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Journal articles on the topic 'Hyper-compression'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Wenbin, Wu, Yue Wu, and Jintao Li. "The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*." MATEC Web of Conferences 173 (2018): 03071. http://dx.doi.org/10.1051/matecconf/201817303071.

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In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

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Wu, Wenbin, Yue Wu, and Xu Qiao. "Parallel Compression Based on Prediction Algorithm of Hyper-spectral Imagery." MATEC Web of Conferences 173 (2018): 03070. http://dx.doi.org/10.1051/matecconf/201817303070.

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Along with the development of the spectral imaging technology, the precision of the hyper-spectral imagery becomes very high, and the size of the hyper-spectral imagery becomes very large. In order to solve the problem of the transmission and the storage, it is necessary to research the compression algorithm. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. In this paper, we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. We select bands along the direction of spectral or the direction of space, so that the hyper-spectral imagery can be divided into sub images. We number the sub images, then send them to different processing units. Each unit does compression tasks at the same time. This paper also compares the relationship between the processing unit number and the compression time. The experiment shows that, the parallel predictive compression algorithm can improve the efficiency of compression effectively.

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3

JEŻ, ARTUR, and ANDREAS MALETTI. "HYPER-MINIMIZATION FOR DETERMINISTIC TREE AUTOMATA." International Journal of Foundations of Computer Science 24, no. 06 (September 2013): 815–30. http://dx.doi.org/10.1142/s0129054113400200.

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Hyper-minimization is a recent automaton compression technique that can reduce the size of an automaton beyond the limits imposed by classical minimization. The additional compression power is enabled by allowing a finite difference in the represented language. The necessary theory for hyper-minimization is developed for (bottom-up) deterministic tree automata. The hyper-minimization problem for deterministic tree automata is reduced to the hyper-minimization problem for deterministic finite-state string automata, for which fast algorithms exist. The fastest algorithm obtained in this way runs in time [Formula: see text], where m is the size of the transition table and n is the number of states of the input tree automaton.

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Malachy, Ronan, Nicholas Ward, Robert Sazdov, and Hyunkook Lee. "The Perception of Hyper-Compression by Mastering Engineers." Journal of the Audio Engineering Society 65, no. 7/8 (August 15, 2017): 613–21. http://dx.doi.org/10.17743/jaes.2017.0023.

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5

Wen, Jia, Caiwen Ma, and Junsuo Zhao. "FIVQ algorithm for interference hyper-spectral image compression." Optics Communications 322 (July 2014): 97–104. http://dx.doi.org/10.1016/j.optcom.2014.02.016.

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6

Sucharitha, B., and Dr K. Anitha Sheela. "Compression of Hyper Spectral Images using Tensor Decomposition Methods." International Journal of Circuits, Systems and Signal Processing 16 (October 7, 2022): 1148–55. http://dx.doi.org/10.46300/9106.2022.16.138.

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Tensor decomposition methods have beenrecently identified as an effective approach for compressing high-dimensional data. Tensors have a wide range of applications in numerical linear algebra, chemo metrics, data mining, signal processing, statics, and data mining and machine learning. Due to the huge amount of information that the hyper spectral images carry, they require more memory to store, process and send. We need to compress the hyper spectral images in order to reduce storage and processing costs. Tensor decomposition techniques can be used to compress the hyper spectral data. The primary objective of this work is to utilize tensor decomposition methods to compress the hyper spectral images. This paper explores three types of tensor decompositions: Tucker Decomposition (TD_ALS), CANDECOMP/PARAFAC (CP) and Tucker_HOSVD (Higher order singular value Decomposition) and comparison of these methods experimented on two real hyper spectral images: the Salinas image (512 x 217 x 224) and Indian Pines corrected (145 x 145 x 200). The PSNR and SSIM are used to evaluate how well these techniques work. When compared to the iterative approximation methods employed in the CP and Tucker_ALS methods, the Tucker_HOSVD method decomposes the hyper spectral image into core and component matrices more quickly. According to experimental analysis, Tucker HOSVD's reconstruction of the image preserves image quality while having a higher compression ratio than the other two techniques.

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Hu, Yueyu, Wenhan Yang, and Jiaying Liu. "Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11013–20. http://dx.doi.org/10.1609/aaai.v34i07.6736.

