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Showing 1 to 20 of 20 for “"Compressive sensing (CS)"”.
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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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Energy efficient compressed sensing in wireless sensor networks via random walk
… we explore the 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 …
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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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Infrastructure for large-scale tests in marine autonomy
… and the design of sampling trajectories for compressive sensing (CS). The newly developed infrastructure includes a bare-bones acoustic modem and two types of low-cost and scalable vehicles. One vehicle is a holonomic raft designed for station-keeping and precise maneuvering, and the other is …
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Empirical rate-distortion study of compressive sensing-based joint source-channel coding
… study of a communication scheme that uses compressive sensing (CS) as joint source-channel coding. We investigate the rate-distortion behavior of both point-to-point and distributed cases. First, we propose an efficient algorithm to find the 4-norm regularization parameter that is required …
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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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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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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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Phase Retrieval of Sparse Signals from Magnitude Information
… large number of measurements. By using compressive sensing (CS) techniques, the number of measurements required for phase retrieval can be reduced with the additional information pertaining to the signal structure. With the aim of reducing the number of measurements, this dissertation …
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Low Latency Compressive Sensing using Multi-Resolution Analysis In Radar Signal Processing
… the Nyquist rate (sub-Nyquist). In recent years, compressive sensing (CS) has come to light as a new signal processing paradigm. CS exploits signal sparsity characteristics to acquire the signal using a number of samples much lower than the Nyquist rate. Our focus is studying CS and its …
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Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array
Compressive sensing (CS) also known as compressive sampling is a technique used to reconstruct or recover the full-length of a signal with only a few non-adaptive measurements. It is a model-based framework for data acquisition and signal recovery that is based on the principles of sparsity and …
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Compressive Sensing Approaches for Sensor based Predictive Analytics in Manufacturing and Service Systems
Recent advancements in sensing technologies offer new opportunities for quality improvement and assurance in manufacturing and service systems. The sensor advances provide a vast amount of data, accommodating quality improvement decisions such as fault diagnosis (root cause analysis), and real-time …
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Compressive sensing of images and video: towards low-complexity, real-time operation
Compressive Sensing (CS) acquires sparse signals with far fewer measurements than samples required by the classical Nyquist sampling theorem, at the cost of more computationally intensive reconstruction. Video Block Compressive Sensing (VBCS), using a Multi Pixel Camera (MPC), divides the sensed …
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Dictionary learning for scalable sparse image representation
… motivated by the main perception characteristics of the Human Visual System (HVS) mechanism. Specifically, its core structure relies on the exploitation of the spatial high-frequency image components and contrast variations in order to achieve visual scene objects identification at all scalable …
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Two New Applications of Tensors to Machine Learning for Wireless Communications
… to obtain their low-dimensional estimates using compressive sensing (CS)-based technique and transmit to the server for joint training of the CNN. We exploit a natural tensor structure offered by the convolutional gradients to demonstrate the correlation of a gradient element with its neighbors. …
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Compressive Detection and Estimation with Applications to Cognitive Radio and Radar
… cannot be achieved due to hardware limitations. Compressive sensing (CS) is a technique to reconstruct a signal from sub-Nyquist samples, given that the signal is sparse in a known domain. The CS technique has been applied to different areas in the field of communications and networking. Of …
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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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A spin on compressive sensing imaging : reticle-based single-pixel imaging system
… number of columns in the image and the use of compressive sensing techniques was investigated as imaging of the entire scene in one reticle rotation was desired. compressive sensing (CS) is a signal acquisition technique to recover a sparse vector from only a few linear measurements. CS assumes …
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Optimal spectral reconstructions from deterministic and stochastic sampling geometries using compressive sensing and spectral statistical models
… optimal image reconstruction framework based on Compressive Sensing (CS) techniques and a new, Spectral Statistical approach based on the use of isotropic models over a dyadic partitioning of the spectrum. The proposed methods are demonstrated in applications in reconstructing fMRI and remote …
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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 …