检索结果-内蒙古大学图书馆

IEEE Conference on Wireless Communications and Networking

作者： Zhonghui Wu Changqiao Xu Mu Wang Yunxiao Ma Zhongrui Wu Zhenyu Xiahou Luigi Alfredo Grieco State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Information Technology Zhejiang Financial College Zhejiang China Jiangxi Academy of Cyber Security Jiangxi China Department of Electrical and Information Engineering and CNIT Bari Italy

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

As Deep Neural Networks (DNNs) are evolving in complexity to meet the demands of novel applications, a single device becomes insufficient for training, leading to the emergence of distributed DNN training. However, this evolution exposes a gap in research surrounding security vulnerabilities on model poisoning attacks, especially in model parallel setups, an area that has been scarcely studied. To bridge this gap, we introduce Patronus, an approach that counters model poisoning attacks in distributed DNN training, accommodating both data and model parallelism. With the employment of Loss-aware Credit Evaluation, Patronus scores each participating client. Based on the continuously updated credit, malicious clients are isolated and detected after multiple epochs by Shuffling-based Isolation Mechanism. Additionally, the training system is reinforced by Byzantine Fault-tolerant Aggregation to minimize malicious client impacts. Comprehensive experiments confirm Patronus's superior reliable and efficient performance over the existing methods under attack scenarios.

关键词： Training Fault tolerance image edge detection Fault tolerant systems distributed databases Artificial neural networks parallel processing

来源：评论

学校读者我要写书评

暂无评论

Dynamic Checkpointing for Heterogeneous IoT Devices Through Self-Referencing

Dynamic Checkpointing for Heterogeneous IoT Devices Through ...

引用

International Symposium on parallel and distributed processing with Applications, ISPA

作者： Nan Wang Ziyi Wang Lijun Lu Zhiyuan Ma Qun Chao School of Information Science and Engineering East China University of Science and Technology Shanghai China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Failure recovery is one of the most essential problems in Internet of Things (IoT) systems, and the conventional snapshot method is an effective way to solve this problem. However, snapshot methods lack specialized designs for heterogeneous IoT devices, and when implemented in edge devices, serious system interruptions occur and performance is impacted. To address these problems, a dynamic checkpointing strategy is proposed for IoT systems that consist of heterogeneous devices. Firstly, an anomaly detection network for snapshots (i.e., ADSnet) that combines long short-term memory networks with multilayer convolutional networks is used to learn the multidimensional features of system resource usage. Secondly, ADSnet is tuned during deployment to learn the behaviors of target devices, so that ADSnet can report the anomalies of target devices in the near future. Finally, a dynamic checkpointing strategy is proposed to dynamically create snapshots on the basis of the anomaly detection results. The experimental results show that the proposed ADSnet achieves 97.73% accuracy in detecting anomalies in the target device; furthermore, our proposed dynamic checkpointing strategy reduces 25.4% snapshots than that created by the recently proposed ResCheck.

关键词： Checkpointing Performance evaluation Accuracy Design methodology image edge detection Nonhomogeneous media Feature extraction Internet of Things Anomaly detection Long short term memory

来源：评论

学校读者我要写书评

暂无评论

THE ARIA-S1-GUNW: THE ARIA SENTINEL-1 GEOCODED UNWRAPPED PHASE PRODUCT FOR OPEN INSAR SCIENCE AND DISASTER RESPONSE

THE ARIA-S1-GUNW: THE ARIA SENTINEL-1 GEOCODED UNWRAPPED PHA...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Bekaert, David Arena, Nicholas Bato, M. Grace Buzzanga, Brett Govorcin, Marin Havazli, Emre Hogenson, Kirk Hua, Hook Johnston, Andrew Karim, Mohammed Kennedy, Joseph H. Lu, Zhong Marshak, Charlie Z. Meyer, Franz Owen, Susan Sangha, Simran Short, Gregory Wang, Jiahui Zinke, Robert CALTECH Jet Prop Lab Pasadena CA 91109 USA NASA Distributed Active Arch Ctr Alaska Satellite Facil Fairbanks AK 99775 USA Univ Alaska Fairbanks Geophys Inst Fairbanks AK 99775 USA Southern Methodist Univ Dept Earth Sci Dallas TX 75205 USA