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Approaches to image compression with machine learning now achieve superior performance on the compression rate compared to existing hybrid codecs. The conventional learning-based methods for image compression exploits hyper-prior and spatial context model to facilitate probability estimations. Such models have limitations in modeling long-term dependency and do not fully squeeze out the spatial redundancy in images. In this paper, we propose a coarse-to-fine framework with hierarchical layers of hyper-priors to conduct comprehensive analysis of the image and more effectively reduce spatial redundancy, which improves the rate-distortion performance of image compression significantly. Signal Preserving Hyper Transforms are designed to achieve an in-depth analysis of the latent representation and the Information Aggregation Reconstruction sub-network is proposed to maximally utilize side-information for reconstruction. Experimental results show the effectiveness of the proposed network to efficiently reduce the redundancies in images and improve the rate-distortion performance, especially for high-resolution images. Our project is publicly available at https://huzi96.github.io/coarse-to-fine-compression.html.

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8

Li, Jin, and Zilong Liu. "Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition." Open Physics 15, no. 1 (December 29, 2017): 992–96. http://dx.doi.org/10.1515/phys-2017-0123.

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AbstractNonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band. However, the use of a NTD has a very high computational cost. In this paper, we propose a low complexity NTD-based compression method of hyper-spectral images. This method is based on a pair-wise multilevel grouping approach for the NTD to overcome its high computational cost. The proposed method has a low complexity under a slight decrease of the coding performance compared to conventional NTD. We experimentally confirm this method, which indicates that this method has the less processing time and keeps a better coding performance than the case that the NTD is not used. The proposed approach has a potential application in the loss compression of hyper-spectral or multi-spectral images

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9

MALETTI, ANDREAS, and DANIEL QUERNHEIM. "UNWEIGHTED AND WEIGHTED HYPER-MINIMIZATION." International Journal of Foundations of Computer Science 23, no. 06 (September 2012): 1207–25. http://dx.doi.org/10.1142/s0129054112400485.

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Hyper-minimization of deterministic finite automata (DFA) is a recently introduced state reduction technique that allows a finite change in the recognized language. A generalization of this lossy compression method to the weighted setting over semifields is presented, which allows the recognized weighted language to differ for finitely many input strings. First, the structure of hyper-minimal deterministic weighted finite automata is characterized in a similar way as in classical weighted minimization and unweighted hyper-minimization. Second, an efficient hyper-minimization algorithm, which runs in time [Formula: see text], is derived from this characterization. Third, the closure properties of canonical regular languages, which are languages recognized by hyper-minimal DFA, are investigated. Finally, some recent results in the area of hyper-minimization are recalled.

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10

YIN Chuan-li, 尹传历, and 李嘉全 LI Jia-quan. "Embedded Hyper-Spectral Image Compression System Based on Bit-plane." Chinese Journal of Liquid Crystals and Displays 27, no. 2 (2012): 245–49. http://dx.doi.org/10.3788/yjyxs20122702.0245.

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11

Sin, Gwang-Cheol, and Sang-Yong Han. "Hyper-Text Compression Method Based on LZW Dictionary Entry Management." KIPS Transactions:PartA 9A, no. 3 (September 1, 2002): 311–16. http://dx.doi.org/10.3745/kipsta.2002.9a.3.311.

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12

WANG Di, 王娣, 邓家先 DENG Jia-xian, 邓小梅 DENG Xiao-mei, and 邓海涛 DENG Hai-tao. "Joint image compression and encryption algorithm using hyper-chaotic system." Optics and Precision Engineering 22, no. 9 (2014): 2528–35. http://dx.doi.org/10.3788/ope.20142209.2528.

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13

Xianyun Wu, Yunsong Li, Chengke Wu, and Li Wang. "Linear Prediction Based Hyper-spectral Imagery Compression Using Distributed Source Coding." Journal of Convergence Information Technology 7, no. 12 (July 31, 2012): 27–34. http://dx.doi.org/10.4156/jcit.vol7.issue12.4.

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14

Karmakar, J., D. Nandi, and M. K. Mandal. "A novel hyper-chaotic image encryption with sparse-representation based compression." Multimedia Tools and Applications 79, no. 37-38 (August 1, 2020): 28277–300. http://dx.doi.org/10.1007/s11042-020-09125-9.

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15

Wheatley, Benjamin B., Renée B. Pietsch, Tammy L. Haut Donahue, and Lakiesha N. Williams. "Fully non-linear hyper-viscoelastic modeling of skeletal muscle in compression." Computer Methods in Biomechanics and Biomedical Engineering 19, no. 11 (December 10, 2015): 1181–89. http://dx.doi.org/10.1080/10255842.2015.1118468.

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16

Tavakkoli Hosseini, Morteza, Aron Frederik Popov, Antonios Kourliouros, Andre Ruediger Simon, and Toufan Bahrami. "Left Atrial Compression by the Hyper-Expanded Right Lower Pulmonary Lobe." Annals of Thoracic Surgery 93, no. 1 (January 2012): e15. http://dx.doi.org/10.1016/j.athoracsur.2011.09.058.

