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Academic literature on the topic 'Unrolling methods'
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Journal articles on the topic "Unrolling methods"
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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.
Full textAbstract:
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 use of side bezels, and restriction on the gripping type). Prototypes differed in their right-side thickness (2, 6, and 10 mm). Side bezel regions grasped during screen unrolling and corresponding bimanual grip comfort ratings were obtained. Results To improve perceived grip comfort and accommodate user-preferred gripping methods, rollable display devices should be 6 mm (preferably 10 mm) thick (vs. 2 mm) and have at least 20-mm-wide side bezels. Relative to device thickness, gripping conditions were more influential on grip comfort ratings. The “no restriction” condition improved grip comfort ratings and strengthened bimanual coupling in terms of grip comfort ratings. Conclusion Contrary to current smartphone trends toward thinner and bezel-less designs, hand-held rollable display devices should be sufficiently thick and have sufficiently wide side bezels for screen unrolling. Application Hand-held rollable display devices should be 6- or preferably 10-mm thick (vs. 2 mm) and have at least 20-mm-wide side bezels to ensure higher perceived grip comfort during bilateral screen unrolling.
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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.
Full textAbstract:
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 solve sparsity-induced CS reconstruction problems. SALSA-Net inherits the interpretability of the SALSA algorithm while incorporating the learning ability and fast reconstruction speed of deep neural networks. By converting the SALSA algorithm into a deep network structure, SALSA-Net consists of a gradient update module, a threshold denoising module, and an auxiliary update module. All parameters, including the shrinkage thresholds and gradient steps, are optimized through end-to-end learning and are subject to forward constraints to ensure faster convergence. Furthermore, we introduce learned sampling to replace traditional sampling methods so that the sampling matrix can better preserve the feature information of the original signal and improve sampling efficiency. Experimental results demonstrate that SALSA-Net achieves significant reconstruction performance compared to state-of-the-art methods while inheriting the advantages of explainable recovery and high speed from the DUNs paradigm.
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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 (ResNeXt) and squeeze-and-excitation mechanisms into the ISTA block. This block serves as a deep equilibrium layer connected to a semi-tensor product network for convenient sampling and providing an initial reconstruction. The resulting model, called MsDC-DEQ-Net, exhibits competitive performance compared to state-of-the-art network-based methods. It significantly reduces storage requirements compared to deep unrolling methods, using only one iteration block instead of multiple iterations. Unlike deep unrolling models, MsDC-DEQ-Net can be iteratively used, gradually improving reconstruction accuracy while considering computation tradeoffs. Additionally, the model benefits from multiscale dilated convolutions, further enhancing performance.
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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 method that combines safety optimization and safety projection. This novel technique explicitly enforces hard constraints via the deep unrolling architecture and enjoys structural advantages in navigating the trade-off between reward improvement and constraint satisfaction. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit, a toolkit that provides off-the-shelf interfaces and evaluation utilities for safety-critical tasks. We then perform a comparative study of the involved algorithms on six benchmarks ranging from robotic control to autonomous driving. The empirical results provide an insight into their applicability and robustness in learning zero-cost-return policies without task-dependent handcrafting. The project page is available at https://sites.google.com/view/saferlkit.
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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 main loop will be removed. Also, instead of unrolling the main loop of AES algorithm, we implement pipelining structure by replicating nonpipelined AES architectures and using an auto-assigner mechanism for each AES block. By implementing the new pipelined architecture, we achieve two valuable advantages: (a) solving single point of failure problem when one of the replicated parts is faulty and (b) deploying the proposed design as a fault tolerant AES architecture. In addition, we put emphasis on area optimization for all four AES main functions to reduce the overhead associated with AES block replication. The simulation results show that the maximum frequency of our proposed AES architecture is 675.62[Formula: see text]MHz, and for AES128 the throughput is 86.5[Formula: see text]Gbps which is 30.9% better than its closest existing competitor.
