Global ETD Search
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 20 of 119 for “"compressed sensing"”.
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Ionospheric imaging with compressed sensing
Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can …
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Greedy Algorithms In Approximation Theory and Compressed Sensing
… two aspects, Nonlinear Approximation Theory and Compressed Sensing. In the setting of Nonlinear Approximation Theory, we mainly study the direction (Jackson) and inverse (Bernstein) theorems with bases that are tensor products of univariate greedy bases, as well as Lebesgue type inequalities for …
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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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One-bit Compressed Sensing in the Presence of Noise
… the signal acquisition paradigm known as one-bit compressed sensing (one-bit CS) for signal reconstruction and parameter estimation. </p><p>We first consider the problem of joint sparse support estimation with one-bit measurements in a distributed setting. Each node observes sparse signals with …
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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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Optimized Image Compressed Sensing And Transmission Through Wireless Channels
… thesis examines the robust behavior of quantized compressed sensing measurements during transmission through an additive white gaussian noise wireless channel. The poor rate-distortion performance that accompanies compressed sensing after applying quantization has led to several works in quantized …
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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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Analysis of weighted l̳₁-minimization for model based compressed sensing
The central problem of Compressed Sensing is to recover a sparse signal from fewer measurements than its ambient dimension. Recent results by Donoho, and Candes and Tao giving theoretical guarantees that ( 1-minimization succeeds in recovering the signal in a large number of cases have stirred up …
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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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Model and Data Reduction for Control, Identification and Compressed Sensing
… dynamic mode decomposition). Subsequently, a new compressed sensing based classification algorithm is developed which incorporates the extracted dynamic information into the sensing basis. We show that this augmented classification basis makes the method more robust to noise, and results in …
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A Compressed Sensing Approach to Detect Immobilized Nanoparticles Using Superparamagnetic Relaxometry
… a novel reconstruction algorithm based on compressed sensing methods that relies on only clinically feasible information. This approach is based on the hypothesis that the true distribution of cancer-bound nanoparticles consists of only a few highly-focal clusters around tumors and …
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Energy efficient compressed sensing in wireless sensor networks via random walk
… problem of data acquisition using compressive sensing (CS) in wireless sensor networks. Unique properties of wireless sensor networks require we minimize communication cost for efficient power usage. At first, a compressive distributed sensing (CDS) algorithm is proposed but is then modified to …
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Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications
… and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic …
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