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17

Chen, Bowen, Junwu Dai, and Zhipeng Shao. "Modeling of Hyper-Viscoelastic Properties of High-Damping Rubber Materials during the Cyclic Tension and Compression Process in the Vertical Direction." Polymers 14, no. 24 (December 9, 2022): 5395. http://dx.doi.org/10.3390/polym14245395.

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With the rapid development of the economy and urbanization, the construction of the urban rail transit system has had a great impact on the work, life, and health of residents in buildings along the rail transit line. Thus, it is particularly urgent and necessary to develop base isolation technologies to control and reduce the impact of vibrations of rail transit systems on building structures. High-damping rubber isolation bearings have shown significant effectiveness in the reduction of this impact, and their isolation performance mainly depends on the mechanical and damping energy dissipation characteristics of the high-damping rubber material. This paper aims to investigate the hyper-viscoelastic properties of the high-damping rubber material used for high-damping rubber isolation bearings during the cyclic tension and compression process in the vertical direction. These properties include hyperelastic parameters, viscoelastic coefficients, and the relaxation times of the material. For this purpose, uniaxial cyclic tension and compression tests were conducted. A three-element Maxwell rheological model combining a strain energy density function was proposed for modeling the hyper-viscoelastic behaviors of the materials during the cyclic tension and compression process. Based on the obtained results, an iterative identification procedure was used to determine the constitutive parameters of the material for each loading-unloading cycle. The aforementioned parameters were further expressed as a function of the number of cycles. New insights into hyper-viscoelastic property changes in this high-damping rubber material during the cyclic tension and compression process were gained in this work. These investigations could facilitate the development of computational tools, which would regulate fundamental guidelines for the better controlling and optimization of the isolation performance of the high-damping rubber material used for high-damping rubber isolation bearings, which have a wider perspective of applications in the urban rail transit system.

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18

Berre, Simon Le, Isabelle Ramière, Jules Fauque, and David Ryckelynck. "Condition Number and Clustering-Based Efficiency Improvement of Reduced-Order Solvers for Contact Problems Using Lagrange Multipliers." Mathematics 10, no. 9 (April 30, 2022): 1495. http://dx.doi.org/10.3390/math10091495.

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This paper focuses on reduced-order modeling for contact mechanics problems treated by Lagrange multipliers. The high nonlinearity of the dual solutions lead to poor classical data compression. A hyper-reduction approach based on a reduced integration domain (RID) is considered. The dual reduced basis is the restriction to the RID of the full-order dual basis, which ensures the hyper-reduced model to respect the non-linearity constraints. However, the verification of the solvability condition, associated with the well-posedness of the solution, may induce an extension of the primal reduced basis without guaranteeing accurate dual forces. We highlight the strong link between the condition number of the projected contact rigidity matrix and the precision of the dual reduced solutions. Two efficient strategies of enrichment of the primal POD reduced basis are then introduced. However, for large parametric variation of the contact zone, the reachable dual precision may remain limited. A clustering strategy on the parametric space is then proposed in order to deal with piece-wise low-rank approximations. On each cluster, a local accurate hyper-reduced model is built thanks to the enrichment strategies. The overall solution is then deeply improved while preserving an interesting compression of both primal and dual bases.

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19

Laureti, Stefano, Hamed Malekmohammadi, Muhammad Khalid Rizwan, Pietro Burrascano, Stefano Sfarra, Miranda Mostacci, and Marco Ricci. "Looking Through Paintings by Combining Hyper-Spectral Imaging and Pulse-Compression Thermography." Sensors 19, no. 19 (October 8, 2019): 4335. http://dx.doi.org/10.3390/s19194335.

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The use of different spectral bands in the inspection of artworks is highly recommended to identify the maximum number of defects/anomalies (i.e., the targets), whose presence ought to be known before any possible restoration action. Although an artwork cannot be considered as a composite material in which the zero-defect theory is usually followed by scientists, it is possible to state that the preservation of a multi-layered structure fabricated by the artist’s hands is based on a methodological analysis, where the use of non-destructive testing methods is highly desirable. In this paper, the infrared thermography and hyperspectral imaging methods were applied to identify both fabricated and non-fabricated targets in a canvas painting mocking up the famous character “Venus” by Botticelli. The pulse-compression thermography technique was used to retrieve info about the inner structure of the sample and low power light-emitting diode (LED) chips, whose emission was modulated via a pseudo-noise sequence, were exploited as the heat source for minimizing the heat radiated on the sample surface. Hyper-spectral imaging was employed to detect surface and subsurface features such as pentimenti and facial contours. The results demonstrate how the application of statistical algorithms (i.e., principal component and independent component analyses) maximized the number of targets retrieved during the post-acquisition steps for both the employed techniques. Finally, the best results obtained by both techniques and post-processing methods were fused together, resulting in a clear targets map, in which both the surface, subsurface and deeper information are all shown at a glance.

