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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11314 条记录，以下是4611-4620 订阅

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N-Queens-based algorithm for moving object detection in distributed wireless sensor networks

N-Queens-based algorithm for moving object detection in dist...

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30th international conference on Information Technology Interfaces

作者： Stojkoska, Biljana Davcev, Danco Vladimir, Trajkovik Fac Elect Engn & Informat Technol Skopje North Macedonia

ISBN: (纸本)9789537138127

The main constraint of wireless sensor networks (WSN) in enabling wireless image communication is the high energy requirement, which may exceed even the future capabilities of battery technologies. In this paper we have shown that this bottleneck can be overcome by developing local in-network image processing algorithm that offers optimal energy consumption. Our algorithm is very suitable for intruder detection applications. each node is responsible for processing the image captured by the video sensor, which consists of NxN blocks. If an intruder is detected in the monitoring region, the node will transmit the image for further processing. Otherwise, the node takes no action. Results provided from our experiments show that our algorithm is better than the traditional moving object detection techniques by a factor of (N/2) in terms of energy savings.

关键词： wireless sensor networks multimedia image processing motion detection

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Leveraging Neural Networks to Enhance Large-Scale Data processing Performance 3

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pandey, Megha Vinay Kumar, S.B. Khurana, Jatin Kalpana, B. Pathak, Kishor R. Kumar, Mukesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India 1 Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350319125

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications. © 2023 IEEE.

关键词： applications Data Networks presented processing

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TorqueDB: distributed Querying of Time-Series Data from Edge-local Storage 26th

TorqueDB: Distributed Querying of Time-Series Data from Edge...

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Garg, Dhruv Shirolkar, Prathik Shukla, Anshu Simmhan, Yogesh Ericsson Res Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9783030576752;9783030576745

The rapid growth in edge computing devices as part of Internet of Things (IoT) allows real-time access to time-series data from 1000's of sensors. Such observations are often queried to optimize the health of the infrastructure. Recently, edge storage systems allow us to retain data on the edge rather than moving them centrally to the cloud. However, such systems do not support flexible querying over the data spread across 10-100's of devices. There is also a lack of distributed time-series databases that can run on the edge devices. Here, we propose TorqueDB, a distributed query engine over time-series data that operates on edge and fog resources. TorqueDB leverages our prior work on ElfStore, a distributed edge-local file store, and InfluxDB, a time-series database, to enable temporal queries to be decomposed and executed across multiple fog and edge devices. Interestingly, we move data into InfluxDB on-demand while retaining the durable data within ElfStore for use by other applications. We also design a cost model that maximizes parallel movement and execution of the queries across resources, and utilizes caching. Our experiments on a real edge, fog and cloud deployment show that TorqueDB performs comparable to InfluxDB on a cloud VM for a smart city query workload, but without the associated monetary costs.

关键词： Edge storage Time-series database distributed querying Internet of Things Cloud computing

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Euro-Par 2009 parallel processing - 15th international Euro-Par conference, Proceedings

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Euro-Par 2009 parallel processing - 15th international Euro-Par conference, Proceedings

ISBN: (纸本)3642038689

The proceedings contain 85 papers. The topics discussed include: multicore programming challenges;assigning blame: mapping performance to high level parallel programming abstractions;a holistic approach towards automated performance analysis and tuning;an extensible I/O performance analysis framework for distributed environments;grouping MPI processes for partial checkpoint and co migratiion;process mapping for MPI collective communications;stochastic analysis of hierarchical publish/subscribe systems;characterizing and understanding the bandwidth behavior of workloads on multi core processors;hubrid techniques for fast multicore simulation;a methodology to characterize critical section bottlenecks in DSM multiprocessors;dynamic load balancing of matrix-vector multiplications on roadrunner compute nodes;and a unified framework for load distribution and fault-tolerance of application servers.

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Proceedings - IEEE international conference on Cluster Computing, ICCC

Proceedings - IEEE International Conference on Cluster Compu...

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IEEE international conference on Cluster Computing, CLUSTER 2002

ISBN: (纸本)0769517455

The proceedings contain 58 papers. The topics discussed include: blades - an emerging system design model for economic delivery of high performance computing;an agent-based infrastructure for parallel java on heterogeneous clusters;leveraging standard core technologies to programmatically build Linux cluster appliances;clusters as large-scale development facilities;experience in offloading protocol processing to a programmable NIC;selective buddy allocation for scheduling parallel jobs on clusters;performance and implementation of distributed data CPHF and SCF algorithms;trends in high performance computing and using numerical libraries on clusters;first light of the earth simulator and its PC cluster applications;protocol-dependent message-passing performance on Linux clusters;and adaptive message management using hybrid channel model in parallel file system.