ISBN: (纸本)9798350320107

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility - all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid image Analysis (ARIA) project's Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

关键词： InSAR Open Science Surface Deformation Disaster Response

来源：评论

学校读者我要写书评

暂无评论

3D Perception With Slanted Stixels on GPU

引用

IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2021年第10期32卷 2434-2447页

作者： Hernandez-Juarez, Daniel Espinosa, Antonio Vazquez, David Lopez, Antonio M. Moure, Juan C. SLAMcore Ltd London SE1 1JA England Univ Autonoma Barcelona Comp Architecture & Operating Syst Dept Bellaterra 08193 Spain Univ Autonoma Barcelona Bellaterra 08193 Spain Element AI Montreal PQ H2S 3G9 Canada Univ Autonoma Barcelona Comp Vis Ctr Bellaterra 08193 Spain

This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 x 1024 image sizes and 4 x 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier.

关键词： Graphics processing units Semantics Computational modeling Real-time systems Mathematical model image segmentation Indexes Stereo vision stixel world autonomous vehicles scene understanding computer vision embedded systems GPU acceleration

来源：评论

学校读者我要写书评

暂无评论

Algorithm and Hardware Co-Design for Efficient PMCMC Acceleration on FPGA

Algorithm and Hardware Co-Design for Efficient PMCMC Acceler...

引用

IEEE International Conference on Signal and image processing (ICSIP)

作者： Fulong Wang Shiyu Peng Yuanhao Gong Minghan Li Shuanglong Liu Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Key Laboratory of Physics and Devices in Post-Moore Era College of Hunan Province Hunan Normal University Changsha China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

The particle Markov-chain Monte Carlo (PMCMC) method is a stochastic algorithm that combines Particle Filters (PFs) and Markov-chain Monte Carlo (MCMC) techniques. This approach is widely used in Bayesian inference for high-dimensional state spaces and nonlinear, non-Gaussian dynamic systems. However, current PMCMC accelerators face significant challenges due to their intensive computational complexity and the intricate particle routing, limiting their application in real-time scenarios. To address these challenges, we propose a novel distributed PMCMC method that leverages parallel computing to enhance hardware execution speed. Additionally, our method introduces a particle exchange scheme that not only resolves the accuracy issues caused by particle routing in distributed PMCMC but also achieves faster computing speed. Our design is implemented on a Xilinx Kintex-7 xc7k480t FPGA device. Experimental results demonstrate that our accelerator is nearly 65 ×faster than CPU performance, and provides speedups up to 5× compared to existing FPGA-based accelerators.

关键词： Performance evaluation Monte Carlo methods Accuracy Particle accelerators Computer architecture Routing Hardware

来源：评论

学校读者我要写书评

暂无评论

RiverMapper: Step-wisely Mapping the Surface Rivers on Optical Remote Sensing images

RiverMapper: Step-wisely Mapping the Surface Rivers on Optic...

引用

2022 International Conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, Peng Pan, Hengyue Yang, Ke Dou, Yong Niu, Xin National Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China Artificial Intelligence Research Center National Innovation Institute of Defense Technology BeiJing100850 China

ISBN: (纸本)9781510655171

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs. © 2022 SPIE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Area-NeRF: Area-based Neural Radiance Fields

Area-NeRF: Area-based Neural Radiance Fields

引用

image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Zonxin Ye Wenyu Li Peng Qiao Yong Dou National Key Laboratory of Parallel and Distributed Computing School of Computer National University of Defense Technology Changsha China

