Systems Applications. 615 24$aSpecial Purpose and Application-Based Systems. 615 24$aComputer Communication Networks. 615 24$aArtificial Intelligence. 615 24$aComputers and Education. 676 $a362.40480285 702 $aMiesenberger$b Klaus$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKouroupetroglou$b Georgios$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349426803321 996 $aComputers Helping People with Special Needs$92889197 997 $aUNINA