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A lightweight edge computing platform designed on embeded system

A lightweight edge computing platform designed on embeded sy...

引用

2019 IEEE international conference on Signal, Information and Data Processing, ICSIDP 2019

作者： Lin, Qin Pan, Ling Jia, Mingquan Liu, Hongwei Wu, Mingqin Zhang, Hao Zhong, Yu Key Laboratory of Agile Intelligent Computing 10th Research Institute of China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9781728123455

Edge computing platform is a critical component to form next-generation intelligent equipment. In an effort to support new military and industrial applications with more flexible computing power, a new lightweight edge computing platform is introduced in this paper. This platform is fully based on embedded hardware, such as ARM, DSP and FPGA. Platform's management and scheduling architecture is developed using Go language to minimize code size and dependence on three-party libraries. In addition, the management and scheduling commands transmit across the platform over the Ethernet, while data transmit among computing nodes via the RapidIO network to isolate the commands and data channels. An experimental letter count application was performed to validate the feasibility of the platform and also supply optimization hint with a data throughput test. This work has the potential to reduce developer's experience in parallel and distributed systems, and then the cost and time in development of embedded intelligent equipment. © 2019 IEEE.

关键词： Edge computing

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SparsePipe: parallel Deep Learning for 3D Point Clouds

SparsePipe: Parallel Deep Learning for 3D Point Clouds

引用

international conference on High Performance computing

作者： Keke Zhai Pan He Tania Banerjee Anand Rangarajan Sanjay Ranka Department of Computer and Information Science and Engineering University of Florida University of Florida

We propose SparsePipe, an efficient and asynchronous parallelism approach for handling 3D point clouds with multi-GPU training. SparsePipe is built to support 3D sparse data such as point clouds. It achieves this by adopting generalized convolutions with sparse tensor representation to build expressive high-dimensional convolutional neural networks. Compared to dense solutions, the new models can efficiently process irregular point clouds without densely sliding over the entire space, significantly reducing the memory requirements and allowing higher resolutions of the underlying 3D volumes for better performance. SparsePipe exploits intra-batch parallelism that partitions input data into multiple processors and further improves the training throughput with inter-batch pipelining to overlap communication and computing. Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead. Using experimental results on an eight-GPU platform, we show that SparsePipe can parallelize effectively and obtain better performance on current point cloud benchmarks for both training and inference, compared to its dense solutions.

关键词： Training Three-dimensional displays Tensors Program processors Computational modeling Memory management distributed databases

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Hyaline: A Transparent Distribwuted computing Framework for CUDA 5

Hyaline: A Transparent Distribwuted Computing Framework for ...

引用

5th IEEE international conference for Convergence in Technolog, I2CT 2019

作者： Jain, Akshat Kazi, Owais Joshi, Raviraj Basantwani, Shraddha Bang, Yogita Khengare, Rahul PICT Computer Engineering Pune India IIT Computer Science Madras India

ISBN: (纸本)9781538680759

The GPU usually handles the homogenous data parallel work, by taking advantage of its massive number of cores. In most of the applications, we use CUDA programming for utilizing the power of GPU. In data intensive high computational applications like neural networks, utilizing the GPU on a single machine is time consuming. Instead if multiple GPUs are used in a network the amount of time required will be significantly reduced. Traditionally to enable a set of program to be run in a distributed environment, programmer has to accordingly design components to make his system dynamic and resilient to changes in number of systems in cluster, which is a daunting task. This work distribution can be a poor solution as it may underutilize the GPUsIn our approach, we developed a framework which transparently distributes data parallel kernels across multiple GPUs in a distributed network. The programmer is responsible for developing a single data parallel kernel in CUDA while the framework automatically distributes the workload across an arbitrary set of CUDA enabled GPUs. Depending on current workload on GPUs and the amount of data to be processed optimal distribution is done. The goal is to maximally utilize the available resources with minimal programming complexity. The systems not compatible with CUDA can also utilize our solution. We expect our framework to reduce the processing time along with simplifying the task of programmers. © 2019 IEEE.

关键词： parallel programming

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A distributed support vector machine using apache spark for semi-supervised classification with data augmentation

A distributed support vector machine using apache spark for ...

引用

international conference on Soft computing and Signal Processing, ICSCSP 2018

作者： Blessy Trencia Lincy, S.S. Nagarajan, Suresh Kumar School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789811333927

One of the popular and extensively used classification algorithms in the data mining and the machine learning technique is the support vector machine (SVM). Yet, conversely they have been traditionally applied to a small dataset or to an extent medium dataset. The current requirement and demand to scale up with the evolving size of the datasets have fascinated the research notice and attention such that new techniques and implementations can be carried out for the SVM, and as a result can scale well with large datasets and tasks. Recently, the distributed SVM is studied by the researchers, but the data augmentation with semi-supervised classification using the distributed SVM is not yet implemented. In this paper, a distributed implementation of support vector machine along with the data augmentation upon the SparkR, which is a recent and effective platform for performing distributed computation, is introduced and analyzed. This framework—A distributed Support Vector Machine under Apache Spark for Semi-supervised Classification with Smart Data Augmentation—is implemented with a large-scale dataset with more than million data points. The results and analysis show that the proposed approach greatly enhances the predictive performance of the method in terms of execution time and faster processing. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Support vector machines

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Challenges and Mitigation Strategies in Reusing Requirements in Large-Scale distributed Agile Software Development: A Survey Result

Challenges and Mitigation Strategies in Reusing Requirements...

