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Relevant bibliographies by topics / Unrolling methods

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Journal articles

Academic literature on the topic 'Unrolling methods'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Unrolling methods"

1

Lee, Songil, Gyouhyung Kyung, Minjoong Kim, et al. "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, no. 5 (2019): 770–86. http://dx.doi.org/10.1177/0018720819855225.

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Objective To examine the effects of the gripping condition, device thickness, and hand length on bimanual perceived grip comfort associated with unrolling hand-held rollable screens. Background Rollable displays can be rolled and unrolled to change screen size. Although diverse rollable display device concepts have been suggested, little is known regarding ergonomic forms for comfortable screen unrolling. Method Thirty young individuals (10 in each hand-length group) evaluated three rollable display device prototypes in three gripping conditions (no restriction on using side bezels, minimal us

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Song, Heping, Qifeng Ding, Jingyao Gong, Hongying Meng, and Yuping Lai. "SALSA-Net: Explainable Deep Unrolling Networks for Compressed Sensing." Sensors 23, no. 11 (2023): 5142. http://dx.doi.org/10.3390/s23115142.

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Deep unrolling networks (DUNs) have emerged as a promising approach for solving compressed sensing (CS) problems due to their superior explainability, speed, and performance compared to classical deep network models. However, the CS performance in terms of efficiency and accuracy remains a principal challenge for approaching further improvements. In this paper, we propose a novel deep unrolling model, SALSA-Net, to solve the image CS problem. The network architecture of SALSA-Net is inspired by unrolling and truncating the split augmented Lagrangian shrinkage algorithm (SALSA) which is used to

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3

Yu, Youhao, and Richard M. Dansereau. "MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)." IET Signal Processing 2024 (January 18, 2024): 1–12. http://dx.doi.org/10.1049/2024/6666549.

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Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images using CS. We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. To enhance learning ability and incorporate structural diversity, we integrate aggregated residual transformations (Re

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Zhang, Linrui, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang, and Dacheng Tao. "Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15313–21. http://dx.doi.org/10.1609/aaai.v37i12.26786.

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Safety comes first in many real-world applications involving autonomous agents. Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics. In this paper, we revisit prior work in this scope from the perspective of state-wise safe RL and categorize them as projection-based, recovery-based, and optimization-based approaches, respectively. Furthermore, we propose Unrolling Safety Layer (USL), a joint

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Kamali, Hadi Mardani, and Shaahin Hessabi. "A Fault Tolerant Parallelism Approach for Implementing High-Throughput Pipelined Advanced Encryption Standard." Journal of Circuits, Systems and Computers 25, no. 09 (2016): 1650113. http://dx.doi.org/10.1142/s0218126616501139.

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Advanced Encryption Standard (AES) is the most popular symmetric encryption method, which encrypts streams of data by using symmetric keys. The current preferable AES architectures employ effective methods to achieve two important goals: protection against power analysis attacks and high-throughput. Based on a different architectural point of view, we implement a particular parallel architecture for the latter goal, which is capable of implementing a more efficient pipelining in field-programmable gate array (FPGA). In this regard, all intermediate registers which have a role for unrolling the

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Мельник, Л. М., А. С. Конотоп та О. П. Кизимчук. "ЗАСТОСУВАННЯ ТРАДИЦІЙНИХ НАЦІОНАЛЬНИХ ЕЛЕМЕНТІВ ОЗДОБЛЕННЯ В СУЧАСНОМУ ОДЯЗІ". Art and Design, № 2 (15 червня 2018): 51–58. http://dx.doi.org/10.30857/2617-0272.2018.2.6.

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The purpose of this work is to establish the possibility of the decorating of modern women's clothes with artistic elements and ornaments of traditional national clothes of Ukraine different regions and their subsequent reproduction in knitwear. Methods of literary-analytical review and visual-analytical research are used.The elements of clothing decoration that are characteristic of ethnic Ukrainian clothing have been defined during the research. The structures of knitted fabrics based on on openwork, plated and interlooping with unrolling, which simulate different merezhka of traditional Ukr

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Ye, Yutong, Hongyin Zhu, Chaoying Zhang, and Binghai Wen. "Efficient graphic processing unit implementation of the chemical-potential multiphase lattice Boltzmann method." International Journal of High Performance Computing Applications 35, no. 1 (2020): 78–96. http://dx.doi.org/10.1177/1094342020968272.

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The chemical-potential multiphase lattice Boltzmann method (CP-LBM) has the advantages of satisfying the thermodynamic consistency and Galilean invariance, and it realizes a very large density ratio and easily expresses the surface wettability. Compared with the traditional central difference scheme, the CP-LBM uses the Thomas algorithm to calculate the differences in the multiphase simulations, which significantly improves the calculation accuracy but increases the calculation complexity. In this study, we designed and implemented a parallel algorithm for the chemical-potential model on a gra

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Guo, Yang, Wei Gao, Siwei Ma, and Ge Li. "Accelerating Transform Algorithm Implementation for Efficient Intra Coding of 8K UHD Videos." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–20. http://dx.doi.org/10.1145/3507970.

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Real-time ultra-high-definition (UHD) video applications have attracted much attention, where the encoder side urgently demands the high-throughput two-dimensional (2D) transform hardware implementation for the latest video coding standards. This article proposes an effective acceleration method for transform algorithm in UHD intra coding based on the third generation of audio video coding standard (AVS3). First, by conducting detailed statistical analysis, we devise an efficient hardware-friendly transform algorithm that can reduce running cycles and resource consumption remarkably. Second, t

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Aydin, Seda Guzel, and Hasan Şakir Bilge. "FPGA Implementation of Image Registration Using Accelerated CNN." Sensors 23, no. 14 (2023): 6590. http://dx.doi.org/10.3390/s23146590.

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Background: Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN a

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Wang, Nan, Xiaoling Zhang, Tianwen Zhang, et al. "A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering." Remote Sensing 14, no. 11 (2022): 2614. http://dx.doi.org/10.3390/rs14112614.

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Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are frequently superior to traditional ones. However, most of the existing DL-based methods are purely data-driven and neglect the filtering model, so that they often need to use a large-scale complex architecture to fit the huge training sets. The issue brings a challenge to improve the accuracy of interfero

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