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Showing 1 to 12 of 12 for “"Sparse Signal Recovery"”.
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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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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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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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In pursuit of high resolution radar using pursuit algorithms
… employ matched filters designed to maximize signal to noise ratio (SNR) in a single target environment. In a multi-target environment, however, matched filter estimates of target environment often consist of spurious targets because of radar signal sidelobes. As a result, matched filters are …
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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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Compressive sensing based non-destructive testing using ultrasonic arrays.
… compressive sensing approach and the notion of sparse signal recovery to the non-destructive testing application, using ultrasonic arrays. In many signal processing applications including array signal processing, there is a remarkable effort to use the concept of sparsity to solve an …
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Phase Retrieval of Sparse Signals from Magnitude Information
The ability to recover the phase information of a signal of interest from a measurement process plays an important role in many practical applications. When only the Fourier transform magnitude of the signal is recorded, recovering the complete signal from these nonlinear measurements turns into a …
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Sparse modeling of high-dimensional data for learning and vision
Sparse representations account for most or all of the information of a signal by a linear combination of a few elementary signals called atoms, and have increasingly become recognized as providing high performance for applications as diverse as noise reduction, compression, inpainting, compressive …
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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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New group testing paradigms: from practice to theory
We propose a novel group testing framework, termed semi-quantitative group testing, motivated by a class of problems arising in genome screening experiments in addition to other applications such as interpretable rule learning for decision making. Semi-quantitative group testing (SQGT) is a …