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 46 for “"sparse signal"”.
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Advances in sparse signal recovery methods
… of data is ubiquitous; it has applications in signal acquisition, data compression, sub-linear space algorithms, etc. In this thesis we focus on sparse recovery, where the goal is to recover sparse vectors exactly, and to approximately recover nearly-sparse vectors. More precisely, from the …
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l0 Sparse signal processing and model selection with applications
Sparse signal processing has far-reaching applications including compressed sensing, media compression/denoising/deblurring, microarray analysis and medical imaging. The main reason for its popularity is that many signals have a sparse representation given that the basis is suitably selected. …
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Adaptive nonlocal and structured sparse signal modeling and applications
"Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. Many applications such as image and video denoising, inpainting, demosaicing, super-resolution, magnetic resonance imaging (MRI), and …
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Random observations on random observations: Sparse signal acquisition and processing
In recent years, signal processing has come under mounting pressure to accommodate the increasingly high-dimensional raw data generated by modern sensing systems. Despite extraordinary advances in computational power, processing the signals produced in application areas such as imaging, video, …
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Sparse signal representation based algorithms with application to ultrasonic array imaging
… the problem of mode conversion. We propose a sparse signal representation based method for imaging solid materials in the presence of mode conversion phenomenon. In the case of two-layer imaging we model the signal propagation effect using Huygens principle and Rayleigh-Sommerfeld diffraction …
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A sparse signal reconstruction perspective for source localization with sensor arrays
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques
… detecting the presence of interference in weak signal measurements. This thesis presents a new method for confirming the source of detected energy in weak signal measurements by sampling them directly, then estimating their expected effects. First, we assume that the detected signal is located …
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Analysis of weighted l̳₁-minimization for model based compressed sensing
… 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 much …
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Mathematical analysis of a dynamical system for sparse recovery
… analysis of a continuous-times system for sparse 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 …
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Nonadaptive lossy encoding of sparse signals
At high rate, a sparse signal is optimally encoded through an adaptive strategy that finds and encodes the signal's representation in the sparsity-inducing basis. This thesis examines how much the distortion rate (D(R)) performance of a nonadaptive encoder, one that is not allowed to explicitly …
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Compressive sensing based imaging via belief propagation
… can accurately recover any compressible or sparse signal from a lesser number of non-adaptive, randomized linear projection samples than that specified by the Nyquist rate. In this work, we demonstrate how CS-based encoding generates measurements from the sparse image signal and the …
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Image reconstruction through polyfiltered variation minimization
… in both the theory and available software for sparse signal reconstruction through function minimization make it an attractive approach for recreating the missing frequency data. It is the aim of this thesis to generalize the reconstruction technique known as Total Variation (TV) minimization …
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Jump Variation in High-Frequency Asset Returns: New Estimation Methods
… 2005) for their discrete Bayesian model of a sparse signal plus noise and argue for its use as a model of high-frequency asset returns with jumps. Within this model, we derive optimal estimators of jump variation and integrated variance. In a simulation analysis, we find outperformance of our …
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Some Notes on Compressive Sensing
… a clue about how we can reconstruct images and signals from frequency data, by having less samples compared to the conventional ways of data acquisition, which somehow helps us with the storage problem and gives us some other benefits that we try to present in this thesis. The basic principle of …
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Bayesian Adjustment for Multiplicity
… Chapter 6 introduces a new approach to sparse-signal modeling based upon local shrinkage rules. Here the focus is not on multiplicity per se, but rather on using ideas from Bayesian multiple-testing models to motivate a new class of multivariate scale-mixture priors. Finally, Chapter 7 …
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Decentralized signal processing systems with conservation principles
… algorithms realized as asynchronous, distributed signal processing systems is developed with an emphasis on the system's stability, robustness, and variational properties. These systems are formed by connecting basic modules together via interconnecting networks. Several classes of systems are …
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Super Greedy Type Algorithms and Applications In Compressed Sensing
… serves as one of the fundamental tools in sparse signal recovery. Using the super-greedy 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 …
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Image super-resolution via sparse representation
… to single-image super-resolution (SR), based on sparse signal recovery. Research on image statistics suggests that image patches can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse …
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Computational imaging with small numbers of photons
… be accurately solved using novel variations on sparse signal pursuit methods and regularized convex optimization techniques. We demonstrate our frameworks' photon efficiencies in six imaging scenarios that have been well-studied in the classical settings with large numbers of photon detections: …
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Graphical models and message passing receivers for interference limited communication systems
… decoding, approximate message passing, and sparse signal recovery for joint channel/interference estimation and data decoding. The resulting receivers provide huge improvements in communication performance (more than 10dB) over the conventional receivers at a comparable computational …
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