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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy 48

A Targeted Sampling Strategy for Compressive Cryo Focused Io...

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Nicholls, D. Wells, J. Robinson, A.W. Moshtaghpour, A. Kobylynska, M. Fleck, R.A. Kirkland, A.I. Browning, N.D. University of Liverpool Distributed Algorithms Centre for Doctoral Training United Kingdom King's College London Centre for Ultrastructural Imaging London United Kingdom Harwell Science and Innovation Campus Correlated Imaging Group Rosalind Franklin Institute Didcot United Kingdom University of Oxford Department of Materials United Kingdom University of Liverpool Department of Mechanical Materials and Aerospace Engineering United Kingdom Pacific Northwest National Laboratory Physical and Computational Science Richland United States Sivananthan Laboratories 590 Territorial Drive BolingbrookIL United States

ISBN: (纸本)9781728163277

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial. © 2023 IEEE.

关键词： Beta-process factor analysis Compressive sensing Focused ion beam Scanning electron microscopy

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Node Load Rate Calculation Method based on Database Query Statistics in Load Balancing Strategy

Node Load Rate Calculation Method based on Database Query St...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Qingqing Song Zhen Wu Shaoliang Xia Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The calculation of node load rate has a crucial impact on the quality of load balancing strategies in distributed systems. The existing commonly used methods for calculating node load rates do not fully consider the current state of nodes, leading to a discrepancy between their calculation results and reality. This paper focuses on distributed information systems, taking databases as the starting point, and proposes a node load rate calculation method based on database query statistics to be used in load balancing strategies. It adopts a method of combining theoretical calculation data with benchmark performance testing data for analysis, fully considering the impact of factors such as data volume in distributed information system databases, peak database access periods, and data table indexes on calculation results. While ensuring the accuracy of node load rate calculation results, it minimizes the need for repetitive testing, conserves computer performance and energy resources, and reduces the time and economic expenses associated with node load rate calculation. The validation results of the algorithm indicate that the average error rate between the calculated database seconds per query (SPQ) and the actual values is as low as 1.7%.

关键词： Economics Concurrent computing Error analysis Energy resources image processing distributed databases distributed information systems Benchmark testing Load management Indexes

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Queuing parallel Computing CAD Tasks in the Design and Optimization of IC Topography 28

Queuing Parallel Computing CAD Tasks in the Design and Optim...

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28th International conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Wojtasik, Adam Warsaw Univ Technol Inst Microelect & Optoelect Warsaw Poland

ISBN: (纸本)9788363578190

In recent years, the development of personal computers hardware has been aimed at increasing the number of processor cores. At the same time, the efficiency and reliability of computer interconnecting networks is increased. This enables the introduction and development of parallel and distributed processing methods also in CAD systems. The paper presents the problems related to the parallelization of the computational process and the methods of solving them, based on the example of typical computational experiments used in the design and optimization of integrated circuits topography.

关键词： parallel computing distributed computing CAD integrated circuit design

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Accuracy of parallel Distance Mapping Algorithms When Applied to Sub-Pixel Precision Transform 23rd

Accuracy of Parallel Distance Mapping Algorithms When Appli...

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22nd Scandinavian conference on image Analysis, SCIA 2023

作者： Ericsson, Daniel Detterfelt, Åsa Ragnemalm, Ingemar Mindroad AB Teknikringen 1F Linköping583 30 Sweden Linköping University Linköping581 83 Sweden

ISBN: (纸本)9783031314346

The problem of distance mapping is a thoroughly studied topic in the field of image processing. Much focus has been spent on perfecting algorithms, calculating the Euclidean Distance Transform on binary images, in order to achieve the highest accuracy as efficiently as possible. Less focus has been spent on perfecting algorithms calculating the Euclidean Distance Transform on non-binary images where a much higher degree of precision is made possible by increasing the granularity of the input image. This study focuses on taking three different established methods for calculating distance mapping on binary images and applying them to non-binary images. We have found that the simpler algorithms, which simply propagate distance information according to a predetermined pattern work well with our new transform. The more analytical and in the case of the binary image, exact method, however, does not work as well with our new transform. Its way of propagating distance information is making too strict assumptions about the geometry of an image, which hold true for binary images, but not for non-binary images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Binary images

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ESNet: An Efficient Real-time Semantic Segmentation Network

ESNet: An Efficient Real-time Semantic Segmentation Network

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Renping Xie Cong He Ming Tao Kai Ding School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Efficient image segmentation algorithms are critical in computer vision, as they maintain high processing speeds while handling large amounts of data and providing practical solutions in resource-limited environments. While existing classical segmentation methods achieve good results, their real-time performance can be further improved. To address this issue, we propose an efficient segmentation network (ESNet) to capture extensive contextual information and improve segmentation fineness, which are two main issues in semantic segmentation, while ensuring real-time capability. Then, we first introduce an efficient context module (ECM) that effectively enlarges the effective receptive field (ERF) of the model and improves its performance in integrating contextual information. Subsequently, a skip connection with a simple feature fusion module (SFFM) is designed to provide rich detail information, thereby improving segmentation fineness. The efficacy of ESNet was evaluated on the PASCAL VOC2012 semantic segmentation dataset against several state-of-the-art methods. ESNet achieves 71.4 FPS at a resolution of 1024×2048 on a single NVIDIA GeForce RTX 3090 GPU and achieves 62.42% mIoU at a resolution of 320×480 with an extremely simple training recipe, striking a fine balance between segmentation accuracy and inference speed.

