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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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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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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Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission
Applied Sciences, 2019 Compressed sensing is well known for its superior compression performance, in existing schemes, in lossy compression. Conventional research aims to reach a larger compression ratio at the encoder, with acceptable quality reconstructed images at the ...
Hsiang-Cheh Huang +2 more
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Leaf Classification for Crop Pests and Diseases in the Compressed Domain
Sensors, 2022 Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately.
Jing Hua, Tuan Zhu, Jizhong Liu
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Computational Complexity versus Statistical Performance on Sparse Recovery Problems [PDF]
, 2018 We show that several classical quantities controlling compressed sensing performance directly match classical parameters controlling algorithmic complexity.
Boumal, Nicolas +2 more
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Sensors, 2021 Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind ...
Yan Wang +3 more
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On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis [PDF]
, 2015 Despite the linearity of its encoding, compressed sensing may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dimensional measurements (ciphertexts).
Cambareri, Valerio +4 more
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Compressed Measurements Based Spectrum Sensing for Wideband Cognitive Radio Systems
International Journal of Antennas and Propagation, 2015 Spectrum sensing is the most important component in the cognitive radio (CR) technology. Spectrum sensing has considerable technical challenges, especially in wideband systems where higher sampling rates are required which increases the complexity and ...
Taha A. Khalaf +2 more
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Recovery of binary sparse signals from compressed linear measurements via polynomial optimization [PDF]
, 2019 The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics.
Abuabiah, Mohammad, Fosson, Sophie M.
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