Results 21 to 30 of about 35,538 (312)
Sensors, 2020 With the widespread application of wireless sensor networks, large-scale systems with high sampling rates are becoming more and more common. The amount of original data generated by the wireless sensor network is very large, and transmitting all the ...
Youtian Qie, Chuangbo Hao, Ping Song
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Compressed Sensing with nonlinear observations and related non-linear optimisation problems [PDF]
, 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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Chinese Journal of Magnetic Resonance, 2018 Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data.
CHAI Qing-huan +2 more
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Research on LFM signal parameter estimation method based on Gabor transform to improve MWC system
AIP Advances, 2023 The “compressed sensing” theory is the foundation for the compressed sampling system’s design. In addition to the sparse representation and observation matrix, more studies in compressed sensing theory focus on signal reconstruction and recovery.
Shuo Meng, Chen Meng, Cheng Wang
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Multi-signal Compressed Sensing For Polarimetric SAR Tomography [PDF]
, 2011 In recent years, three-dimensional imaging by means of SAR tomography has become a field of intensive research. In SAR tomography, the vertical reflectivity function for every azimuth-range pixel is usually recovered by processing data collected using a ...
Aguilera, Esteban Pedro +6 more
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Stochastic Parameterization Using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model
Tellus: Series A, Dynamic Meteorology and Oceanography, 2022 Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing.
A. Mukherjee +3 more
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Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas [PDF]
, 2012 Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a ...
Nannini, Matteo +2 more
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EEG Emotion Recognition Based on Deep Compressed Sensing
Taiyuan Ligong Daxue xuebao, 2023 Purposes Deep compressed sensing is the use of deep learning to solve the problems existing in traditional compressed sensing, such as the adaptability of observation matrix to traditional signal compression and the dependency on dictionary by ...
Jinxin FENG +5 more
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A remark on Compressed Sensing [PDF]
Mathematical Notes, 2007 A classical problem in signal processing is the recovery problem: One is interested in reconstructing a vector \(u \in \mathbb R^m\) from given linear functionals \((u, \phi_j)\), \(j = 1, 2, \ldots, n\), with some known values \(\phi_1, \ldots, \phi_n \in \mathbb R^m\). In most typical applications, \(n\) is substantially smaller than \(m\).
Kashin, B. S., Temlyakov, V. N.
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Efficient distributed storage strategy based on compressed sensing for space information network
International Journal of Distributed Sensor Networks, 2016 This article investigates the distributed data storage problem with compressed sensing in the space information network. Since there exists a performance-energy trade-off, most existing strategies focus only on improving the compressed sensing ...
Bo Kong +4 more
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