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检索条件"任意字段=5th International Conference on Algorithms and Architectures for Parallel Processing"

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Successive-parallel Interference Cancellation Multi-user Detection Algorithm for MUSA Uplink 10th

Successive-Parallel Interference Cancellation Multi-user Det...

引用

10th European-Alliance-for-Innovation (EAI) international conference on Wireless and Satellite Systems (WiSATS)

作者： Wu, Shaochuan Zuo, Rundong Zhang, Wenbing Song, Yanwu Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China

ISBN: (纸本)9783030191566;9783030191559

With the approaching of Internet of things (IoT), non-orthogonal multiple access technology was proposed in the fifth generation (5G) mobile communication system to improve the system capacity and meet the needs of massive connectivity. Multi-User Shared Access (MUSA) technology is a non-orthogonal multiple access technology of code domain. MUSA receiver adopts multi-user detection algorithm, mainly using interference cancellation based on linear detection. this paper proposes the successive-parallel interference cancellation multi-user detection algorithm for the shortage of typical multi-user detection algorithms of MUSA uplink receiver, and gives the comparison results of the proposed algorithm and typical algorithms. Compared with parallel interference cancellation detection algorithm, the proposed algorithm improves the detection performance greatly. Compared with successive interference cancellation detection algorithm, the proposed algorithm reduces the processing time delay effectively.

关键词： Multi-User Shared Access Multi-user detection Interference cancellation Time delay

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Time series mining at petascale performance 35th

Time series mining at petascale performance

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35th international conference on High Performance Computing, ISC High Performance 2020

作者： Raoofy, Amir Karlstetter, Roman Yang, Dai Trinitis, Carsten Schulz, Martin Computer Architecture and Parallel Systems Technical University of Munich Garching Germany IfTA GmbH Puchheim Germany NVIDIA Munich Germany

ISBN: (纸本)9783030507428

the mining of time series data plays an important role in modern information retrieval and analysis systems. In particular, the identification of similarities within and across time series has garnered significant attention and effort over the last few years. For this task, the class of matrix profile algorithms, which create a generic structure that encodes correlations among records and dimensions—the matrix profile—is a promising approach, as it allows simplified post-processing and analysis steps by examining the resulting matrix profile structure. However, it is expensive to create a matrix profile: it requires significant computational power to evaluate the distance among all subsequence pairs in a time series, especially for very long and multi-dimensional time series with a large dimensionality. Existing approaches are limited in their scalability, as they do not target High Performance Computing systems, and—for most realistic problems—are suited only for datasets with a small dimensionality. In this paper, we introduce a novel MPI-based approach for the calculation of a matrix profile for multi-dimensional time series that pushes these limits. We evaluate the efficiency of our approach using an analytical performance model combined with experimental data. Finally, we demonstrate our solution on a 128-dimensional time series dataset of 1 million records, solving 274 trillion sorts at a sustained 1.3 Petaflop/s performance on the SuperMUC-NG system. © Springer Nature Switzerland AG 2020.

关键词： Time series

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parallel multi-scale CNN for image denoising 5

Parallel multi-scale CNN for image denoising

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5th international conference on Communication and Information processing, ICCIP 2019

作者： Zhao, Taoling Zhong, Yi Wang, Yuanli Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology WuhanHubei China

ISBN: (纸本)9781450372589

Deep learning has achieved outstanding performance in the field of image processing. Inspired by the inception model, we propose a deep neural network convolving simultaneously at two different scales. We utilize the convolution kernel of different sizes to carry out the convolution, which can obtain the features of the image at different scales. the features of the output are no longer uniformly distributed, but converge the features with strong correlation. Batch normalization (BN) and residual learning (RL) are utilized to speed up the training process as well as enhance the denoising ability of model. the experimental results of three salt- and-pepper noise levels based on the public dataset BSDS500 show that the newly proposed method can obtain significantly superior performance than current state-of-the-art approaches and higher peak signal to Noise Ratio (PSNR) in salt-and-pepper noise removal. © 2019 Association for Computing Machinery.

关键词： Salt and pepper noise

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Performance of some image processing algorithms in TensorFlow 25

Performance of some image processing algorithms in TensorFlo...

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25th international conference on Systems, Signals and Image processing (IWSSIP)

作者： Demirovic, Damir Skejic, Emir Serifovic-Trbalic, Amira Univ Tuzla Fac Elect Engn Franjevacka 2 Tuzla 75000 Bosnia & Herceg

ISBN: (纸本)9781538669792

Signal, image and Synthetic Aperture Radar imagery algorithms in recent time are used in a daily routine. Due to huge data and complexity, their processing is almost impossible in a real time. Often image processing algorithms are inherently parallel in nature, so they fit nicely into parallel architectures multicore Central processing Unit (CPU) and Graphics processing Unit GPUs. In this paper image processing algorithms were evaluated, which are capable to execute in parallel manner on several platforms CPU and GPU. All algorithms were tested in TensorFlow, which is a novel framework for deep learning, but also for image processing. Relative speedups compared to CPU were given for all algorithms. TensorFlow GPU implementation can outperform multi-core CPUs for tested algorithms, obtained speedups range from 3.6 to 15 times.

关键词： image processing tensorflow parallel processing central processing unit graphics processing unit

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Implementation of the FDTD method block algorithm in the MATLAB language using a graphics processing unit. 2D-decomposition case 5

Implementation of the FDTD method block algorithm in the MAT...