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20

Gu, Jing, and Dianxun Shuai. "The faster higher-order cellular automaton for hyper-parallel undistorted data compression." Journal of Computer Science and Technology 15, no. 2 (March 2000): 126–35. http://dx.doi.org/10.1007/bf02948796.

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21

Bai, Sen, Gui Bin Zhu, and Xiao Yong Ji. "Comments on “A Novel Image Encryption-Compression Scheme Using Hyper-Chaos and Chinese Remainder Theorem”." Applied Mechanics and Materials 743 (March 2015): 333–37. http://dx.doi.org/10.4028/www.scientific.net/amm.743.333.

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In a recent paper by Zhu and Zhao [1], an image encryption-compression scheme (IECS) based on hyper-chaos and Chinese remainder theorem (CRT) is proposed. This comment points out that in [1] there are some errors in two important algorithms and the performance of the proposed IECS is severely overestimated. It is shown that the IECS presented in paper [1] can be easily discredited and ipso facto cannot be used for compressing the plain image with a given compression ratio. Both theoretical analysis and experimental results are given to support our conclusion.

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22

Li, Teng, Le Mu, Xian Da Wang, Bo Feng, and Zi Yi Jin. "Contact Stress Analysis of Packer Rubber Based on ANSYS Software." Advanced Materials Research 860-863 (December 2013): 2966–69. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2966.

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In this paper three-rubber compression packers are regard as study objects, hyper-elastic constitution equation Yeoh has been used, the ANSYS mechanical model of packer rubber has been established by ANSYS software, the influence of axial setting force and frictional coefficient on the contact stress of packer rubber has been analyzed. The research results indicate that with the increasing of setting force, the axial compression amount and Von Mises stress increase; with the increasing of frictional coefficient between rubber and casing, the Von Mises stress of the intermediate rubber decreases slowly.

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23

TSENG, GUAN-HAO, CHENG-TAO CHANG, CHOU-CHING K. LIN, TERRY YUAN-FANG CHEN, and MING-SHAUNG JU. "EFFECTS OF DIABETES MELLITUS ON VISCOELASTICITY OF ULTRASTRUCTURES OF PERIPHERAL NERVES: THREE-DIMENSIONAL FINITE ELEMENT ANALYSES." Journal of Mechanics in Medicine and Biology 19, no. 04 (June 2019): 1950022. http://dx.doi.org/10.1142/s0219519419500222.

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Diabetes mellitus induces a variety of neuropathies and causes various symptoms. Understanding how diabetes affects mechanical properties of nerves is useful for preventing complications of diabetes mellitus such as the carpal tunnel syndrome. In a previous study, a two-dimensional hyper-viscoelastic finite element model (FEM) of the ultra-structures of normal rat sciatic nerves was developed using an optical coherence tomography (OCT) microscope and in vitro parallel compression tests. The main goal of this study was to extend the FEM from two to three dimensions and use it to explore hyper-viscoelasticity of ultra-structures of sciatic nerves of diabetic rats. A modification of the compression testing system to enhance OCT cross-sectional images of the nerve samples was also conducted. The results showed that the instantaneous shear moduli of the perineurium, epineurium, and endoneurium of the diabetic rat were all greater than those of the normal rats. Due to high instantaneous shear moduli and low percentage of relaxation, the diabetic nerve is prone to damage when subjected to prolonged mechanical loads.

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24

Zhou, Nanrun, Shumin Pan, Shan Cheng, and Zhihong Zhou. "Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing." Optics & Laser Technology 82 (August 2016): 121–33. http://dx.doi.org/10.1016/j.optlastec.2016.02.018.

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25

Wen, Jia, JunSuo Zhao, CaiWen Ma, and CaiLing Wang. "Improved non-negative tensor Tucker decomposition algorithm for interference hyper-spectral image compression." Science China Information Sciences 58, no. 5 (December 20, 2014): 1–9. http://dx.doi.org/10.1007/s11432-014-5165-x.

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26

Tong, Xiao-Jun, Miao Zhang, Zhu Wang, and Jing Ma. "A joint color image encryption and compression scheme based on hyper-chaotic system." Nonlinear Dynamics 84, no. 4 (February 4, 2016): 2333–56. http://dx.doi.org/10.1007/s11071-016-2648-x.

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27

Zhu, Hegui, Cheng Zhao, and Xiangde Zhang. "A novel image encryption–compression scheme using hyper-chaos and Chinese remainder theorem." Signal Processing: Image Communication 28, no. 6 (July 2013): 670–80. http://dx.doi.org/10.1016/j.image.2013.02.004.

