An Adaptive Prediction-Correction Method for Solving Large-Scale Nonlinear Systems of Monotone Equations with Applications by Gaohang Yu, Shanzhou Niu, Jianhua Ma, Yisheng Song

“…Some practical applications of the proposed method are demonstrated and tested on sparse signal reconstruction, compressed sensing, and image deconvolution problems.…”
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A Sparsity Preestimated Adaptive Matching Pursuit Algorithm by Xinhe Zhang, Yufeng Liu, Xin Wang

“…In the matching pursuit algorithm of compressed sensing, the traditional reconstruction algorithm needs to know the signal sparsity. …”
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Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks by Chengtie Li, Jinkuan Wang, Mingwei Li

“…Firstly, congestion control and link allocation are separately provided at transport layer and network layer, by supply and demand based on compressed sensing (CS). Secondly, we propose the cross-layer scheme to minimize the power cost of the whole network by a linear optimization problem. …”
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Channel Estimation in DCT-Based OFDM by Yulin Wang, Gengxin Zhang, Zhidong Xie, Jing Hu

“…We also study a compressed sensing (CS) based channel estimation, which takes the sparse property of wireless channel into account. …”
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A Novel Decentralized Scheme for Cooperative Compressed Spectrum Sensing in Distributed Networks by Huang Jijun, Zha Song

“…Compressed sensing (CS) recently turns out to be an effective approach to alleviate the sampling bottleneck in wideband spectrum sensing. …”
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Efficient high‐speed framework for sparse representation‐based iris recognition by Michael Melek, Mohamed F. Abu‐Elyazeed, Ahmed Khattab

“…To considerably improve classification performance, SRC is proposed, using a greedy compressed‐sensing recovery algorithm, as opposed to employing the traditional computationally expensive ℓ1 minimisation. …”
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Random Frequency Division Multiplexing by Chanzi Liu, Jianjian Wu, Qingfeng Zhou

“…In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. …”
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Pilot Design for Sparse Channel Estimation in Large-Scale MIMO-OFDM System by Chao Xu, Jianhua Zhang, Mengmeng Liu, Changchuan Yin

“…The pilot design problem in large-scale multi-input-multioutput orthogonal frequency division multiplexing (MIMO-OFDM) system is investigated from the perspective of compressed sensing (CS). According to the CS theory, the success probability of estimation is dependent on the mutual coherence of the reconstruction matrix. …”
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Doppler Ambiguity Resolution Based on Random Sparse Probing Pulses by Yunjian Zhang, Zhenmiao Deng, Jianghong Shi, Linmei Ye, Maozhong Fu, Chen Zhao

“…A novel method for solving Doppler ambiguous problem based on compressed sensing (CS) theory is proposed in this paper. …”
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Distributed Compressed Video Sensing in Camera Sensor Networks by Yu Liu, Xuqi Zhu, Lin Zhang, Sung Ho Cho

“…In such scenarios, distributed compressed video sensing (DCVS), combining distributed video coding (DVC) and compressed sensing (CS), is developed as a novel and powerful signal-sensing and compression algorithm for video signals. …”
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Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning by Hua Xiao, Zhongliang Wang, Xueying Cui, Liping Wang, Hongsheng Yang, Yingbiao Jia

“…Extensive experimental results on five real hyperspectral datasets demonstrate that the proposed spectral library learning, abundance initialization, and reconstruction strategy can effectively improve the compressed sensing reconstruction accuracy, outperforming the existing state-of-the-art methods.…”
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IM-OFDM ISAC Outperforms OFDM ISAC by Combining Multiple Sensing Observations by Hugo Hawkins, Chao Xu, Lie-Liang Yang, Lajos Hanzo

“…The existing solutions either insert a radar signal into the deactivated subcarriers, thereby using a radar signal for sensing, or employ compressed sensing, which leads to a lower sensing performance than OFDM ISAC. …”
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Conditional diffusion-generated super-resolution for myocardial perfusion MRI by Changyu Sun, Changyu Sun, Neha Goyal, Yu Wang, Darla L. Tharp, Senthil Kumar, Talissa A. Altes

“…While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction.MethodsThis study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. …”
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UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. …”
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Zero-effort projection for sensory data reconstruction in wireless sensor networks by Xiancun Zhou, Haibo Ling

“…Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. …”
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Sparse Recovery for Bistatic MIMO Radar Imaging in the Presence of Array Gain Uncertainties by Jun Li, Shengqi Zhu, Xixi Chen, Li Lv, Guisheng Liao, Menglei Yi

“…The imaging is then performed by compressive sensing method with consideration of both the transmit and receive array gain uncertainties. …”
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Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization by Yi Zhang, Weihua Zhang, Jiliu Zhou

“…Sparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. …”
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Improved Sparse Channel Estimation for Cooperative Communication Systems by Guan Gui, Wei Peng, Ling Wang

“…At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. …”
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A Three-Phase Top- Query Based Distributed Data Collection Scheme in Wireless Sensor Networks by Guorui Li, Jingsha He, Cong Wang

“…Simulation results show that there is no obvious difference in the performance of data reconstruction between our proposed scheme and existing compressive sensing theory based data collection schemes. …”
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Design of Fixed Beamformers Based on Vector-Sensor Arrays by Matthew Hawes, Wei Liu

“…We propose solving this problem by converting the traditional l1 norm minimisation associated with compressive sensing into a modified l1 norm minimisation which simultaneously minimises all four parts of the quaternionic weight coefficients. …”
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