Auswahl der wissenschaftlichen Literatur zum Thema „Unrolling methods“
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Zeitschriftenartikel zum Thema "Unrolling methods"
Lee, Songil, Gyouhyung Kyung, Minjoong Kim, Donghee Choi, Hyeeun Choi, Kitae Hwang, Seonghyeok Park, Su Young Kim und Seungbae Lee. „Shaping Rollable Display Devices: Effects of Gripping Condition, Device Thickness, and Hand Length on Bimanual Perceived Grip Comfort“. Human Factors: The Journal of the Human Factors and Ergonomics Society 62, Nr. 5 (02.07.2019): 770–86. http://dx.doi.org/10.1177/0018720819855225.
Der volle Inhalt der QuelleSong, Heping, Qifeng Ding, Jingyao Gong, Hongying Meng und Yuping Lai. „SALSA-Net: Explainable Deep Unrolling Networks for Compressed Sensing“. Sensors 23, Nr. 11 (28.05.2023): 5142. http://dx.doi.org/10.3390/s23115142.
Der volle Inhalt der QuelleYu, Youhao, und Richard M. Dansereau. „MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)“. IET Signal Processing 2024 (18.01.2024): 1–12. http://dx.doi.org/10.1049/2024/6666549.
Der volle Inhalt der QuelleZhang, Linrui, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang und Dacheng Tao. „Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 12 (26.06.2023): 15313–21. http://dx.doi.org/10.1609/aaai.v37i12.26786.
Der volle Inhalt der QuelleKamali, Hadi Mardani, und Shaahin Hessabi. „A Fault Tolerant Parallelism Approach for Implementing High-Throughput Pipelined Advanced Encryption Standard“. Journal of Circuits, Systems and Computers 25, Nr. 09 (21.06.2016): 1650113. http://dx.doi.org/10.1142/s0218126616501139.
Der volle Inhalt der QuelleМельник, Л. М., А. С. Конотоп und О. П. Кизимчук. „ЗАСТОСУВАННЯ ТРАДИЦІЙНИХ НАЦІОНАЛЬНИХ ЕЛЕМЕНТІВ ОЗДОБЛЕННЯ В СУЧАСНОМУ ОДЯЗІ“. Art and Design, Nr. 2 (15.06.2018): 51–58. http://dx.doi.org/10.30857/2617-0272.2018.2.6.
Der volle Inhalt der QuelleYe, Yutong, Hongyin Zhu, Chaoying Zhang und Binghai Wen. „Efficient graphic processing unit implementation of the chemical-potential multiphase lattice Boltzmann method“. International Journal of High Performance Computing Applications 35, Nr. 1 (27.10.2020): 78–96. http://dx.doi.org/10.1177/1094342020968272.
Der volle Inhalt der QuelleGuo, Yang, Wei Gao, Siwei Ma und Ge Li. „Accelerating Transform Algorithm Implementation for Efficient Intra Coding of 8K UHD Videos“. ACM Transactions on Multimedia Computing, Communications, and Applications 18, Nr. 4 (30.11.2022): 1–20. http://dx.doi.org/10.1145/3507970.
Der volle Inhalt der QuelleAydin, Seda Guzel, und Hasan Şakir Bilge. „FPGA Implementation of Image Registration Using Accelerated CNN“. Sensors 23, Nr. 14 (21.07.2023): 6590. http://dx.doi.org/10.3390/s23146590.
Der volle Inhalt der QuelleWang, Nan, Xiaoling Zhang, Tianwen Zhang, Liming Pu, Xu Zhan, Xiaowo Xu, Yunqiao Hu, Jun Shi und Shunjun Wei. „A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering“. Remote Sensing 14, Nr. 11 (30.05.2022): 2614. http://dx.doi.org/10.3390/rs14112614.
Der volle Inhalt der QuelleDissertationen zum Thema "Unrolling methods"
Mom, Kannara. „Deep learning based phase retrieval for X-ray phase contrast imaging“. Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0087.
