Literatura científica selecionada sobre o tema "Unrolling methods"
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Artigos de revistas sobre o assunto "Unrolling methods"
Lee, Songil, Gyouhyung Kyung, Minjoong Kim, Donghee Choi, Hyeeun Choi, Kitae Hwang, Seonghyeok Park, Su Young Kim e 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, n.º 5 (2 de julho de 2019): 770–86. http://dx.doi.org/10.1177/0018720819855225.
Texto completo da fonteSong, Heping, Qifeng Ding, Jingyao Gong, Hongying Meng e Yuping Lai. "SALSA-Net: Explainable Deep Unrolling Networks for Compressed Sensing". Sensors 23, n.º 11 (28 de maio de 2023): 5142. http://dx.doi.org/10.3390/s23115142.
Texto completo da fonteYu, Youhao, e Richard M. Dansereau. "MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)". IET Signal Processing 2024 (18 de janeiro de 2024): 1–12. http://dx.doi.org/10.1049/2024/6666549.
Texto completo da fonteZhang, Linrui, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang e Dacheng Tao. "Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junho de 2023): 15313–21. http://dx.doi.org/10.1609/aaai.v37i12.26786.
Texto completo da fonteKamali, Hadi Mardani, e Shaahin Hessabi. "A Fault Tolerant Parallelism Approach for Implementing High-Throughput Pipelined Advanced Encryption Standard". Journal of Circuits, Systems and Computers 25, n.º 09 (21 de junho de 2016): 1650113. http://dx.doi.org/10.1142/s0218126616501139.
Texto completo da fonteМельник, Л. М., А. С. Конотоп e О. П. Кизимчук. "ЗАСТОСУВАННЯ ТРАДИЦІЙНИХ НАЦІОНАЛЬНИХ ЕЛЕМЕНТІВ ОЗДОБЛЕННЯ В СУЧАСНОМУ ОДЯЗІ". Art and Design, n.º 2 (15 de junho de 2018): 51–58. http://dx.doi.org/10.30857/2617-0272.2018.2.6.
Texto completo da fonteYe, Yutong, Hongyin Zhu, Chaoying Zhang e Binghai Wen. "Efficient graphic processing unit implementation of the chemical-potential multiphase lattice Boltzmann method". International Journal of High Performance Computing Applications 35, n.º 1 (27 de outubro de 2020): 78–96. http://dx.doi.org/10.1177/1094342020968272.
Texto completo da fonteGuo, Yang, Wei Gao, Siwei Ma e Ge Li. "Accelerating Transform Algorithm Implementation for Efficient Intra Coding of 8K UHD Videos". ACM Transactions on Multimedia Computing, Communications, and Applications 18, n.º 4 (30 de novembro de 2022): 1–20. http://dx.doi.org/10.1145/3507970.
Texto completo da fonteAydin, Seda Guzel, e Hasan Şakir Bilge. "FPGA Implementation of Image Registration Using Accelerated CNN". Sensors 23, n.º 14 (21 de julho de 2023): 6590. http://dx.doi.org/10.3390/s23146590.
Texto completo da fonteWang, Nan, Xiaoling Zhang, Tianwen Zhang, Liming Pu, Xu Zhan, Xiaowo Xu, Yunqiao Hu, Jun Shi e Shunjun Wei. "A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering". Remote Sensing 14, n.º 11 (30 de maio de 2022): 2614. http://dx.doi.org/10.3390/rs14112614.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteThe 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)
Capítulos de livros sobre o assunto "Unrolling methods"
Aggarwal, Sakshi, Navjot Singh e 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.
Texto completo da fonteGupta, Sharoni, Pinki Bala Punjabi e 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.
Texto completo da fonteHerz, Norman, e 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.
Texto completo da fonteEmmott, Catherine. "Summary". In Narrative Comprehension, 267–75. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198236498.003.0009.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Unrolling methods"
Gurfinkel, Arie, e 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.
Texto completo da fonteMlambo, Cynthia Sthembile, e 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.
Texto completo da fonteBonettini, Silvia, Giorgia Franchini, Danilo Pezzi e 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.
Texto completo da fonteLiu, Frank, e 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.
Texto completo da fonteBakan, Altug, e 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.
Texto completo da fonteBulavintsev, Vadim, e 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.
Texto completo da fonteDrymonitis, 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.
Texto completo da fonteDong, Yazhuo, Jie Zhou, Yong Dou, Lin Deng e 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.
Texto completo da fonteMalta, Eduardo Ribeiro, Clóvis de Arruda Martins, Silas Henrique Gonçalves e 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.
Texto completo da fonte