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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 17 of 17 for “"Compressed sensing (CS)"”.
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Building compressed sensing systems : sensors and analog-to-information converters
Compressed sensing (CS) is a promising method for recovering sparse signals from fewer measurements than ordinarily used in the Shannon's sampling theorem [14]. Introducing the CS theory has sparked interest in designing new hardware architectures which can be potential substitutions for …
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Sparse nonlinear optimization for signal processing and communications
… response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms.</p> <p>For 3-D SAR image reconstruction, the proposed …
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Super Greedy Type Algorithms and Applications In Compressed Sensing
… idea, we build new recovery algorithms in Compressed Sensing (CS) which are Orthogonal Multi Matching Pursuit (OMMP) and Orthogonal Multi Matching Pursuit with Thresholding Pruning (OMMPTP). The performances of there two algorithms are analyzed under Restricted Isometry Property (RIP) …
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A compressed sensing approach to block-iterative equalization: connections and applications to radar imaging reconstruction
… algorithmic solutions, which capitalize on the Compressed Sensing (CS) of sparse data. While well known greedy or iterative threshold type of CS recursions take the form of an adaptive filter followed by a proximal operator, this is no different in spirit from the role of block iterative …
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NEW ALGORITHMS FOR COMPRESSED SENSING OF MRI: WTWTS, DWTS, WDWTS
… a crucial challenge for many imaging techniques. Compressed Sensing (CS) theory is an appealing framework to address this issue since it provides theoretical guarantees on the reconstruction of sparse signals while projection on a low dimensional linear subspace. Further enhancements have extended …
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On the Foundations of Computation and Sampling for Reconstruction and Approximation
… also analyse their non-linear cousin structured compressed sensing (CS). Finally, we consider deep learning with neural networks, which differs to the ones before in terms that it is data-based in contrast to model-based. For the model-based reconstruction methods we focus on their numerical …
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Mathematical analysis of a dynamical system for sparse recovery
… signal recovery. Sparse recovery arises in Compressed Sensing (CS), where signals of large dimension must be recovered from a small number of linear measurements, and can be accomplished by solving a complex optimization program. While many solvers have been proposed and analyzed to solve …
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Quantification and Reconstruction in Photoacoustic Tomography
… and reduce the system cost. We adapted Compressed Sensing: CS) for the reconstruction in PACT. CS-based PACT was implemented as a non-linear conjugate gradient descent algorithm and tested with both phantom and in vivo experiments. Speckles have been considered ubiquitous in all …
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Optimization of Fast MR Imaging Technologies using the Case-PDM to Quantitatively Assess Image Quality
… To date, most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. We used an advanced observer experiment and Artifact-PDM, an extension of Case-PDM, to measure relative disturbance …
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Energy-efficient wireless sensors : fewer bits, Moore MEMS
… these two limitations, this thesis adopts compressed sensing (CS) theory as a generic source coding framework to minimize the transmitted data and proposes the use of micro-electro-mechanical (MEM) relay technology to eliminate the idle leakage. To assess the practicality of adopting CS as …
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Compressed Sensing based Micro-CT Methods and Applications
… image reconstruction, spurred by the advent of compressed sensing (CS) theory in 2006 and interior tomography theory since 2007, offers great reduction in the number of views and an increment in the volume of samples, while maintaining reconstruction accuracy. Yet, for a number of reasons, …
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Three-dimensional Quantitative Magnetic Resonance Imaging of Carotid Atherosclerotic Plaque
… In recent years, morphological characteristics of atherosclerotic plaque such as a thin fibrous cap, large lipid-rich necrotic core, intraplaque haemorrhage and ulceration have shown correlations with subsequent clinical events. High resolution, multi-contrast magnetic resonance imaging (MRI) …
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Magnetic resonance image reconstruction from highly undersampled K-Space data using dictionary learning
Compressed sensing (CS) utilizes the sparsity of MR images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this thesis, we propose a novel framework for …
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Accelerating magnetic resonance imaging by unifying sparse models and multiple receivers
… an accelerated parallel imaging method, and compressed sensing (CS) have been successfully employed to accelerate the acquisition process by reducing the number of k-space samples required. GRAPPA leverages the different spatial weightings of each receiver coil to undo the aliasing from the …
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Bridging Mri Reconstruction Across Eras: From Novel Optimization Of Traditional Methods To Efficient Deep Learning Strategies
… the clinical impact and success of PI methods, compressed sensing (CS) techniques were developed to reconstruct images by using compressibility of images in a pre-specified linear transform domain. Transform learning (TL) was another line of work that learned the linear transforms from data, …
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Acceleration of Subtractive Non-contrast-enhanced Magnetic Resonance Angiography
… the vascular system, including the characteristics of arteries and veins, and the MR properties and flow characteristics of blood. These characteristics are the foundation of NCE-MRA technique development. Chapter 2 introduces commonly used diagnostic angiographic methods, particularly CE-MRA and …
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Adaptive sparse representations and their applications
… applications such as denoising, inpainting, and compressed sensing. While there has been extensive research on learning synthesis dictionaries and some recent work on learning analysis dictionaries, the idea of learning sparsifying transforms has received no attention. In the first part of this …