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Compressive sensing in health care

Khosravy, Mahdi.

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Compressive sensing in health care

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Compressive sensing in health care/ edited by Mahdi Khosravy, Nilanjan Dey, Carlos A. Duque.
其他作者:	Khosravy, Mahdi.
出版者:	London :Academic Press, : 2020.,
面頁冊數:	1 online resource.
附註:	Includes index.
內容註:	Front Cover -- Compressive Sensing in Healthcare -- Copyright -- Contents -- List of contributors -- 1 Compressive sensing theoretical foundations in a nutshell -- 1.1 Introduction -- 1.2 Digital signal acquisition -- 1.3 Vectorial representation of signal -- l1 norm -- l2 norm -- l∞norm -- Spheres made by different lp norms as distance criterion -- Basis/dictionary -- Orthonormal basis/dictionary -- Frame/ over-complete dictionary -- Alternate/dual frame -- 1.4 Sparsity -- k-sparse signal -- Non-linearity of sparsity -- Sparsity and compressibility -- 1.5 Compressive sensing
內容註:	Compressive sensing model -- 1.6 Essential properties of compressive sensing matrix -- 1.6.1 Null space property (NSP) -- The essence of the concept of recovery -- Maximum compression in compressive sensing (lower bound of m) -- 1.6.2 Restricted isometry property -- 1.6.3 Coherence a simple way to check NSP -- Relation between coherence and spark of a matrix -- Coherence approach to RIP -- 1.7 Summary -- 1.A -- Null space property of order 2k -- References -- 2 Recovery in compressive sensing: a review -- 2.1 Introduction -- 2.1.1 Compressive sensing formulation
內容註:	2.2 Criteria required for a compressive sensing matrix -- 2.2.1 Null space property -- Null space property of order k -- 2.2.1.1 Uniqueness theorem [46] -- Maximum compression in compressive sensing -- 2.2.2 Restricted isometry property -- 2.2.3 Coherence property -- 2.2.3.1 Coherence and spark of a matrix -- 2.2.3.2 The upper bound of sparsity level -- 2.3 Recovery -- 2.3.1 Recovery via minimization of l1 norm -- 2.3.2 Greedy algorithms -- 2.3.2.1 Pursuits -- 2.3.2.2Matching pursuit -- 2.3.2.3 Orthogonal matching pursuit -- 2.3.2.4 Iterative hard thresholding -- 2.4 Summary -- References
內容註:	Measure SGini -- 3.5 Summary -- References -- 4 Compressive sensing in practice and potential advancements -- 4.1 Introduction -- 4.2 Compressive sensing theory -- 4.3 Example compressive sensing implementations -- 4.3.1 Compressivesensing in physiological signal monitoring -- In the eld application results -- 4.3.2 Compressive sensing in THEMIS imaging -- In-the- eld application results -- 4.4 Review of CS literature -- 4.4.1 Practical manifestations of theoretical bounds -- 4.5 Advancements in compressive sensing -- 4.5.1 Personalized basis -- Challenges
標題:	Compressed sensing (Telecommunication) -
電子資源:	https://www.sciencedirect.com/science/book/9780128212479
ISBN:	9780128212486 (electronic bk.)

Compressive sensing in health care
Compressive sensing in health care[electronic resource] /edited by Mahdi Khosravy, Nilanjan Dey, Carlos A. Duque. - London :Academic Press,2020. - 1 online resource. - Advances in ubiquitous sensing applications for healthcare. - Advances in ubiquitous sensing applications for healthcare..

Includes index.

Front Cover -- Compressive Sensing in Healthcare -- Copyright -- Contents -- List of contributors -- 1 Compressive sensing theoretical foundations in a nutshell -- 1.1 Introduction -- 1.2 Digital signal acquisition -- 1.3 Vectorial representation of signal -- l1 norm -- l2 norm -- l∞norm -- Spheres made by different lp norms as distance criterion -- Basis/dictionary -- Orthonormal basis/dictionary -- Frame/ over-complete dictionary -- Alternate/dual frame -- 1.4 Sparsity -- k-sparse signal -- Non-linearity of sparsity -- Sparsity and compressibility -- 1.5 Compressive sensing

ISBN: 9780128212486 (electronic bk.)Subjects--Topical Terms:

3214582
Compressed sensing (Telecommunication)
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: TA1638

Dewey Class. No.: 621.382/2

Compressive sensing in health care
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505 8 $a 2.2 Criteria required for a compressive sensing matrix -- 2.2.1 Null space property -- Null space property of order k -- 2.2.1.1 Uniqueness theorem [46] -- Maximum compression in compressive sensing -- 2.2.2 Restricted isometry property -- 2.2.3 Coherence property -- 2.2.3.1 Coherence and spark of a matrix -- 2.2.3.2 The upper bound of sparsity level -- 2.3 Recovery -- 2.3.1 Recovery via minimization of l1 norm -- 2.3.2 Greedy algorithms -- 2.3.2.1 Pursuits -- 2.3.2.2Matching pursuit -- 2.3.2.3 Orthogonal matching pursuit -- 2.3.2.4 Iterative hard thresholding -- 2.4 Summary -- References
505 8 $a Measure SGini -- 3.5 Summary -- References -- 4 Compressive sensing in practice and potential advancements -- 4.1 Introduction -- 4.2 Compressive sensing theory -- 4.3 Example compressive sensing implementations -- 4.3.1 Compressivesensing in physiological signal monitoring -- In the eld application results -- 4.3.2 Compressive sensing in THEMIS imaging -- In-the- eld application results -- 4.4 Review of CS literature -- 4.4.1 Practical manifestations of theoretical bounds -- 4.5 Advancements in compressive sensing -- 4.5.1 Personalized basis -- Challenges
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650 0 $a Medical innovations. $3 605994
650 1 2 $a Biosensing Techniques. $3 956063
655 0 $a Electronic books. $2 lcsh $3 542853
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700 1 $a Dey, Nilanjan, $d 1984- $3 3408435
700 1 $a Duque, Carlos A. $3 3508727
830 0 $a Advances in ubiquitous sensing applications for healthcare. $3 3508721
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