Neural Radiance Field (NeRF) has received widespread attention for its photo-realistic novel view synthesis quality. Current methods mainly represent the scene based on point sampling of ray casting, ignoring the influence of the observed area changing with distance. In addition, The current sampling strategies are all focused on the distribution of sampling points on the ray, without paying attention to the sampling of the ray. We found that the current ray sampling strategy for scenes with the camera moving forward severely reduces the convergence speed. In this work, we extend the point representation to area representation by using relative positional encoding, and propose a ray sampling strategy that is suitable for camera trajectory moving forward. We validated the effectiveness of our method on multiple public datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA

引用

SENSORS 2022年第6期22卷 2292页

作者： Cao, Yue Guo, Shuchen Jiang, Shuai Zhou, Xuan Wang, Xiaobei Luo, Yunhua Yu, Zhongjun Zhang, Zhimin Deng, Yunkai Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, this research creatively puts forward the fully parallel processing architecture for the back projection (BP) algorithm based on Field-Programmable Gate Array (FPGA). The processing time consumption has significant advantages compared with existing methods. This article describes the BP imaging algorithm, which stands out with its high processing accuracy and two-dimensional decoupling of distance and azimuth, and analyses the algorithmic flow, operation, and storage requirements. The algorithm is divided into five core operations: range pulse compression, upsampling, oblique distance calculation, data reading, and phase accumulation. The architecture and optimisation of the algorithm are presented, and the optimisation methods are described in detail from the perspective of algorithm flow, fixed-point operation, parallel processing, and distributed storage. Next, the maximum resource utilisation rate of the hardware platform in this study is found to be more than 80%, the system power consumption is 21.073 W, and the processing time efficiency is better than designs with other FPGA, DSP, GPU, and CPU. Finally, the correctness of the processing results is verified using actual data. The experimental results showed that 1.1 s were required to generate an image with a size of 900 x 900 pixels at a 200 MHz clock rate. This technology can solve the multi-mode, multi-resolution, and multi-geometry signal processing problems in an integrated manner, thus laying a foundation for the development of a new, high-performance, SAR system for real-time imaging processing.

关键词： synthetic aperture radar (SAR) back-projection algorithm (BP) real-time image processing field-programmable gate array (FPGA)

来源：评论

学校读者我要写书评

暂无评论

High Performance Spatial and Spatio-Temporal Big Data processing

High Performance Spatial and Spatio-Temporal Big Data Proces...

引用

作者： Baig, Furqan State University of New York at Stony Brook

学位级别：Ph.D.

This dissertation presents our research and development works to address the challenges for delivering effective, scalable, and high performance methods and systems for managing, and querying complex spatial and spatio-temporal big data at multiple dimensions. This is driven mainly by big data problems emerging from geospatial applications, medical image analysis, satellite imaging, weather analytics, and collaborative spatial data collection. Managing and analyzing such data poses major challenges, including explosion of data volume, an unprecedented rate of data generation, and high complexity of handling spatial and/or temporal dynamics. Traditional spatial data processing algorithms and data structures have been developed mostly for linear processing. Despite the need, these are not optimized for modern requirements of big data processing. To this end, we present strategies for adapting spatial and spatio-temporal principles and methods to the parallel and distributed domain. In particular, we approached the problem from three different aspects. In distributed domain, recent attempts have proposed Hadoop based strategies to accelerate big spatial data processing. Hadoop based systems, however, rely on disk IO, fairly limiting system performance. Apache Spark, on the other hand, is a widely popular framework, based on similar distributed principles, providing iterative in-memory processing. Despite their efficiency, Spark based systems are oblivious to spatial characteristics and provide limited memory optimizations catering to spatial data. To address these, we designed and implemented SparkGIS; which not only provid in-memory spatial querying capabilities but also introduce a novel resource-aware dynamic query re-writing mechanism to efficiently manage distributed resources for large spatial workloads. Spatial processing is inherently compute intensive due to its complexity and multi-dimensional properties. While SparkGIS addresses in-memory aspects of spatial

关键词：

来源：评论

学校读者我要写书评

暂无评论

引用

Information Science, parallel and distributed Systems (ISPDS), International Conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：