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

作者： Cheng Wang Hailei Zou Juncheng Yin Applied Math Dept China Jiliang Univ. Hang Zhou City China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

Recently big data is a hot topic in many fields. Here we use distributed algorithms to treat big data in Chinese stock market. First the two stock indexes (Shanghai Composite and Shenzhen Composite indexes) from 1990-2019 are collected. Then the data are divided to several subsets and are analyzed separately via computer programs. Finally the results for every subset are combined due to distributed algorithms theory. In addition to big data scheme, we adopt stochastic processes theory and moment equations methods.

关键词： distributed algorithm Big data Lèvy processes Chinese stock index Moment estimations Lè vy processes big data distributed algorithms stocks Computers

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An Event-Based Architecture for Cross-Breed Multi-population Bio-inspired Optimization Algorithms 1

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23rd European International conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar conference

作者： Minguela, Erick Garcia-Valdez, J. Mario Guervos, Juan Julian Merelo Inst Tecnol Tijuana Tijuana BC Mexico Univ Granada Granada Spain

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

Multi-population methods can combine multiple algorithms, with different parameters, interacting with each other at the same time. For instance, a genetic algorithm could find a promising global solution that is not optimal while another algorithm, more suitable for a local search, finds the global optimum. This approach has been followed extensively in recent years, with success. Moreover, there is a need for frameworks, architectures, and implementation models that can allow researchers the development of new parallel, asynchronous, heterogeneous, and parameter-free algorithms in a scalable way. In this work, we present an event-driven architecture, designed to distribute the processing of population-based algorithms asynchronously. The search algorithm uses a multi-population approach, creating multiple populations with different parameters of execution, allowing the implementation of multiple algorithms. In this work, we cross-breed Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). Experiments show that the framework allows the combined algorithms outperform, with a high probability, single-algorithm versions. The framework we provide also has few parameters to tune since single-algorithm parameters are selected randomly;in general, this will boost the diversity of every algorithm by itself.

关键词： Multi-population Asynchronous Sub-population Serverless distributed Cross-breed multi-population

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The Research of parallel Wavelet Transform Algorithms on Remote Sensing image

The Research of Parallel Wavelet Transform Algorithms on Rem...

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International conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Shengzhong Zhang Information Network Center China University of Geosciences (Beijing) Beijing China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199290

Focusing on the Wavelet Transform, the paper explores four parallel Wavelet Transform algorithms and techniques from the perspectives of data parallel and algorithm parallel for remote sensing images. Among them, the algorithm based on "Working Pool parallel" achieves dynamic load balance without any limits to the scale of the data and the number of the Slaves. Therefore, this algorithm is easier to achieve the goal of processing the vast data of remote sensing images rapidly in the distributed network systems.

关键词： Wavelet transforms Filtering Task analysis Heuristic algorithms Recycling Wavelet analysis parallel algorithms

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Proceedings - 2019 20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

Proceedings - 2019 20th International Conference on Parallel...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

ISBN: (纸本)9781728126166

The proceedings contain 94 papers. The topics discussed include: optimizing deep learning split deployment for IoT edge networks;efficient fault-tolerant syndrome measurement of quantum error-correcting codes based on 'flag';comparison of binary rain prediction on HIMAWARI using MPI and CUDA;effect of parallel processing by duplicating histogram in automatic image binarization for high-level synthesis;joint mobile data collection and energy supply scheme for rechargeable wireless sensor networks;ultra reliable communication: availability analysis in 5G cellular networks;an improved online multidimensional bin packing algorithm;and improving recommender systems accuracy in social networks using popularity.

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The FitOptiVis ECSEL Project: Highly Efficient distributed Embedded image/Video processing in Cyber-Physical Systems 19

The FitOptiVis ECSEL Project: Highly Efficient Distributed E...