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DSP Based Acceleration for Long Short-Term Memory Model Based Word Prediction Application 10

DSP Based Acceleration for Long Short-Term Memory Model Base...

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10th international conference on Intelligent Computation Technology and Automation (ICICTA)

作者： Zhu, Keqian Jiang, Jingfei Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410000 Hunan Peoples R China

ISBN: (纸本)9781538612309

Neural network based deep learning algorithm is a study hotspot in artificial intelligence. Moreover, embedded artificial intelligence and mobile computing are becoming more and more important in industry. For these applications, not only high performance computing is required, but also low power consumption restriction cannot be ignored. DSP has special hardware architecture with characteristics of high performance and low power consumption, which is an ideal computing platform for embedded artificial intelligence. This paper concerns the energy efficiency of DSP under deep learning applications. However, many relative researches are insufficient in application scale and optimization techniques. This research extends application scale and puts forward some optimization methods in detail. Specifically, for the Long Short-Term Memory (LSTM) model based word prediction application, we use TI's high-performance multi-core DSP to accelerate its inference process. We apply a variety of optimization techniques to our initial DSP program. Relative experimental results show that these techniques bring notable performance improvement. Furthermore, we regard the MATLAB program which runs on general CPU and C program which runs on ARM as contrast. In terms of performance and power ratio, DSP is 7.79 times over general CPU and 2.28 times over ARM, which indicates that DSP is a suitable platform for embedded artificial intelligence.

关键词： Deep Learning Embedded Artificial Intelligence Multi-core DSP Long Short-Term Memory Model Acceleration

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Implementing Sparse Matrix-Vector multiplication using CUDA based on a hybrid sparse matrix format

Implementing Sparse Matrix-Vector multiplication using CUDA ...

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2010 international conference on Computer Application and System Modeling, ICCASM 2010

作者： Cao, Wei Lu, Yao Li, Zongzhe Wang, Yongxian Wang, Zhenghua National Key Lab. for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781424472369

The Sparse Matrix-Vector product (SpMV) is a key operation in engineering and scientific computing. Methods for efficiently implementing it in parallel are critical to the performance of many applications. Modern Graphics processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA's CUDA, have gained interest as a viable architecture for data-parallel general purpose computations. Currently, SpMV implementations using CUDA based on common sparse matrix format have already appeared. Among them, the performance of implementation based on ELLPACK-R format is the best. However, in this implementation, when the maximum number of nonzeros per row does substantially differ from the average, thread is suffering from load imbalance. This paper proposes a new matrix storage format called ELLPACK-RP, which combines ELLPACK-R format with JAD format, and implements the SpMV using CUDA based on it. The result proves that it can decrease the load imbalance and improve the SpMV performance efficiently. © 2010 IEEE.

关键词： Graphics processing unit

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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th international conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential processing techniques. This paper proposes a Multi-Core parallel processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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Personalized mobile assistant applications using cognitive techniques

Personalized mobile assistant applications using cognitive t...

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1st international conference on parallel, distributed Computing Technologies and applications, PDCTA 2011

作者： Saboo, Rohan Sourav Agarwal, Rakseh Agarwal, Rohit Patil, Kiran Kumari Kumar, B. P. Vijaya Department of Computer Science and Engineering REVA Institute of Technology and Management Bangalore India M.S. Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9783642240362

Developing a personalized, user-centric system to provide a peaceful and a better quality life is one of today's challenging issues. Cognition is the scientific term for "the process of thought". It refers to the processing of information, applying knowledge, and changing preferences. The paper discusses the development of a Cognition Based Personal Assistance Application, the idea of how we can use a smart phone to learn about the user's daily life pattern in a cognitive way and provide him assistance, making his/her life better and with less tension. In this paper we have presented our design and development of mobile application based on cognitive algorithm. The project is developed and tested at Nokia research lab, RITM, Bangalore, India. © 2011 Springer-Verlag.

关键词： Smartphones

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Adaptive data consistency management in collaborative editing environment

Adaptive data consistency management in collaborative editin...

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Proceedings - 2004 international conference on Information and Communication Technologies: From Theory to applications, ICTTA 2004

作者： Omar, Mohd Adib Haron, Fazilah Abdullah, Rosni Parallel/Distr. Computing Centre School of Computer Science Universiti Sains Malaysia 11800 Penang Malaysia

ISBN: (纸本)0780384822

The techniques to achieve data consistency using operational transformation approach to support real-time and non-real-time collaborative editing are discussed. A proposed collaborative editing prototype system called Adaptive Collaborative Editing (ACE) helps in demonstrating the adaptive data consistency management, adaptive logical time-stamping and adaptive notification. The collaborative environment enables computer-based collaboration in wide-area network. The main objective of a collaborative environment is to allow coherent and consistent object sharing and manipulation by distributed users.

关键词： Computer supported cooperative work

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