Compressive sensing in health care /

The focus of the book is on healthcare applications for this technology. --

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Khosravy, Mahdi, Dey, Nilanjan, 1984-, Duque, Carlos A.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2020.
Colección:Advances in ubiquitous sensing applications for healthcare.
Temas:
Compressed sensing (Telecommunication)
Medical innovations.
Biosensing Techniques
Acquisition comprim�ee.
M�edecine > Innovations.
Medical innovations
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

Ejemplares similares