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IEEE International conference on Communications (IEEE ICC)

作者： Fujihashi, Takuya Koike-Akino, Toshiaki Watanabe, Takashi Orlik, Philip V. MERL Cambridge MA 02139 USA Ehime Univ Grad Sch Sci & Engn Matsuyama Ehime Japan Osaka Univ Grad Sch Informat & Sci Osaka Japan

ISBN: (纸本)9781538680889

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multidimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.

关键词： image coding Generative adversarial networks Generators Convolution Neural networks Redundancy Training"

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Multi-installment divisible load processing in heterogeneous distributed systems

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2007年第17期19卷 2237-2253页

作者： Drozdowski, M. Lawehda, M. Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Poznan Supercomp & Networking Ctr PL-61704 Poznan Poland

Divisible loads are parallel applications with fine granularity and negligible data dependencies. Such computations can be divided into parts of arbitrary sizes and processed independently in parallel. The load distribution process incurs considerable communication delays. To reduce processor waiting time during the computation initialization phase, the load is distributed in multiple small installments rather than in one big chunk. In this paper we analyze multi-installment divisible load processing in heterogeneous distributed systems. Scheduling divisible loads in heterogeneous systems is hard because the sizes of the installments should be adjusted to the communication and computation capabilities of the system. We show that ignoring heterogeneity of the distributed system may result in arbitrarily bad solutions. Two algorithms are proposed to gear the load chunk sizes to different communication and computation speeds: an optimization branch-and-bound algorithm and a heuristic based on a genetic search method. The running times of both methods and the quality of the solutions are compared. Then, we use these algorithms to study the features of the multi-installment divisible load scheduling problem. We demonstrate that it has both combinatorial and algebraic nature, and that optimum solutions are harder to find with the growing heterogeneity of the system. Copyright (c) 2007 John Wiley & Sons, Ltd.

关键词： scheduling and load balancing divisible loads multi-installment processing heterogeneous systems combinatorial optimization algorithms

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Communication and computation patterns of large scale image convolutions on parallel architectures

Communication and computation patterns of large scale image ...

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International Symposium on parallel processing

作者： S.G. Dykes Xiaodong Zhang Yan Zhou Haixu Yang High Performance Computing and Software Laboratory University of Texas San Antonio San Antonio TX USA

Segmentation and other image processing operations rely on convolution calculations with heavy computational and memory access demands. The article presents an analysis of a texture segmentation application containing a 96/spl times/96 convolution. Sequential execution required several hours an single processor systems with over 99% of the time spent performing the large convolution. 70% to 75% of execution time is attributable to cache misses within the convolution. We implemented the same application on CM-5, iPSC/860 and PVM distributed memory multicomputers, tailoring the parallel algorithms to each machine's architecture. parallelization significantly reduced execution time, taking 49 seconds on a 512 node CM-5 and 6.5 minutes on a 32 node iPSC/860. The results indicate for large kernel convolutions the size and bandwidth of the fast memory store is more important than processor power or communication overhead.< >

关键词： Concurrent computing Large-scale systems parallel architectures image segmentation image processing Kernel Laboratories Bandwidth Pixel Smoothing methods

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Automated image and Video Quality Assessment for Computational Video Editing 9th

Automated Image and Video Quality Assessment for Computation...

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9th International conference on Analysis of images, Social Networks and Texts, AIST 2020

作者： Lomotin, Konstantin Makarov, Ilya HSE University Moscow Russia Samsung-PDMI Joint AI Center St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia

ISBN: (纸本)9783030726096

We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos. We present a complex framework for assessing the quality of images and videos. The scoring process consists of several parallel steps of metric collection with final score aggregation step. Most of the individual scoring models are based on deep convolutional neural networks (CNN). The framework can be flexibly extended or reduced by adding or removing these steps. Using Deep CNN-Based Blind image Quality Predictor (DIQA) as a baseline for IQA, we proposed improvements based on two patching strategies, such as uniform patching and object-based patching, and add intelligent pre-training step with distortion classification. We evaluated our model on three IQA benchmark image datasets (LIVE, TID2008, and TID2013) and manually collected short YouTube videos. We also consider interesting for automated video editing metrics used for video scoring based on the scale of a scene, face presence in frame and compliance of the shot transitions with the shooting rules. The results of this work are applicable to the development of intelligent video and image processing systems. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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parallel High Utility Itemset Mining Algorithm on the Spark 18th

Parallel High Utility Itemset Mining Algorithm on the Spark

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18th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Li, Chengyan Zhang, Lei Sun, Anqi Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9789819996360

In the field of efficient utility itemset mining, considering both internal and external utility values provides a more comprehensive approach compared to traditional frequency-based methods. However, the increased complexity of computations and the generation of numerous candidate itemsets pose challenges for efficient mining on large-scale datasets. To address these challenges, this paper proposes a parallel mining algorithm based on the Spark framework. The algorithm leverages a vertical dataset structure to efficiently store and process the data. A utility table is utilized to store the data items along with their corresponding transaction utility values. By utilizing the utility table, the algorithm can directly access transaction utility values, simplifying the computation process and reducing overhead. To further enhance efficiency, the algorithm combines a prefix partitioning strategy with a minimum utility threshold. By employing this strategy, the generation of candidate itemsets is effectively reduced, resulting in a smaller search space and enhancing the efficiency of the mining process. The algorithm is implemented on the Spark framework, leveraging its capabilities in parallel processing and scalability. By leveraging the distributed computing capabilities of Spark, the algorithm can efficiently mine efficient utility frequent item-sets from large-scale datasets. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm in performing efficient utility itemset mining. It surpasses traditional approaches and showcases its ability to handle large-scale datasets while maintaining high performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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A Comparison of Road-Network-Constrained Trajectory Compression methods

A Comparison of Road-Network-Constrained Trajectory Compress...

