Results 41 to 50 of about 2,561,650 (296)
SVM Intrusion Detection Model Based on Compressed Sampling
Journal of Electrical and Computer Engineering, 2016 Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data.
Shanxiong Chen +3 more
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Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
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Sample Distortion for Compressed Imaging [PDF]
IEEE Transactions on Signal Processing, 2013 We propose the notion of a sample distortion (SD) function for independent and identically distributed (i.i.d) compressive distributions to fundamentally quantify the achievable reconstruction performance of compressed sensing for certain encoder-decoder pairs at a given sampling ratio.
Chunli Guo, Mike E. Davies 0001
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BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING
Forum of Mathematics, Sigma, 2017 This paper presents a framework for compressed sensing that bridges a gap between existing theory and the current use of compressed sensing in many real-world applications.
BEN ADCOCK +3 more
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An Enhanced Photonic-Assisted Sampling Approach for Spectrum-Sparse Signal by Compressed Sensing
IEEE Access, 2022 Spectrum-sparse signals are vital for wideband radar and wireless communication applications. A high-speed analog-to-digital converter (ADC) with the capacities of tens of gigahertz sampling rates is often required to acquire these signals. In this work,
Fangxing Lyu +4 more
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A survey of potential sampling strategies for measurement of structured surfaces. [PDF]
, 2009 Uniform sampling is the predominant sampling method for surface measuring instruments. However, measurement for structured surfaces brings an increasingly serious conflict between sampling range and small resolution.
Wang, J, Blunt, Liam, Jiang, Xiang
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Fault Diagnosis of a Propeller Using Sub-Nyquist Sampling and Compressed Sensing
IEEE Access, 2022 The fault diagnosis of rotating machinery is generally performed using methods that employ vibration and sound. These methods are simple and accurate. However, all of these methods measure vibration data on the basis of the sampling theorem.
Yuki Kato
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Low Complexity Cyclic Feature Recovery Based on Compressed Sampling
International Journal of Distributed Sensor Networks, 2015 To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be
Zhuo Sun +4 more
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Imaging via Compressive Sampling [PDF]
IEEE Signal Processing Magazine, 2008 Image compression algorithms convert high-resolution images into a relatively small bit streams in effect turning a large digital data set into a substantially smaller one. This article introduces compressive sampling and recovery using convex programming.
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Compressed Sensing with nonlinear observations and related non-linear optimisation problems
, 2013 Non-convex constraints have recently proven a valuable tool in many optimisation problems. In particular sparsity constraints have had a significant impact on sampling theory, where they are used in Compressed Sensing and allow structured signals to be ...
Blumensath, Thomas
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