关键词： Training distributed processing Computer vision Accuracy Semantic segmentation Computational modeling Graphics processing units Rendering (computer graphics) Real-time systems Context modeling

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Underwater image Enhancement Method Based on Polarized images Fusion and Quality Evaluation

Underwater Image Enhancement Method Based on Polarized Image...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Bowen Guan Yan Wang Jiankai Yin School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

Underwater images suffer from blur, low contrast, color distortion and loss of details. Recently, since polarized images can provide more information, many underwater image enhancement(UIE) methods based on polarized images are proposed. Most existing polarized methods usually enhance images by physical techniques based on some priors or assumptions. However, the priors or assumptions are not always reliable in the real scene, which limits their performance. Besides, existing deep learning methods fuse four polarized images at different angles ($\mathbf{I}_{0-3}$) into a clear visible light image, which only use simple fusion methods and do not preserve image information well. In order to avoid the limitation of priors and obtain the rich information contained in polarized images more reasonable, we propose a novel framework that using the quality maps to guide the fusion of the four images to enhance images, called QualityGuided Enhancement Network(QGE-Net). Specifically, QGE-Net can be divided into Quality Evaluator and Enhancement Module. A loss called SROCC Rank Loss is proposed to train Quality Evaluator, which can improve the ability of Quality Evaluator to obtain differential information from different images in the same scene. In Enhancement Module, a module called MultiScale Quality Guidance Fusion(MQGF) is proposed to fuse the features of polarized images under the guidance of the quality map to make full use of the information of the input image. Extensive experimental results show that the proposed method achieves superior performance against state-of-the-art methods on both synthetic and real data.

关键词： Deep learning Fuses image color analysis Distortion Reliability Artificial intelligence image enhancement

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Effective GPU parallelization of distributed and Localized Model Predictive Control 17

Effective GPU Parallelization of Distributed and Localized M...

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IEEE 17th International conference on Control and Automation (ICCA)

作者： Alonso, Carmen Amo Tseng, Shih-Hao CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

ISBN: (纸本)9781665495721

To effectively control large-scale distributed systems online, model predictive control (MPC) has to swiftly solve the underlying high-dimensional optimization. There are multiple techniques applied to accelerate the solving process in the literature, mainly attributed to software-based algorithmic advancements and hardware-assisted computation enhancements. However, those methods focus on arithmetic accelerations and overlook the benefits of the underlying system's structure. In particular, the existing decoupled software-hardware algorithm design that naively parallelizes the arithmetic operations by the hardware does not tackle the hardware overheads such as CPU-GPU and thread-to-thread communications in a principled manner. Also, the advantages of parallelizable subproblem decomposition in distributed MPC are not well recognized and exploited. As a result, we have not reached the full potential of hardware acceleration for MPC. In this paper, we explore those opportunities by leveraging GPU to parallelize the distributed and localized MPC (DLMPC) algorithm. We exploit the locality constraints embedded in the DLMPC formulation to reduce the hardware-intrinsic communication overheads. Our parallel implementation achieves up to 50x faster runtime than its CPU counterparts under various parameters. Furthermore, we find that the locality-aware GPU parallelization could halve the optimization runtime comparing to the naive acceleration. Overall, our results demonstrate the performance gains brought by software-hardware co-design with the information exchange structure in mind.

关键词： Runtime Instruction sets Graphics processing units Performance gain Prediction algorithms Optimization Information exchange

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parallel Data Stream Anonymization methods: A Review 2

Parallel Data Stream Anonymization Methods: A Review

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2nd International conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Trivedi, Brinit Degadwala, Sheshang Vyas, Dhairya Sigma Institute of Engineering Dept. of Comp. Eng Gujarat Vadodara India Shree Drashti Infotech Llp Gujarat Vadodara India

ISBN: (纸本)9781665400527

Sustainable stream processing algorithms have gained popularity in recent years. Flow control is a way of searching and modifying real-time data streams. Missing values are ubiquitous in real-world data streams, making data stream privacy challenging to safeguard. On the other hand, most privacy preservation methods need not take absent values into account when developed. They can anonymize data in certain study, however this results in data loss. This research proposes a unique parallel distributed approach for protecting privacy while using incomplete data streams. This method uses a production computational system to continually anonymize data streams, using clustering to construct each tuple. It clusters data in partial and complete forms using variable and array dimensions as similarity metrics. In order to prevent values and outliers' pollution, a generalization approach that is based on more than matches is used. The experiments used real data to compare current systems with varied settings. This research will cover several anonymization mechanisms and their advantages. There are also drawbacks. Finally, the future of continuous data anonymization research has been explored. © 2022 IEEE.

关键词： Measurement Data privacy Privacy Pollution distributed databases Production Real-time systems

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Face Recognition and Expression Analysis Technology of image Signal processing in Digital Media

Face Recognition and Expression Analysis Technology of Image...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Yuan Zhu National demonstration center for experimental designing art education Tai Yuan University of Technology Jinzhong Shanxi China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

With the rapid development of digital media technology, image analysis plays a vital role in the fields of digitalization and informatization. This article will provide an in-depth explanation of face recognition and classification, morphological modeling principles, and processing methods. Through the analysis and research on feature extraction of graphic signals, this article designs corresponding algorithms and uses MATLAB toolbox diagram software to verify the experimental results. Research shows that the recognition rate of angry and happy expressions is 100% within a distance of 300m. Then, like the recognition rate of the other three expressions, as the recognition distance increases, the overall trend decreases, but the overall recognition rate is above 90%. This shows that the recognition accuracy of this system is relatively high. At the same time, the lowest error and omission rate of this system is 0%, and the highest error and omission rate is 10%.

关键词： Analytical models image recognition Fourier transforms Face recognition Signal processing algorithms Media Feature extraction

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FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

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12th National CCF conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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