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 87 for “"Compressive sensing"”.
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Compressive Sensing
… overview of certain key elements in the area of compressive sensing. As a sub-discipline of signal processing, compressive sensing is concerned with both sampling and reconstruction techniques. In this expository, sampling will center on random matrices and expander graphs, while reconstruction …
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Some Notes on Compressive Sensing
… problem is how we can store this amount of data. Compressive sensing is giving us 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 …
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A Cognitive Radio Compressive Sensing Framework
… and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, …
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Compressive sensing based imaging via belief propagation
Multiple description coding (MDC) using Compressive Sensing (CS) mainly aims at restoring an image from a small subset of samples with reasonable accuracy using an iterative message passing decoding algorithm commonly known as Belief Propagation (BP). The CS technique can accurately recover any …
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A compressive sensing algorithm for attitude determination
We propose a framework for compressive sensing of images with local distinguishable objects, such as stars, and apply it to solve a problem in celestial navigation. Specifically, let x [epsilon] RN be an N-pixel image, consisting of a small number of local distinguishable objects plus noise. Our …
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Compressive Sensing in Positron Emission Tomography (PET) Imaging
… this thesis , a mathematical technique known as compressive sensing is applied in an effort to decrease the number of detectors required, while maintaining good image quality. A CS model was developed based on a combination of gradient magnitude and wavelet domains to recover missing observations …
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Prior Information Guided Image Processing and Compressive Sensing
… (NL-means) denoising framework; enhancing the compressive sensing signal/image reconstruction with the guidance of prior information.The first topic is geometric information based image denoising, where we develop a segmentation boosted image denoising scheme, balancing the removal of excessive …
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Global optimization methods for localization in compressive sensing
The dissertation discusses compressive sensing and its applications to localization in multiple-input multiple-output (MIMO) radars. Compressive sensing is a paradigm at the intersection between signal processing and optimization. It advocates the sensing of "sparse" signals (i.e., represented …
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Advanced imaging via multiplexed sensing and compressive sensing
… on advanced imaging systems using multiplexed sensing and compressive sensing (CS). Conventional cameras (e.g., pin-hole and lens cameras) follow the one-object-point-to-one-image-point or one-to-one (OTO) mapping model. Multipled sensing and compressive sensing attempt to improve conventional …
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Parallelisation of greedy algorithms for compressive sensing reconstruction
Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as it is captured. The process of capturing and simultaneously compressing the signal is represented as linear sampling, which can encompass a variety of physical processes or signal processing. Instead …
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From bits to information : learning meets compressive sensing
A quantization approach to supervised learning, compressive sensing, and phase retrieval is presented in this thesis. We introduce a set of common techniques that allow us, in those three settings, to represent high dimensional data using the order statistics of linear and non linear measurements. …
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Compressive sensing based non-destructive testing using ultrasonic arrays.
In this thesis, we apply 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 …
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STUDY OF ADAPTIVE COMPRESSIVE SENSING FOR LOW POWER APPLICATIONS
Compressive sensing (CS) technique potentially allows sparse signals to be sampled at rates lower than their Nyquist Rates, making it appealing for implementation of low-power sensors. This dissertation investigates techniques to further improve CS efficiency by adaptively adjusting the sampling …
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Enhancing land seismic data with compressive sensing and processing
… seismic data quality. In this thesis, I research compressive sensing approaches to reduce the number of sensors required for non-aliased recordings of land wavefields and methods to improve regularly sampled aliased data. I consider a multi-channel extension of compressive sensing using both …
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Machine Learning and Bayesian Statistics for Seismic Compressive Sensing
… Modern algorithms utilise the principle of Compressive Sensing (CS) for reconstruction which uses the assumption that the signal of interest is either sparse in nature or in some other bases. Most algorithms are designed with the only aim to fill in gaps in the data without any consideration …
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Model-based compressive sensing with Earth Mover's Distance constraints
In compressive sensing, we want to recover ... from linear measurements of the form ... describes the measurement process. Standard results in compressive sensing show that it is possible to exactly recover the signal x from only m ... measurements for certain types of matrices. Model-based …
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COMPRESSIVE SENSING BASED IMAGE RECONSTRUCTION FOR COMPUTED TOMOGRAPHY DOSE REDUCTION
Excessive radiation exposure is one of the major concerns in the computed tomography (CT) field. Few-view reconstruction using iterative algorithm is an important strategy to reduce the radiation dose. In the iterative CT reconstruction, the projection / backprojection model plays an important role …
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Assessing the benefits of DCT compressive sensing for computational electromagnetics
… enough for our technologically advancing world. Compressive sensing theory states that signals, such as those used in computational electromagnetic problems have a property known as sparseness. It has been proven that through under sampling, computation runtimes can be substantially decreased …
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Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI)
… acquisition process due to physical limitations. Compressive Sensing (CS) is a recently developed mathematical framework that o ers signi cant bene ts in MRI image speed by reducing the amount of acquired data without degrading the image quality. The process of image reconstruction involves …
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