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5th international conference on Information Technology and Nanotechnology, ITNT 2019

作者： Morunov, N.D. Golovashkin, D.L. Samara National Research University Moskovskoe Shosse 34A Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

the problem of limited video memory when organizing parallel computing using the FDTD method on a non-professional graphics processor was considered in this article. As a solution, a block algorithm of the FDTD method with 2-D decomposition and its implementation in the MATLAB language are proposed. As a result, the problem of limited graphics memory was solved, the maximum possible discretization of the grid in calculations on the GPU was expanded from 10 million to 85 million nodes with an average acceleration of 7,5 times for the two-dimensional case of the FDTD method. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Graphics processing unit

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V international conference on Information Technology and Nanotechnology, ITNT 2019 - 3. Image processing and Earth Remote Sensing

V International Conference on Information Technology and Nan...

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5th international conference on Information Technology and Nanotechnology, ITNT 2019

the proceedings contain 251 papers. the topics discussed include: investigation of the multiple comparisons problem in the analysis of the wave train electrical activity of muscles in Parkinson's disease patients;addressing system and routing without tables in new generation networks;CUDA parallel programming technology application for analysis of big biomedical data based on computation of effectiveness features;autoregressive models of random fields on the circle;detection and identification of objects on multispectral satellite images;creation of zinc oxide based nanomaterials by repetitively pulsed laser treatment;long-haul few mode fiber optic link with differential mode delay compensation;dynamics of the electrochemical reaction behavior under the influence of random perturbations;and decomposition of enzyme kinetics equations.

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Research and Implementation of High Performance Traffic processing based on Intel DPDK 9

Research and Implementation of High Performance Traffic Proc...

引用

9th international conference on parallel architectures, algorithms and Programming (PAAP)

作者： Zhu, Wenjun Li, Peng Luo, Baozhou Xu, He Zhang, Yujie Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781538694039

With the rapid development of Internet and the continuous rise of network users, the network traffic in various regions is increasing rapidly. In the face of a large number of high speed and high throughput of the network environment, traditional packet capture methods and processing capabilities cannot reach the corresponding speed, which results in severe packet loss. this paper focuses on a high-performance packet acquisition and distribution method to break through the performance bottleneck of universal servers and network cards. this paper studies a packet capture method based on DPDK platform, and uses the processing of hash value in RSS to improve the efficiency of data packet distribution, which realizes the process from performance acquisition to efficiently multi-core parallel processing. this method can effectively reduce packet loss and improve the data packet processing rate. It can also reduce resource waste and network overhead for traffic capture and distribution. Preliminary experiments show that DPDK-based traffic processing has obvious advantages over PF-RING and Netmap in data processing speed.

关键词： DPDK Traffic processing Distribution Multi-core parallel processing

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A CNN Accelerator on FPGA with a Flexible Structure

A CNN Accelerator on FPGA with a Flexible Structure

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international conference on Computational Intelligence and Applications (ICCIA)

作者： Dan Shan Guotao Cong Wei Lu Department of Electronic Engineering Dalian Neusoft University of Information Dalian P. R. China School of Control Science and Engineering Dalian University of Technology Dalian P. R. China

ISBN: (数字)9781728160429

ISBN: (纸本)9781728160436

Most of the existing convolutional neural networks (CNNs) are based on PC software, which cannot meet the real-time, low power and miniaturization requirements of the systems. In this paper, a CNN accelerator with flexible structure based on Field-Programmable Gate Array (FPGA) is proposed to achieve recognition of MNIST handwritten numeric characters. the system adopts deep pipeline processing and optimizes inter-layer and intra-layer parallelism from two levels of coarse and fine granularity. In view of the similarity of convolution structure, this design adopts structured circuit, which can easily expand the number of layers and neurons. the classification throughput and inter-layer data throughput capability can be improved by rationally organizing the internal memory resources of the FPGA. Compared with the general CPU, it achieves 3 times acceleration at 50MHz frequency, while the power consumption is only 2% of the CPU. Finally performance and power consumption are compared with other accelerators by VGG16.

关键词： Convolution Neurons Field programmable gate arrays parallel processing Kernel Computer architecture System-on-chip

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Methods for emotions, mood, gender and age recognition 5

Methods for emotions, mood, gender and age recognition

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Data Science Session at the 5th international conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Pribavkin, D.D. Yakimov, P.Y. Samara National Research University Moskovskoe Shosse 34А Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

Recognition on images not only of shapes, but also of metadata is becoming increasingly popular among researchers in the field of convolutional neural networks and deep learning. this article provides an analytical overview of modern software solutions that recognize the images of emotions, mood, gender and age of a person. Enthusiasts invent all new and new architectures of convolutional neural networks, allowing to solve the tasks with considerable recognition accuracy. © 2019 CEUR Workshop Proceedings.

关键词： Emotion Recognition

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High performance radar images modelling and recognition of real objects 5

High performance radar images modelling and recognition of r...

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Data Science Session at the 5th international conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Zherdev, D.A. Prokudin, V.V. Samara National Research University Moskovskoe Shosse 34А Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

In the work there is a modernization of the parallel algorithm for the radar images formation of 3D models with the synthesis of the antenna aperture. In the formation of the scene description, the various structures are used in which it is possible to use more efficient and derived calculations. In addition, it is the topical task to recognize objects on radar images. thus, on the basis of the implemented parallel program for modelling, the high performance required for simulating multiple radar images can be achieved. © 2019 CEUR Workshop Proceedings.

关键词： Radar imaging

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