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dc.contributor.advisorAl-Naffouri, Tareq Y.
dc.contributor.authorSana, Furrukh
dc.date.accessioned2016-11-23T06:12:44Z
dc.date.available2017-12-01T00:00:00Z
dc.date.issued2016-11
dc.identifier.citationSana, F. (2016). Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences. KAUST Research Repository. https://doi.org/10.25781/KAUST-4J86L
dc.identifier.doi10.25781/KAUST-4J86L
dc.identifier.urihttp://hdl.handle.net/10754/621865
dc.description.abstractSparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the fundamental bottlenecks, both in terms of estimation accuracies and required computational resources. In the first part of dissertation, we present a high precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust performance in the presence of noise and interferences. We also present a comprehensive framework for the important problem of extracting the fetal electrocardiogram (ECG) signals from abdominal ECG recordings of pregnant women. The multiple measurement vectors approach utilized for this purpose provides an efficient mechanism of exploiting the common structure of ECG signals, when represented in sparse transform domains, and allows leveraging information from multiple ECG electrodes under a joint estimation formulation. In the second part of dissertation, we adopt sparse signal processing principles for improved information recovery in large-scale subsurface reservoir characterization problems. We propose multiple new algorithms for sparse representation of the subsurface geological structures, incorporation of useful prior information in the estimation process, and for reducing computational complexities of the problem. The techniques presented here enable significantly enhanced imaging of the subsurface earth and result in substantial savings in terms of convergence time, leading to optimized placement of oil wells. This dissertation demonstrates through detailed experimental analysis that the sparse estimation approach not only enables enhanced information recovery in variety of application areas, but also greatly helps in reducing the computational complexities associated with the problems.
dc.language.isoen
dc.subjectCompressed Sensing
dc.subjectBiomedical
dc.subjectGeophysics
dc.subjectSignal processing
dc.subjectBayesian Estimation
dc.subjectSparsity
dc.titleEfficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences
dc.typeDissertation
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.rights.embargodate2017-12-01
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberHoteit, Ibrahim
dc.contributor.committeememberMagistretti, Pierre J.
dc.contributor.committeememberAlouini, Mohamed-Slim
dc.contributor.committeememberLaleg-Kirati, Taous-Meriem
dc.contributor.committeememberAlRegib, Ghassan
thesis.degree.disciplineElectrical Engineering
thesis.degree.nameDoctor of Philosophy
dc.rights.accessrightsAt the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2017-12-01.
refterms.dateFOA2017-12-01T00:00:00Z


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