Results 31 to 40 of about 1,002,666 (308)
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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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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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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Compressed sensing applied to modeshapes reconstruction [PDF]
, 2011 Modal analysis classicaly used signals that respect the Shannon/Nyquist theory. Compressive sampling (or Compressed Sampling, CS) is a recent development in digital signal processing that offers the potential of high resolution capture of physical ...
Dimitri Bettebghor +3 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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