| Description |
1 online resource |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Series |
Advances in ubiquitous sensing applications for healthcare |
|
Advances in ubiquitous sensing applications for healthcare.
|
| Note |
Includes index. |
| Contents |
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 |
|
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 |
|
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 -- 2.3.2.2 Matching pursuit -- 2.3.2.3 Orthogonal matching pursuit -- 2.3.2.4 Iterative hard thresholding -- 2.4 Summary -- References |
|
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 Compressive sensing 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 |
| Summary |
The focus of the book is on healthcare applications for this technology. -- Edited summary from book. |
| Subject |
Compressed sensing (Telecommunication)
|
|
Medical innovations.
|
|
Biosensing Techniques |
|
Acquisition comprimée.
|
|
Médecine -- Innovations.
|
|
Compressed sensing (Telecommunication)
|
|
Medical innovations
|
| Added Author |
Khosravy, Mahdi.
|
|
Dey, Nilanjan, 1984-
|
|
Duque, Carlos A.
|
| Other Form: |
Ebook version : 9780128212486 |
|
Print version: Compressive sensing in health care. London : Academic Press, 2020 0128212470 9780128212479 (OCoLC)1122451466 |
| ISBN |
9780128212486 (electronic bk.) |
|
0128212489 (electronic bk.) |
|
9780128212479 |
|
0128212470 |
| Standard No. |
AU@ 000067312795 |
|
UKMGB 019738505 |
|
