Data Compression and Compressed Sensing in Imaging Mass Spectrometry and Sporadic Communication

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Bartels, Andreas
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin : Logos Verlag Berlin, 2014.
Temas:
Mass spectrometry.
Spectrométrie de masse.
mass spectrometry.
Mass spectrometry
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro; 1 Introduction; 1.1 The big data problem; 1.2 Data compression; 1.3 What compressed sensing is about; 1.4 Scientific contributions of the thesis; 1.5 Organization of the thesis; 2 Preliminaries and concepts; 2.1 Notations; 2.2 Proximity operators and algorithms; 3 Data compression; 3.1 What is compression?; 3.2 Compression and quality measures; 3.3 Mathematical techniques; 3.3.1 `0 and `1 minimization; 3.3.2 TV minimization; 3.3.3 Nonnegative matrix factorization; 4 Compressed Sensing; 4.1 Introduction; 4.2 Uniqueness, sparseness and other properties; 4.2.1 Coherence
  • 4.2.2 Restricted isometry property4.3 Stable `1 minimization; 4.4 Stable total variation minimization; 4.5 Coherent and redundant dictionaries; 4.6 Asymmetric restricted isometry property; 5 Imaging mass spectrometry in a nutshell; 5.1 Mass spectrometry; 5.2 Imaging mass spectrometry; 5.3 Datasets used in this thesis; 5.3.1 Rat brain; 5.3.2 Rat kidney; 6 Compression in imaging mass spectrometry; 6.1 Introduction; 6.2 Peak picking; 6.2.1 Spectral peak picking; 6.2.2 Spatial peak picking; 6.3 Denoising; 6.4 Nonnegative matrix factorization; 6.5 Conclusion
  • 7 Compressed sensing in imaging mass spectrometry7.1 Introduction; 7.2 The compressed sensing process; 7.3 First assumption: compressible spectra; 7.4 Second assumption: sparse image gradients; 7.5 The final model; 7.6 Robust recovery; 7.7 Numerical results; 7.8 Conclusion; 8 Compressed sensing based multi-user detection; 8.1 Introduction; 8.2 Sporadic communication; 8.3 Multi-user system modelling; 8.4 The elastic-net; 8.5 The multi-user test setup; 8.6 A parameter choice rule: The C-curve criterion; 8.7 An offline approach; 8.8 Conclusion; 9 Conclusion

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