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28

Lee, Whasil, Holly Leddy, Amy McNulty, Farshid Guilak, and Wolfgang Liedtke. "Inflammatory Cytokine Il-1α Up-Regulates Piezo1 and Hyper-Sensitizes Chondrocytes to Compression." Biophysical Journal 110, no. 3 (February 2016): 349a. http://dx.doi.org/10.1016/j.bpj.2015.11.1878.

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29

Xue, Xianglian, and Haiyan Jin. "A novel Image Security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2066/1/012011.

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Abstract This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.

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30

Luding, Stefan. "How does static granular matter re-arrange for different isotropic strain rate?" EPJ Web of Conferences 249 (2021): 10001. http://dx.doi.org/10.1051/epjconf/202124910001.

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The question of how soft granular matter, or dense amorphous systems, re-arrange their microstructure under isotropic compression and de-compression, at different strain rates, will be answered by particle simulations of frictionless model systems in a periodic three-dimensional cuboid. Starting compression below jamming, the systems experience the well known jamming transition, with characteristic evolutions of the state variables elastic energy, elastic stress, coordination number, and elastic moduli. For large strain rates, kinetic energy comes into play and the evolution is more dynamic. In contrast, at extremely slow deformation, the system relaxes to hyper-elastic states, with well-defined elastic moduli, in static equilibrium between irreversible (plastic) re-arrangement events, discrete in time. Small, finite strains explore those reversible (elastic) states, before larger strains push the system into new states, by irreversible, sudden re-arrangements of the micro-structure.

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31

Sun, Mingbo, and Shengbo Chen. "Deep Learning-Based Super-Resolution Reconstruction and Algorithm Acceleration of Mars Hyperspectral CRISM Data." Remote Sensing 14, no. 13 (June 25, 2022): 3062. http://dx.doi.org/10.3390/rs14133062.

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In Mars exploration, hyper-spectrometry plays an important role due to its high spectral resolution. However, due to the technical difficulty and the data size, the spatial resolution or the coverage of hyperspectral data is often limited. This limitation can be alleviated by deep learning-based super-resolution (SR) reconstruction. But the spatial size and batch size of the input training data is limited due to the large number of spectral channels. To improve the efficiency of model training and SR reconstruction, a dataset based on CRISM hyperspectral data is created in this paper, and its redundancy is analyzed in both spectral and spatial spital dimensions. Compression algorithms based on data selection and PCA are used to reduce the size of the input training data. A network that can perform spatial SR and spectral enhancement is also proposed to make the network can be trained with the compressed data. With these compression algorithms and network, high-resolution data with 235 bands can be reconstructed from the low-resolution data with only 40 bands. Compared with the network trained on the original low-resolution data with 235 bands, the model training time and the SR reconstruction runtime can be reduced to 30% and 23% with practically no accuracy loss. The effectiveness of compression algorithms based on data selection also indicates that maybe not all the bands need to be transmitted from the Mars probes or be collected. Furthermore, it would, in principle, help improve the efficiency of satellite data transmission and simplify the design of the hyper-spectrometer. Additionally, a method for spatial dimension correlation evaluation is also proposed in this paper. The spatial compression shows that the proposed method can reflect the correlation of spatial texture between patches, and the model can be acceptably trained with only half of the original data.

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Li, Huabing, Weichao Jiao, Hao Feng, Xinxu Li, Zhouhua Jiang, Guoping Li, Lixin Wang, Guangwei Fan, and Peide Han. "Deformation Characteristic and Constitutive Modeling of 2707 Hyper Duplex Stainless Steel under Hot Compression." Metals 6, no. 9 (September 12, 2016): 223. http://dx.doi.org/10.3390/met6090223.

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33

Thiyagarajan, Senthivel, Gnanadurai Dhavamani, and Somasundaram Malathi. "An Enhanced Hyper Spectral Image (HSI) Compression Based On Residual Dependent Arithmetic Coder (RDAC)." Current Medical Imaging Reviews 12, no. 3 (May 31, 2016): 178–88. http://dx.doi.org/10.2174/1573405612999160510174933.

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34

Gong, Lihua, Chengzhi Deng, Shumin Pan, and Nanrun Zhou. "Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform." Optics & Laser Technology 103 (July 2018): 48–58. http://dx.doi.org/10.1016/j.optlastec.2018.01.007.

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35

Aiazzi, B., P. Alba, L. Alparone, and S. Baronti. "Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction." IEEE Transactions on Geoscience and Remote Sensing 37, no. 5 (1999): 2287–94. http://dx.doi.org/10.1109/36.789625.

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36

Peshin, Saveliy, Yulia Karakulova, and Alex G. Kuchumov. "Finite Element Modeling of the Fingers and Wrist Flexion/Extension Effect on Median Nerve Compression." Applied Sciences 13, no. 2 (January 16, 2023): 1219. http://dx.doi.org/10.3390/app13021219.