Der volle Inhalt der QuelleThe development of highly coherent X-ray sources, such as third-generation synchrotron radiation facilities, has significantly contributed to the advancement of phase contrast imaging. The high degree of coherence of these sources enables efficient implementation of phase contrast techniques, and can increase sensitivity by several orders of magnitude. This novel imaging technique has found applications in a wide range of fields, including material science, paleontology, bone research, medicine, and biology. It enables the imaging of samples with low absorption constituents, where traditional absorption-based methods may fail to provide sufficient contrast. Several phase-sensitive imaging techniques have been developed, among them, propagation-based imaging requires no equipment other than the source, object and detector. Although the intensity can be measured at one or several propagation distances, the phase information is lost and must be estimated from those diffraction patterns, a process called phase retrieval. Phase retrieval in this context is a nonlinear ill-posed inverse problem. Various classical methods have been proposed to retrieve the phase, either by linearizing the problem to obtain an analytical solution, or by iterative algorithms. The main purpose of this thesis was to study what new deep learning approaches could bring to this phase retrieval problem. Various deep learning algorithms have been proposed and evaluated to address this problem. In the first part of this work, we show how neural networks can be used to reconstruct directly from measurements data, without model information. The architecture of the Mixed Scale Dense Network (MS-D Net) is introduced, combining dilated convolution and dense connection. In the second part of this thesis, we propose a nonlinear primal–dual algorithm for the retrieval of phase shift and absorption from a single X-ray in-line phase contrast. We showed that choosing different regularizers for absorption and phase can improve the reconstructions. In the third part, we propose to integrate neural networks into an existing optimization scheme using so-called unrolling approaches, in order to give the convolutional neural networks a specific role in the reconstruction. The performance of theses algorithms are evaluated using simulated noisy data as well as images acquired at NanoMAX (MAX IV, Lund, Sweden)
Buchteile zum Thema "Unrolling methods"
Aggarwal, Sakshi, Navjot Singh und K. K. Mishra. „Unrolling the COVID-19 Diagnostic Systems Driven by Deep Learning“. In Application of Deep Learning Methods in Healthcare and Medical Science, 177–98. New York: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003303855-10.
Der volle Inhalt der QuelleGupta, Sharoni, Pinki Bala Punjabi und Rakshit Ameta. „Preparation Methods for Graphene and its Derivatives“. In Graphene-based Carbocatalysts: Synthesis, Properties and Applications, 76–117. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815050899123010007.
Der volle Inhalt der QuelleHerz, Norman, und Ervan G. Garrison. „The Scope of Archaeological Geology“. In Geological Methods for Archaeology. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195090246.003.0003.
Der volle Inhalt der QuelleEmmott, Catherine. „Summary“. In Narrative Comprehension, 267–75. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198236498.003.0009.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Unrolling methods"
Gurfinkel, Arie, und Alexander Ivrii. „K-induction without unrolling“. In 2017 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2017. http://dx.doi.org/10.23919/fmcad.2017.8102253.
Der volle Inhalt der QuelleMlambo, Cynthia Sthembile, und Yaseen Moolla. „Complexity and Distortion Analysis on Methods for Unrolling 3D to 2D Fingerprints“. In 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2015. http://dx.doi.org/10.1109/sitis.2015.53.
Der volle Inhalt der QuelleBonettini, Silvia, Giorgia Franchini, Danilo Pezzi und Marco Prato. „Learning the Image Prior by Unrolling an Optimization Method“. In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909852.
Der volle Inhalt der QuelleLiu, Frank, und Peter Feldmann. „A Time-Unrolling Method to Compute Sensitivity of Dynamic Systems“. In the The 51st Annual Design Automation Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2593069.2593080.
Der volle Inhalt der QuelleBakan, Altug, und Isin Erer. „Unrolling Alternating Direction Method of Multipliers for Visible and Infrared Image Fusion“. In 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS). IEEE, 2022. http://dx.doi.org/10.1109/ipas55744.2022.10052930.
Der volle Inhalt der QuelleBulavintsev, Vadim, und Dmitry Zhdanov. „Method for Adaptation of Algorithms to GPU Architecture“. In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-930-941.
Der volle Inhalt der QuelleDrymonitis, Dimitris. „THE UNROLLING METHOD AS A TOOL FOR ORE RESERVES ESTIMATION IN DEPOSITS OF VARIABLE DIP“. In 15th International Multidisciplinary Scientific GeoConference SGEM2015. Stef92 Technology, 2011. http://dx.doi.org/10.5593/sgem2015/b13/s3.094.
Der volle Inhalt der QuelleDong, Yazhuo, Jie Zhou, Yong Dou, Lin Deng und Jinjing Zhao. „Impact of Loop Unrolling on Area, Throughput and Clock Frequency for Window Operations Based on a Data Schedule Method“. In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.211.
Der volle Inhalt der QuelleMalta, Eduardo Ribeiro, Clóvis de Arruda Martins, Silas Henrique Gonçalves und Alfredo Gay Neto. „Finite Element Modeling of Flexible Pipes Under Crushing Loads“. In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10307.
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