Adaptive Lossy Color Image Compression System Based on Hybrid Algorithm by Husam Khalid Khammas, Ayça Kurnaz Türkben

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High-Ratio Nonlinear Compression of Picosecond Lasers Based on Thin Plates by Kainan Zhou, Zhaoli Li, Yanlei Zuo, Zhaohui Wu, Xiaoming Zeng, Xiao Wang, Xiaodong Wang, Jie Mu, Guoying Feng

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Automatic Compressive Sensing of Shack–Hartmann Sensors Based on the Vision Transformer by Qingyang Zhang, Heng Zuo, Xiangqun Cui, Xiangyan Yuan, Tianzhu Hu

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Reconstruction of Harmonic and Transient Electrical Signals Through Compressed Sensing Technique by Oscar Vite, Felipe Uribe, C. A. Lopez de Alba

“…The problem here is that when a very high sampling rate is used with a digital phosphor oscilloscope for the precise recording of the phenomenon, due to uncertainty at the trigger time, or by setting a wide recording aperture on the time scale, the process of extracting information for further signal processing becomes a problem of a lot of information that requires a lot of computing time and computational resources. …”
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Analysis on the DOA estimation of uniform circular arrays based on compressive sensing by Lin-shu HUANG, Hao CHA, Hui-juan YE, Kai XU

“…In order to alleviate the serious problem of the low angle resolution after miniaturizing the antenna array of the surface wave radar,the compressed sensing method is proposed for radar antenna array angle estimation method.The sparse signals model is established,the application conditions are analyzed,and the measurement matrix is designed based on the real-time sea state information.By using the matching algorithm,the signal reconstruction is carried on.Simulation results show that if it satisfies the reconstruction condition,the azimuth resolution is improved by the method in the cost of the calculating resources.…”
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Distributed variational sparse Bayesian compressed sensing based on factor graphs by Cui-tao ZHU, Fan YANG, Han-xin WANG, Zhong-jie LI

“…A distributed variational sparse Bayesian compressed spectrum sensing algorithm based on factor graph was proposed,which decomposed the global spectrum sensing problem into local problem based on factor and variation.Belief propagation was used for the statistical inference of the spectrum occupancy,to implement the “soft fusion”.The temporal and spatial correlation information providing two-dimensional redundancies was exchanged among cooperative cognitive users to improve the detection performance under low SNR.Meanwhile,the algorithm prunes the divergence of hyper-parameters and the corresponding basis functions for reducing the load of communication.The simulation results show that this method can effectively achieve performance of spectrum sensing under a low sampling rate and the low SNR.…”
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1 bit Compressive Spectrum Sensing Algoritbm Based on Distributed Model by Zhijin Zhao, Weikang Hu, Junwei Hu

“…Since the actual sparsity of spectrum is unknown and time-varying, information transmit frequently between nodes in the distributed spectrum sensing network consumes communication bandwidth. …”
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Joint SAR–Optical Image Compression with Tunable Progressive Attentive Fusion by Diego Valsesia, Tiziano Bianchi

“…Remote sensing tasks, such as land cover classification, are increasingly becoming multimodal problems, where information from multiple imaging devices, complementing each other, can be fused. …”
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Vessel Trajectory Data Compression Algorithm considering Critical Region Identification by Xinliang Zhang, Shibo Zhou

“…In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. …”
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An impact of tensor-based data compression methods on deep neural network accuracy by Jakub Grabek, Bogusław Cyganek

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A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method by Saeed Mehri, Navid Hooshangi, Navid Mahdizadeh Gharakhanlou

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Quantization Parameter Cascading for Lossy Point Cloud Attribute Compression in G-PCC by Lei Wei, Zhiwei Zhu, Zhecheng Wang, Shuai Wan

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A Robust Method Based on Deep Learning for Compressive Spectrum Sensing by Haoye Zeng, Yantao Yu, Guojin Liu, Yucheng Wu

“…In cognitive radio, compressive spectrum sensing (CSS) is critical for efficient wideband spectrum sensing (WSS). …”
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Investigation of the geometry changes of body legs with compression stocking in static position by Olena Kyzymchuk, Yordan Kyosev, Liudmyla Melnyk, Natalija Sadretdinova

“…The high speed (4D) body scanning provides technical possibility for accurate measurements of the body geometry and can be applied not only for moving objects, but as well for evaluating the changes of static geometry with time. This information is important for compression socks, because it provides data regarding the relaxation processes of the textile product and of the human leg. …”
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Compression of 3D Optical Encryption Using Singular Value Decomposition by Kyungtae Park, Min-Chul Lee, Myungjin Cho

“…By leveraging this property, the encrypted data generated by DRPE can be effectively compressed. However, this compression may lead to some loss of information in the decrypted data. …”
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Numerical Investigation and Mold Optimization of the Automobile Coat Rack Compression Molding by Youmin Wang, Xiangli Li, He Sui

“…The load mapping is used as the boundary condition of mold topology optimization, and the compression molding of the main body of the coat rack is optimized. …”
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Determination of Degradation and Compressive Strength of Polyurethane Resin Applied for Bridge Abutment by Šarūnas Skuodis, Neringa Dirgėlienė, Mindaugas Zakarka, Dovilė Vasiliauskienė

“…After the degradation tests, compressibility tests were also performed. The use of resin as a ground improvement method has several advantages over conventional methods. …”
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RAT-CC: A Recurrent Autoencoder for Time-Series Compression and Classification by Giacomo Chiarot, Sebastiano Vascon, Claudio Silvestri, Idoia Ochoa

“…RAT-CC leverages a Long Short-Term Memory (LSTM) recurrent autoencoder with a dual-loss function: the standard reconstruction loss to minimize reconstruction error; and an embedding loss to preserve relative distances in the compressed embedding space. This combined loss ensures that the learned embeddings remain meaningful for classification tasks while preserving the necessary information for reconstruction. …”
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The impact of water content and ionic diffusion on the uniaxial compressive strength of shale by Talal AL-Bazali

“…Regression analysis was used in this work to develop a general equation for predicting uniaxial compressive strength of shale from the available information on its water content and dry uniaxial compressive strength. …”
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The q-Dixon sequence for MRI predicts osteoporotic vertebral compression fractures by Jing Zhang, Qiyuan Li, Yao Wang, Li Sun, Qingyuan Zhang, Chuanping Gao

“…ObjectivesTo evaluate whether q-Dixon sequence-based fat fraction (FF) values of the lumbar spine can predict osteoporotic vertebral compression fracture (OVCF) risk in older adult(s) osteoporosis patients.Materials & methodsThirty OVCF patients and 15 osteoporosis patients were enrolled. …”
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