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Carpal tunnel syndrome (CTS) is the most common pathology among disorders of the peripheral nervous system related to median nerve compression. To our knowledge, there are limited data on the effect of tendon movement on median nerve compression. This study focuses on the understanding of the carpal syndrome by simulating the impact of tendons movement caused by fingers flexion by Finite Element Analysis. Therefore, such modeling is the step toward the development of a personalized technique for value determining median nerve compression. Open-source MRI of the human right hand was used to build patient-specific phalanges of the fingers. Carpal tunnel soft tissues were considered as hyper-elastic materials, while bone structures were considered as elastic ones. The final finite-element model had 40 solid bodies which contacted the joint. Results were obtained for four cases of wrist movements: finger flexion, hand flexion/extension, and wrist extension with subsequent by finger flexion. Compression of the median nerve ranged from 129 Pa to 227 Pa. The results show that compression of the median nerve occurs faster during wrist flexion than during wrist extension or finger flexion. A decrease in compression during finger flexion was noticed with wrist extension followed by finger flexion.

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Zhao, Bin, Huan Wei, Yifeng Zhai, Jianmei Feng, and Xueyuan Peng. "Application of CFD Method to Investigate the Evolution of the Thermodynamic Parameters of a Hyper Compressor and Its Pipelines." Energies 15, no. 12 (June 18, 2022): 4452. http://dx.doi.org/10.3390/en15124452.

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Hyper compressors are key facilities for producing the low-density polyethylene with discharge pressure up to 350 MPa. Such high pressure brings great challenges to the design of the hyper compressor in many aspects. In this paper, a 3D transient computational fluid dynamics (CFD) model with inlet and outlet pipelines is built to investigate the thermodynamic performance of a hyper compressor. To realize the interaction between the thermodynamic processes and the pressure pulsation through valve dynamics, the pressures across the valve surfaces were monitored to the dynamic equation of the poppet valve. Then, structured grids were generated for the flow domain inside the valve, and the entire numerical model was solved by a commercial code: ANSYS Fluent. Consequently, the p-V diagram, the valve motion and pressure pulsation could be acquired simultaneously. The results of the numerical model showed that the exponents of the expansion and compression processes were 5.12 and 13.22, which were much larger than the common compressor. The maximal pressure pulsations were 13.25% and 22.07%, which occurred in the suction and discharge chambers, respectively. Severe flutter happened during the opening process of the suction valve due to the high incompressibility of the ethylene.

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KhaIiI Ibrahim Kadhim. "Principal Components Analysis as enhancement Operator and Compression factor." journal of the college of basic education 17, no. 72 (June 17, 2019): 25–33. http://dx.doi.org/10.35950/cbej.v17i72.4495.

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Principal components analysis (PCA) is effective at compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. Unfortunately, information content in hyper spectral images does not always coincide with such projections. We propose an application of projection pursuit (pp), which seeks to find a set of projections that are "interesting" in the sense that they deviate from the Gaussian distribution assumption. Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections, a two –step iterative process is followed where we first search for a projection that maximizes a projection index based on the information divergence of the projections estimated probability distribution from the Gaussian distribution and then reduce the rank by projections the data on to the subspace orthogonal to the previous projection . To calculate each projections, we use a simplified approach to maximizing the projection index, which does not require optimization algorithm. It searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. The effectiveness of the method is demonstrated through simulated examples as well as data from the hyper spectral digital imagery collection experiment and the spatially enhanced broadband and array spectrograph system.

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Liu, Haojie, Han Shen, Lichao Huang, Ming Lu, Tong Chen, and Zhan Ma. "Learned Video Compression via Joint Spatial-Temporal Correlation Exploration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11580–87. http://dx.doi.org/10.1609/aaai.v34i07.6825.

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Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-the-art performance, with consistent gains across all popular test sequences.

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Hilth, William, David Ryckelynck, and Claire Menet. "Data Pruning of Tomographic Data for the Calibration of Strain Localization Models." Mathematical and Computational Applications 24, no. 1 (January 28, 2019): 18. http://dx.doi.org/10.3390/mca24010018.

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The development and generalization of Digital Volume Correlation (DVC) on X-ray computed tomography data highlight the issue of long-term storage. The present paper proposes a new model-free method for pruning experimental data related to DVC, while preserving the ability to identify constitutive equations (i.e., closure equations in solid mechanics) reflecting strain localizations. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The proposed data pruning procedure is deeply linked to hyper-reduction techniques. The DVC data of a resin-bonded sand tested in uniaxial compression is used as an illustrating example. The relevance of the pruned data was tested afterwards for model calibration. A Finite Element Model Updating (FEMU) technique coupled with an hybrid hyper-reduction method aws used to successfully calibrate a constitutive model of the resin bonded sand with the pruned data only.