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16th ACM International conference on Computing Frontiers (CF)

作者： Al-Ars, Zaid Basten, Twan de Beer, Ad Geilen, Marc Goswami, Dip Jaaskelainen, Pekka Kadlec, Jiri de Alejandro, Marcos Martinez Palumbo, Francesca Peeren, Geran Pomante, Luigi van der Linden, Frank Saarinen, Juka Santti, Tero Sau, Carlo Zedda, Maria Katiuscia Delft Univ Technol Delft Netherlands Eindhoven Univ Technol Eindhoven Netherlands Philips Amsterdam Netherlands Tampere Univ Tampere Finland Inst Informat Theory & Automat Prague Czech Republic Thales Alenia Space Cannes France Univ Sassari Sassari Italy Univ Aquila Laquila Italy Nokia Espoo Finland Univ Turku Turku Finland Abinsula Sassari Italy Univ Cagliari Cagliari Italy

ISBN: (纸本)9781450366854

Cyber-Physical Systems (CPS) are systems that are in feedback with their environment, possibly with humans in the loop. They are often distributed with sensors and actuators, smart, adaptive and predictive and react in real-time. image-and video-processing pipelines are a prime source for environmental information improving the possibilities of active, relevant feedback. In such a context, FitOptiVis aims to provide end-to-end multi-objective optimization for imaging and video pipelines of CPS, with emphasis on energy and performance, leveraging on a reference architecture, supported by low-power, high-performance, smart devices, and by methods and tools for combined design-time and run-time multi-objective optimization within system and environment constraints.

关键词： image-video processing distributed systems heterogeneous system energy and performance optimization

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AdaM: An Adaptive Fine-Grained Scheme for distributed Metadata Management 19

AdaM: An Adaptive Fine-Grained Scheme for Distributed Metada...

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48th International conference on parallel processing (ICPP)

作者： Cao, Shiyi Gao, Yuanning Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781450362955

distributed metadata management, administrating the distribution of metadata nodes on different metadata servers (MDS's), can substantially improve overall performance of large-scale distributed storage systems if well designed. A major difficulty confronting many metadata management schemes is the trade-off between two conflicting aspects: system load balance and metadata locality preservation. It becomes even more challenging as file access pattern inevitably varies with time. However, existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make timely and efficient update on distribution of nodes. In this paper, we propose an adaptive fine-grained metadata management scheme, AdaM, leveraging Deep Reinforcement Learning, to address the trade-off dilemma against time-varying access pattern. At each time step, AdaM collects environmental "states" including access pattern, the structure of namespace tree and current distribution of nodes on MDS's. Then an actor-critic network is trained to reallocate hot metadata nodes to different servers according to the observed "states". Adaptive to varying access pattern, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. We test AdaM on real-world data traces. Experimental results demonstrate the superiority of our proposed method over other schemes.

关键词： metadata management distributed file system deep reinforcement learning

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A GPU enhanced LIDAR Perception System for Autonomous Vehicles

A GPU enhanced LIDAR Perception System for Autonomous Vehicl...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Abderrahim Haneche Yazid Lachachi Smail Niar Hamza Ouarnoughi Ecole Nationale Supérieure d’Informatique Algiers Algeria Université des sciences et de la technologie d’Oran Oran Algeria LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Environment vision and understanding is a crucial task in Autonomous Driving (AD) context. This mainly needs image processing approaches such as Convolutional Neural Networks (CNN). Nevertheless, cameras have shown their limits for such a task, especially in dealing with difficult light conditions. LIDAR is a powerful and widely used sensor for AD. Indeed, LIDAR can then cope with the lack of information gathered from cameras. For AD, data processing from the sensors is the key function to obtain a high quality perception. For this, Graphics processing Unit (GPU) platforms show great performances and outperform other processing platforms such as FPGA and Multi-cores. This work presents a new approach to produce multiple 2D representation from 3D points cloud coming from LIDAR. The 2D representation can therefore be used by any efficient image processing applications. Our approach uses only LIDAR sensor and exploits the high GPU parallelism for its implementation. The resulting 2D representations are then used by CNN for AD applications such as image classification and segmentation. Finally, our contributions have been evaluated using the KITTI road benchmark and showed encouraging results.

