Search for dissertations about: "Compressive sampling"
Showing result 1 - 5 of 6 swedish dissertations containing the words Compressive sampling.
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1. Cooperative Compressive Sampling
Abstract : Compressed Sampling (CS) is a promising technique capable of acquiring and processing data of large sizes efficiently. The CS technique exploits the inherent sparsity present in most real-world signals to achieve this feat. Most real-world signals, for example, sound, image, physical phenomenon etc., are compressible or sparse in nature. READ MORE
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2. Mechanical properties of excavated sulfur rich soil stabilized with cement - A laboratory and field experiment
Abstract : Sulfide soils are silty soils, often found in saturated conditions, under the groundwater level. Characteristics of these soils, including particle size distribution and consistency limits along with chemical composition and environmental properties, cause excavation to be necessary for construction purposes. READ MORE
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3. On Invertibility of the Radon Transform and Compressive Sensing
Abstract : This thesis contains three articles. The first two concern inversion andlocal injectivity of the weighted Radon transform in the plane. The thirdpaper concerns two of the key results from compressive sensing.In Paper A we prove an identity involving three singular double integrals. READ MORE
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4. Ultra Wideband: Communication and Localization
Abstract : The first part of this thesis develops methods for UWB communication. To meet the stringent regulatory body constraints, the physical layer signaling technique of the UWB transceiver should be optimally designed. READ MORE
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5. Enhanced block sparse signal recovery and bayesian hierarchical models with applications
Abstract : This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). READ MORE