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Li, Jin, Long-xu Jin, and Guo-ning Li. "Hyper-spectral Remote Sensing Image Compression Based on Nonnegative Tensor Factorizations in Discrete Wavelet Domain." Journal of Electronics & Information Technology 35, no. 2 (February 18, 2014): 489–93. http://dx.doi.org/10.3724/sp.j.1146.2012.00812.

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Patterson, Brian M., Nikolaus L. Cordes, Kevin Henderson, Jason J. Williams, Tyler Stannard, Sudhanshu S. Singh, Angel Rodriguez Ovejero, Xianghui Xiao, Mathew Robinson, and Nikhilesh Chawla. "In situ X-ray synchrotron tomographic imaging during the compression of hyper-elastic polymeric materials." Journal of Materials Science 51, no. 1 (September 8, 2015): 171–87. http://dx.doi.org/10.1007/s10853-015-9355-8.

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AVERBUCH, AMIR Z., VALERY A. ZHELUDEV, MOSHE GUTTMANN, and DAN D. KOSLOFF. "LCT-WAVELET BASED ALGORITHMS FOR DATA COMPRESSION." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 05 (September 2013): 1350032. http://dx.doi.org/10.1142/s021969131350032x.

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We present an algorithm that compresses two-dimensional data, which are piece-wise smooth in one direction and have oscillatory events in the other direction. Fine texture, seismic, hyper-spectral and fingerprints have this mixed structure. The transform part of the compression process is an algorithm that combines the application of the wavelet transform in one direction with the local cosine transform (LCT) in the other direction. This is why it is called hybrid compression. The quantization and the entropy coding parts in the compression process were taken from SPIHT codec but it can also be taken from any multiresolution based codec such as EZW. To efficiently apply the SPIHT codec to a mixed coefficients array, reordering of the LCT coefficients takes place. When oscillating events are present in different directions as in fingerprints or when the image comprises of a fine texture, a 2D LCT with coefficients reordering is applied. These algorithms outperform algorithms that are solely based on the the application of 2D wavelet transforms to each direction with either SPIHT or EZW coding including JPEG2000 compression standard. The proposed algorithms retain fine oscillating events including texture even at a low bitrate. Its compression capabilities are also demonstrated on multimedia images that have a fine texture. The wavelet part in the mixed transform of the hybrid algorithm utilizes the Butterworth wavelet transforms library that outperforms the 9/7 biorthogonal wavelet transform.

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Yin, Miao, Huy Phan, Xiao Zang, Siyu Liao, and Bo Yuan. "BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8874–82. http://dx.doi.org/10.1609/aaai.v36i8.20869.

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Model compression is very important for the efficient deployment of deep neural network (DNN) models on resource-constrained devices. Among various model compression approaches, high-order tensor decomposition is particularly attractive and useful because the decomposed model is very small and fully structured. For this category of approaches, tensor ranks are the most important hyper-parameters that directly determine the architecture and task performance of the compressed DNN models. However, as an NP-hard problem, selecting optimal tensor ranks under the desired budget is very challenging and the state-of-the-art studies suffer from unsatisfied compression performance and timing-consuming search procedures. To systematically address this fundamental problem, in this paper we propose BATUDE, a Budget-Aware TUcker DEcomposition-based compression approach that can efficiently calculate optimal tensor ranks via one-shot training. By integrating the rank selecting procedure to the DNN training process with a specified compression budget, the tensor ranks of the DNN models are learned from the data and thereby bringing very significant improvement on both compression ratio and classification accuracy for the compressed models. The experimental results on ImageNet dataset show that our method enjoys 0.33% top-5 higher accuracy with 2.52X less computational cost as compared to the uncompressed ResNet-18 model. For ResNet-50, the proposed approach enables 0.37% and 0.55% top-5 accuracy increase with 2.97X and 2.04X computational cost reduction, respectively, over the uncompressed model.

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Qu, Jia, Geng Chen, and Yu Wei Yang. "Finite Element Analysis of Rubber Sealing Ring Resilience Behavior." Advanced Materials Research 705 (June 2013): 410–14. http://dx.doi.org/10.4028/www.scientific.net/amr.705.410.

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In this paper, hyper-elastic constitutive models of rubber material have been summed up based on constitutive relation and the Mooney-Rivlin model has been pay more attention. Then through the tension experimental test, data of sealing material under axial experimental are obtained, and M-R model parameters C10 and C01 are fitted by ANSYS. After obtaining the material parameters, compression deformation behavior and the distribution of stress field and resilience behavior of the seal ring are simulated by using ANSYS/LS-DYNA software under different loading conditions.