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International conference on parallel and distributed Systems (ICPADS)

作者： Yudian Ji Hao Liu Xiaoying Liu Ye Ding Wuman Luo The Hong Kong University of Science and Technology University of Macau

ISBN: (纸本)9781509053827

The popularity of location-acquisition devices has led to a rapid increase in the amount of trajectory data collected. The large volume of trajectory data causes the difficulties of storing and processing the data. Various trajectory compression methods are therefore proposed to deal with these problems. In this paper, we overview the existing road-network-constrained trajectory compression methods and propose a novel classification based on the features leveraged by them. We also propose new methods that fill in the research blanks indicated by the classification. We conduct a thorough comparison among the existing and new road-network-constrained trajectory compression methods. The performances of the methods are studied via various metrics on real-world dataset. We make new discoveries regarding the performances and the scalability of existing methods, and provide guidelines of road-network-constrained trajectory compression for various scenarios.

关键词： Trajectory Roads Encoding Compression algorithms Scalability Routing Presses

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An improved Vandermonde Decoding PE for distributed Storage System

An improved Vandermonde Decoding PE for Distributed Storage ...

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

作者： Ruizhen Wu Yan Wu Ping Huang Lin Wang Inspur Electronic Information Industry Co. Ltd Xi’an China

ISBN: (纸本)9781728190181;9781728190198

An improved Vandermonde decoding PE for distributed storage system is proposed in this paper. The PE first give a decoding algorithm based on matrix partition and Lagrange interpolation method to split the complex matrix invention to small Vandermonde matrix's parallel parameter calculation. PE architecture is constructed by hardware working manner in Galois field of GF (2⁸). The PE is designed to work for both encoding and decoding with same module but different data and operation. The PE number is configurable to have best performance in different erasure codes “r” situations backward compatible by the control of data block distribution and Vandermonde erasure codes data selection. The hardware architecture is fully verified at RTL level and synthesized with GF 12nm technology lib and simulation results showed 60.6% to 79% speed advantages compared to Gaussian elimination.

关键词： Technological innovation Interpolation image coding Simulation Pipelines Computer architecture Hardware

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License Plate Detection methods Based on OpenCV

License Plate Detection Methods Based on OpenCV

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

作者： Lin Xu Wenqian Shang Weiguo Lin Wei Huang School of Computer Science and Cybersecurity Communication University of China Beijing China

With the popularization of automobile and the progress of computer vision detection technology, intelligent license plate detection technology has gradually become an important part of intelligent traffic management. License plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other fields. In this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient detection. Basing on OpenCV and visual studio 2012 under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection accuracy. These methods have high efficiency and good interactivity, which provide a reference for later license plate recognition.

关键词： Visualization image edge detection Transportation Licenses Optical character recognition software Monitoring Software engineering

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Overlapping chromosome segmentation model based on dual attention U-Net network 7

Overlapping chromosome segmentation model based on dual atte...

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7th International conference on Advanced Electronic Materials, Computers, and Software Engineering, AEMCSE 2024

作者： Wang, Rui Yao, Minghui School of Artificial Intelligence TianGong University Tianjin300387 China School of Aeronautics and Astronauts TianGong University Tianjin300387 China

ISBN: (纸本)9781510681866

In order to isolate each chromosome more accurately and completely, a two-stage method of processing overlapping chromosomes was proposed. In the first stage, In the first stage, we propose a DA-UNet (dual attention U-Net) network based on improved U-Net network to segment overlapping and non-overlapping regions of chromosomes. In the segmentation network, the encoder part was replaced by the pre-trained ResNet50 extraction feature to accelerate the convergence speed. A dual attention module was designed at the jump junction, combining the features of a multi-level encoder and a parallel decoder. In the second stage, the output of the segmentation network was matched geometrically to obtain a single chromosome. Experiments were carried out on the chromosome segmentation dataset, and the overall accuracy of this method reached 94.70%, which was 5.16% higher than the existing chromosome segmentation and reconstruction methods. Experimental results show that the proposed method can achieve accurate segmentation and reconstruction of chromosome images. © 2024 SPIE.

关键词： image segmentation

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GPU Accelerated Ultrasonic Tomography Using Propagation and Backpropagation Method

GPU Accelerated Ultrasonic Tomography Using Propagation and ...

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25th ACM/IEEE International conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Bello, Pedro D. Jin, Yuanwei Lu, Enyue Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA Univ Maryland Baltimore Cty Dept Engn & Aviat Sci Baltimore MD 21228 USA Salisbury Univ Dept Math & Comp Sci Baltimore MD USA

ISBN: (纸本)9780769549569;9781467362184

This paper develops implementation strategy and method to accelerate the propagation and backpropagation (PBP) tomographic imaging algorithm using Graphic processing Units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to develop our parallelized algorithm since the CUDA model allows the user to interact with the GPU resources more efficiently than traditional Shader methods. The results show an improvement of more than 80x when compared to the C/C++ version of the algorithm, and 515x when compared to the MATLAB version while achieving high quality imaging for both cases. We test different CUDA kernel configurations in order to measure changes in the processing-time of our algorithm. By examining the acceleration rate and the image quality, we develop an optimal kernel configuration that maximizes the throughput of CUDA implementation for the PBP method.

关键词： Medical Imaging Ultrasonic Tomography GPU CUDA parallel Computing

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