Results 11 to 20 of about 136,882 (282)
An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher
Entropy, 2022 In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption.
Xiaomeng Song +3 more
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Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing
Sensors, 2020 The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations.
Zhongliang Wang, Hua Xiao
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Hierarchical Compressed Sensing
, 2022 Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this
Jens Eisert +4 more
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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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A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing
Remote Sensing, 2021 The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing.
Shaofei Dai +3 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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Sensors, 2023 High-definition images covering entire large-scene construction sites are increasingly used for monitoring management. However, the transmission of high-definition images is a huge challenge for construction sites with harsh network conditions and scarce
Tuocheng Zeng +4 more
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Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
Applied Sciences, 2022 Data transmission and storage are inseparable from compression technology. Compressed sensing directly undersamples and reconstructs data at a much lower sampling frequency than Nyquist, which reduces redundant sampling.
Han Diao, Xiaozhu Lin, Chun Fang
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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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Surface Measurement Using Compressed Wavefront Sensing
Photonic Sensors, 2018 Compressed sensing leverages the sparsity of signals to reduce the amount of measurements required for its reconstruction. The Shack-Hartmann wavefront sensor meanwhile is a flexible sensor where its sensitivity and dynamic range can be adjusted based on
Eddy Mun Tik Chow +3 more
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