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Omara, Ahmed, Alaa Hefnawy, and Abdelhalim Zekry. "On Sparse Compression Complexity of Speech Signals." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 2 (February 1, 2016): 329. http://dx.doi.org/10.11591/ijeecs.v1.i2.pp329-340.

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<p>In this paper, we have addressed the issue of the sparse compression complexity for the speech signals. First of all, this work illustrated the effect of the signal length on the complexity levels of Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP) algorithms. Also, this paper introduced a study of possibility to reduce that complexity by exploiting the shared atoms among the contiguous speech compressions. By comparing the shared atoms levels and a threshold level induced by an analytic model based on the both the central and non-central hyper-geometric distributions, we proved the ability of the shared atoms criterion to detect if there is biasing towards a subspace of atoms or not, and to decide if the biasing occurs due to the redundancy in the dictionary of atoms, or due to the redundancy in the signal itself. <br />Moreover, we suggested a subspace bias-based approaches for complexity reduction called "Atoms Reuse" and "Active Cluster". Both methods exploits the higher levels of the shared atoms to reduce the compression complexity by reducing the search space during the pursuit iterations.</p>

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Kala, S., and S. Vasuki. "Feature correlation based parallel hyper spectral image compression using a hybridization of FCM and subtractive clustering." Journal of Communications Technology and Electronics 59, no. 12 (December 2014): 1378–89. http://dx.doi.org/10.1134/s1064226914120195.

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Nagata, Kenji, and Atsushi Matsuda. "504 Numerical Simulation for the Piston Compression Section of the Free Piston Type Hyper Velocity Flow Generator." Proceedings of Conference of Tokai Branch 2013.62 (2013): 267–68. http://dx.doi.org/10.1299/jsmetokai.2013.62.267.

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PURDOIU, Robert Cristian, Reut ASHUR, Laura CONDOR, and Radu LĂCĂTUȘ. "Computed Tomography Findings in Spinal Compression in 196 Dogs." Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Veterinary Medicine 75, no. 1 (May 19, 2018): 46. http://dx.doi.org/10.15835/buasvmcn-vm:003417.

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Spinal cord lesion can be presented as acute, in cases of trauma such as fractures, hematomas and inflammation, or can progress slowly in some chronic diseases.The aim of the study was to identify the sensitivity and specificity of CT examination in diagnostics of different causes of spinal compression in dogs. In this study, different cases with same nature of progressed signs and no findings on x-ray imaging were presented for investigation. All cases were demonstrated for spinal impact sings according to the neurological examination and were good candidates for CT imaging. The study was conducted on 196 dogs, evaluated through CT examination between 2015 and 2017. All dogs arrived with signs suggesting spinal cord lesion of varying degrees, those sings progressed with time but had no evident of spinal lesion in x-ray imaging. Different diagnostics were made based on the CT scans and the cases were divided based on that.For the dogs in the study, signs of spinal compression were evaluated, being taken in consideration the presence of hypo or hyper attenuating material in the medullary canal, integrity of the spinal canal and the vertebral body, the values of A and H index, location of the spinal lesion and presence of vacuum phenomena.Computed tomography examination of the vertebral column proved to be a valuable tool that completed the neurologic examination and show a high sensitivity (94.48%) and a high specificity (87.88%) in diagnostic of spinal cord compression.

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Legerer, Christian, Zakaria Almsherqi, and Craig McLachlan. "Over-Wrapping of the Aortic Wall with an Elastic Extra-Aortic Wrap Results in Luminal Creasing." Journal of Cardiovascular Development and Disease 5, no. 3 (August 11, 2018): 42. http://dx.doi.org/10.3390/jcdd5030042.

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Elastic extra-aortic wrapping is a potential non-pharmacological way to improve aortic compliance and treat isolated systolic hypertension associated with a stiffened aorta. We aimed to use computer simulations to re-evaluate whether there is aortic shape distortion in aortic wrapping to achieve greater elasticity of the wrapped aortic segment. Non-linear transient numerical analysis based on an idealized hyper-elastic single-layered aorta model was performed to simulate the force/displacement regimes of external aortic wrapping. Pressure-displacement relationships were used to establish model aortic wall distensibilities of 4.3 and 5.5 (10−3 mmHg−1). A physiological pulsatile lumen pressure was employed to estimate the potential improvements in aortic distensibility by compression forces representing elastic aortic wrapping. In the less distensible model of the aortic wall there was increased systolic expansion in the wrapped segment. We found a risk of creasing of the aortic luminal wall with wrapping. Sufficient unloading of a thick and elastic aortic wall to induce increased compliance, as observed in elastic wrapping, is associated with the potential risk of over compression and folding (creasing) inside the lumen.

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