LEADER 04070nam 2200361za 450 001 9910555286403321 005 20220309163110.0 010 $a1-118-97931-1 010 $a1-119-55541-8 010 $a1-118-97932-X 035 $a(EXLCZ)994100000007926688 100 $a20190423d2019 uy 0 101 0 $aeng 135 $aurcn|nnn||||| 200 10$aDiscrete wavelet transformations$b[electronic resource] $ean elementary approach with applications /$fPatrick J. Van Fleet 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$d2019 215 $a1 online resource (xxvii, 587 p.) $cill 300 $aPrevious ed.: 2008. 311 08$aPrint version: Van Fleet, Patrick J., 1962- author. Discrete wavelet transformations 2nd edition. Hoboken, NJ : John Wiley & Sons, Inc., [2019] 9781118979273 (DLC) 2018046966 320 $aIncludes bibliographical references and index. 327 $a1. Introduction : why wavelets? -- 2. Vectors and matrices -- 3. An introduction to digital images -- 4. The haar wavelet transformation -- 5. Daubechies wavelet transformations -- 6. Wavelet shrinkage : an application to denoising -- 7. Biorthogonal wavelet transformations -- 8. Complex numbers and Fourier series -- 9. Filter construction in the Fourier domain -- 10. Wavelet packets -- 11. Lifting -- 12. The JPEG2000 image compression standard -- A. Basic statistics. 330 $aThe new edition of Discrete Wavelet Transformations continues to guide readers through the abstract concepts of wavelet theory by using Dr. Van Fleet?s highly practical, application-based approach, which reflects how mathematicians construct solutions to challenges outside the classroom. By introducing the Haar, orthogonal, and biorthogonal filters without the use of Fourier series, Van Fleet allows his audience to connect concepts directly to real-world applications at an earlier point than other publications in the field. Leveraging extensive graphical displays, this self-contained volume integrates concepts from calculus and linear algebra into the constructions of wavelet transformations and their applications, including data compression, edge detection in images and denoising of signals. Conceptual understanding is reinforced with over 500 detailed exercises and 24 computer labs. The second edition discusses new applications including image segmentation, pansharpening, and the FBI fingerprint compression specification. Other notable features include: Two new chapters covering wavelet packets and the lifting method; A reorganization of the presentation so that basic filters can be constructed without the use of Fourier techniques; A new comprehensive chapter that explains filter derivation using Fourier techniques; Over 120 examples of which 91 are ?live examples,? which allow the reader to quickly reproduce these examples in Mathematica or MATLAB and deepen conceptual mastery; An overview of digital image basics, equipping readers with the tools they need to understand the image processing applications presented; A complete rewrite of the DiscreteWavelets package called WaveletWare for use with Mathematica and MATLAB; A website, www.stthomas.edu/wavelets, featuring material containing the WaveletWare package, live examples, and computer labs in addition to companion material for teaching a course using the book. Comprehensive and grounded, this book and its online components provide an excellent foundation for developing undergraduate courses as well as a valuable resource for mathematicians, signal process engineers, and other professionals seeking to understand the practical applications of discrete wavelet transformations in solving real-world challenges. 606 $aWavelets (Mathematics) 606 $aTransformations (Mathematics) 615 0$aWavelets (Mathematics) 615 0$aTransformations (Mathematics) 676 $a515.2433 700 $aVan Fleet$b Patrick J.$f1962-$0521988 906 $aBOOK 912 $a9910555286403321 996 $aDiscrete wavelet transformations$9835247 997 $aUNINA LEADER 02551nam 2200361z- 450 001 9910557114403321 005 20210501 035 $a(CKB)5400000000040902 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68848 035 $a(oapen)doab68848 035 $a(EXLCZ)995400000000040902 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInformation Theory and Language 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (244 p.) 311 08$a3-03936-026-4 311 08$a3-03936-027-2 330 $a"Information Theory and Language" is a collection of 12 articles that appeared recently in Entropy as part of a Special Issue of the same title. These contributions represent state-of-the-art interdisciplinary research at the interface of information theory and language studies. They concern in particular: ? Applications of information theoretic concepts such as Shannon and Re?nyi entropies, mutual information, and rate-distortion curves to the research of natural languages; ? Mathematical work in information theory inspired by natural language phenomena, such as deriving moments of subword complexity or proving continuity of mutual information; ? Empirical and theoretical investigation of quantitative laws of natural language such as Zipf's law, Herdan's law, and Menzerath-Altmann's law; ? Empirical and theoretical investigations of statistical language models, including recently developed neural language models, their entropies, and other parameters; ? Standardizing language resources for statistical investigation of natural language; ? Other topics concerning semantics, syntax, and critical phenomena. Whereas the traditional divide between probabilistic and formal approaches to human language, cultivated in the disjoint scholarships of natural sciences and humanities, has been blurred in recent years, this book can contribute to pointing out potential areas of future research cross-fertilization. 606 $aLanguage and Linguistics$2bicssc 615 7$aLanguage and Linguistics 700 $aD?bowski$b ?ukasz$4edt$01290948 702 $aBentz$b Christian$4edt 702 $aD?bowski$b ?ukasz$4oth 702 $aBentz$b Christian$4oth 906 $aBOOK 912 $a9910557114403321 996 $aInformation Theory and Language$93021678 997 $aUNINA