关键词： Laser radar Graphics processing units Two dimensional displays Three-dimensional displays Task analysis Cameras Rendering (computer graphics)

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A Dynamic Feature Weighting Method for Mangrove Pests image Classification with Heavy-tailed Distributions 2020

A Dynamic Feature Weighting Method for Mangrove Pests Image ...

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Proceedings of the 2020 4th International conference on Vision, image and Signal processing

作者： Zixu Wang Weiguo Gong Weihong Li Key Lab of Optoelectronic Technology & Systems of Education Ministry Chongqing University Chongqing China

ISBN: (纸本)9781450389532

Most of the data in agriculture and forest obey heavy-tailed distribution, in which the data distribution is extremely imbalanced and the amount of most classes is poor. In this paper, we propose a feature centroid based dynamic feature weighting method between head and tail classes for mangrove pest images classification. In the proposed method, firstly, we design a convolutional neural network to extract the clustering centroid as a representation of the subclass. Secondly, we use the centroid feature memory to achieve the dynamic feature weighting through extraction, injection and fusion, which enrich the feature space of the tail classes while retaining identity. Thirdly, to localize the pests in natural background, we optimize the weakly-supervised localization algorithm with a multiscale local maximum index strategy to extract saliency maps. Furthermore, to carry out our research we establish a heavy-tailed distributed image dataset named MIPDGC based on mangrove forest pest. The experimental results prove that the dynamic feature weighting method can obtain higher classification accuracy and better performance than other general methods on the classification accuracy of the MIPDGC.

关键词： Feature Weighting Classification Pests Mangrove Localization Heavy-tailed

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Convolutional neural network-based age estimation using B-mode ultrasound tongue image

arXiv

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arXiv 2021年

作者： Xu, Kele Csapó, Tamás Gábor Feng, Ming National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary Tongji University Shanghai China

Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface. Unlike previous studies, here we explore the feasibility of age estimation using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, this paper leverages deep learning on this task. We train a deep convolutional neural network model on the UltraSuite dataset. The deep model achieves mean absolute error (MAE) of 2.03 for the data from typically developing children, while MAE is 4.87 for the data from the children with speech sound disorders, which suggest that age estimation using ultrasound is more challenging for the children with speech sound disorder. The developed method can be used a tool to evaluate the performance of speech therapy sessions. It is also worthwhile to notice that, although we leverage the ultrasound tongue imaging for our study, the proposed methods may also be extended to other imaging modalities (e.g. MRI) to assist the studies on speech production. © 2021, CC BY-NC-SA.

关键词： Ultrasonics

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image DEMOSAICKING VIA CHROMINANCE imageS WITH parallel CONVOLUTIONAL NEURAL NETWORKS 44

IMAGE DEMOSAICKING VIA CHROMINANCE IMAGES WITH PARALLEL CONV...

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Yamaguchi, Takuro Ikehara, Masaaki Keio Univ EEE Dept Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781479981311

Many conventional demosaicking methods are based on hand-crafted filters. However, the filters yield false colors in salient regions like edges and textures. For acquisition of high quality images, we focus on neural networks. Neural networks lead to high accuracy in many fields. However, there are few methods in demosaicking field. For adaptation to demosaicking, we consider not only network's architecture but also the input. In this research, we utilize a Bayer image as input of our networks. However, different filter is needed in estimation at different color pixels, for example, missing red value at green pixel and that at blue pixel. Therefore, we prepare four networks with downsampling operators classified by color patterns in Bayer images. This downsampling operator not only identifies the color pattern but also reduces the calculation cost in each network due to reduction of the size of feature maps. Besides, preparation of multi-networks instead of a deep single-network is suitable for today's parallel computing. Moreover, we utilize not missing color images but chrominance images as output. Compared to results with missing color images as output, the results with chrominance images obtains higher accuracy. Experimental results show our CNN-based approach produces high quality restored images.

关键词： Demosaicking Convolutional Neural Network Multi-network parallel Computing

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