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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 Ukrainian embroidery, have been developed in this study. The few variants of simple mesh are offered: narrow one made on the basis of the rib 1 + 1 with an unrolling; narrow complex canvas - on the basis of openwork interlooping; wide (more than 2 cm) canvas - on the basis of a plated interlooping. The model of the women's dress is executed in the semi-regular way on two-bar flat-knitted machine, using the developed variants of knitted fabric structures. Taking into account the traditions of an arrangement of the embroidery decoration elements and modern fashion trends, it is proposed to use a narrow simple snap-net to decorate the neck of the product, a wide snap-net, having a plant ornament to knit 2/3 of the bottom of the sleeve. The narrow canvas is also used as a connecting element of the sleeve parts made with various interlooping. The decoration elements of national Ukrainian clothes have been investigated and reproduced in women's knitted dress by new knitted structure creation taking into account modern technology. Research results can be used to expand the range of modern women's clothing
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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 graphic processing unit (GPU). Several strategies were used to optimize the GPU algorithm, such as coalesced access, instruction throughput, thread organization, memory access, and loop unrolling. Compared with dual-Xeon 5117 CPU server, our methods achieved 95 times speedup on an NVIDIA RTX 2080Ti GPU and 106 times speedup on an NVIDIA Tesla P100 GPU. When the algorithm was extended to the environment with dual NVIDIA Tesla P100 GPUs, 189 times speedup was achieved and the workload of each GPU reached 96%.
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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, to implement multiplierless computation for saving resources and power, a series of shift-and-add unit (SAU) hardwares are investigated to have much less adoptions of shifters and adders than the existing methods. Third, different types of hardware acceleration methods, including calculation pipelining, logical-loop unrolling, and module-level parallelism, are designed to efficaciously support the data-intensive high frame-rate 8K UHD video coding. Finally, due to the scarcity of 8K video sources, we also provide a new dataset for the performance verification. Experimental results demonstrate that our proposed method can effectively fulfill the real-time 8K intra encoding at beyond 60 fps, with very negligible loss on rate-distortion (R-D) performance, which is averagely 0.98% Bjontegaard-Delta Bit-Rate (BD-BR).
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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 applications. Methods: This study proposes an FPGA-based ultrasound IR CNN (FUIR-CNN) to regress three rigid registration parameters from image pairs. To speed up the estimation process, the proposed design makes use of fixed-point data and parallel operations carried out by unrolling and pipelining techniques. Experiments were performed on three US datasets in real time using the xc7z020, and the xcku5p was also used during implementation. Results: The FUIR-CNN produced results for the inference phase 139 times faster than the software-based network while retaining a negligible drop in regression performance of under 200 MHz clock frequency. Conclusions: Comprehensive experimental results demonstrate that the proposed end-to-end FPGA-based accelerated CNN achieves a negligible loss, a high speed for registration parameters, less power when compared to the CPU, and the potential for real-time medical imaging.
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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 interferometric phase filtering without sacrificing speed. Therefore, we propose a sparse-model-driven network (SMD-Net) for efficient and high-accuracy InSAR phase filtering by unrolling the sparse regularization (SR) algorithm to solve the filtering model into a network. Unlike the existing DL-based filtering methods, the SMD-Net models the physical process of filtering in the network and contains fewer layers and parameters. It is thus expected to ensure the accuracy of the filtering without sacrificing speed. In addition, unlike the traditional SR algorithm setting the spare transform by handcrafting, a convolutional neural network (CNN) module was established to adaptively learn such a transform, which significantly improved the filtering performance. Extensive experimental results on the simulated and measured data demonstrated that the proposed method outperformed several advanced InSAR phase filtering methods in both accuracy and speed. In addition, to verify the filtering performance of the proposed method under small training samples, the training samples were reduced to 10%. The results show that the performance of the proposed method was comparable on the simulated data and superior on the real data compared with another DL-based method, which demonstrates that our method is not constrained by the requirement of a huge number of training samples.