引用

computing conference, 2019

作者： Hossain, Syeda Sumbul Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783030228675

Requirements re-usability in a distributed software development project is applied to increase system productivity, reliability, quality, decreasing system development sprint and maintaining consistency between two identical systems, which later help to reduce both project time and cost. Nowadays, most of the projects are driven by market so that the intention of this research is to identify the challenges faced by practitioners in requirements re-usability in distributed large-scale agile projects and to find out how practitioners apply the concept of re-usability to mitigating those challenges in distributed large-scale agile software development from requirement engineering or re-usability perspective. In this study, a survey is used to identify requirement re-usability challenges and mitigation approach from practitioners. From a series of semi-structured interview, we have identified 14 challenges and 10 mitigation approaches into three categories, such as communication, coordination, and control from the global software engineering perspective. The findings from this research will help industry people to make a decision in their industry oriented activities. © 2019, Springer Nature Switzerland AG.

关键词： Computer software reusability

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Towards Designing Linguistic Assessments Aggregation as a distributed Neuroalgorithm

Towards Designing Linguistic Assessments Aggregation as a Di...

引用

international conference on Soft computing and Measurements (SCM)

作者： Alexander V. Demidovskij Eduard A. Babkin Higher School of Economics National Research University Nizhny Novgorod Russian Federation

ISBN: (数字)9781728196923

ISBN: (纸本)9781728196930

A challenging problem which arises in the domain of integrating symbolic and sub-symbolic computations within a massively parallel computational environments like Internet of Things is considered in application to the Linguistic Decision Making tasks. A novel theoretical idea is proposed on expressing linguistic operators in dynamics of an artificial neural network. The proposal consists of two consequent stages: expressing linguistic operators as structural manipulations and translating them in a neuroalgorithm. The theoretical foundation is Tensor Product Representation (TPR) that provides a generic framework of designing a neural network that does not require training and produces an exact result equivalent to the result of symbolic algorithms. This paper discusses viability of the proposed idea, demonstrates design of TPR-based arithmetic as a basic building block for construction of such a method and elaborates directions of further research.

关键词： Linguistics Tensile stress Decision making Task analysis Artificial neural networks Training

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IoT based Energy Management System for Smart Grid

IoT based Energy Management System for Smart Grid

引用

advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Munish Kumar Ahmad Faiz Minai Akhlaque A. Khan Satish Kumar Integral University Lucknow INDIA

ISBN: (数字)9781728197852

ISBN: (纸本)9781728197869

In the modern days the word “Smart Grid” becomes a common word which can also be used as a junction which monitor demand side energy as well as the incoming energy and make it balance when the demand is on the peak or in low position it contributes the saved electric energy with the help of Internet of Things (loT). The IoT make power grid (smart grid) more reliable and economical. By IoT there is no lack of information in demand and supply and also it helps to save the energy. It also make pre-pattern of electricity consumption as the load demand and send signal to generation side for the required energy for the specific time period. The smart grid with IoT integrates the high speed response and storage system also with energy consumption. This type of networking by using IoT makes the operation of Smart Grid more reliable and burden free from the high demand periods (because the stored energy is used for the required demand). It also makes a better environment to collect the power from distributed generation and fasten the use of operation in the smart grid environment using loT. In this paper the use of IoT is discussed to work with Smart Grid for the better, economical, loss free power and also it helps in energy saving by reduce wastage. IoT is also used for the transfer of real time information to the Smart Grid and when the increase or decrease of demand takes places it helps in the saving of energy. User and supplier communication has been established by IoT for the proposed system.

关键词： Smart grids Batteries Feeds Wind turbines Renewable energy sources distributed power generation

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15th international conference on distributed computing and Internet Technology, ICDCIT 2019

15th International Conference on Distributed Computing and I...

引用

15th international conference on distributed computing and Internet Technology, ICDCIT 2019

ISBN: (纸本)9783030053659

The proceedings contain 35 papers. The special focus in this conference is on distributed computing and Internet Technology. The topics include: Data scheduling and resource optimization for fog computing architecture in industrial IoT;Research on CNN parallel computing and learning architecture based on real-time streaming architecture;Detection of alcoholism: An EEG hybrid features and ensemble subspace K-NN based approach;on the growth of the prime numbers based encoded vector clock;A sensitivity analysis on weight sum method MCDM approach for product recommendation;event detection and aspects in twitter: A bow approach;a new automatic multi-document text summarization using topic modeling;improved visible light communication using code shift keying modulation;optimization of transmission range for a fault tolerant wireless sensor network;the digital turn: On the quest for holistic approaches;community detection using an enhanced louvain method in complex networks;a fast handoff technique for wireless mobile networks;adaptive partitioning using partial replication for sensor data;design and implementation of cognitive radio sensor network for emergency communication using discrete wavelet packet transform technique;a network formation model for collaboration networks;bees detection on images: Study of different color models for neural networks;mobile charging of wireless sensor networks for internet of things: A multi-attribute decision making approach;efficient searching over encrypted database: Methodology and algorithms;tag-reader authentication system guarded by negative identifier filtering and distance bounding;RSA-based collusion resistant quorum controlled proxy re-encryption scheme for distributed secure communication;secured communications on vehicular networks over cellular networks.

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Design of parallel coupled line band-pass filter using DGS for WiMAX applications

Design of parallel coupled line band-pass filter using DGS f...

引用

international conference on Soft computing and Signal Processing, ICSCSP 2018

作者： Srivastava, Karunesh Singh, Rajeev Department of Electronics & Communication University of Allahabad AllahabadUttar Pradesh India

ISBN: (纸本)9789811333927

Filter constitutes an integral part of microwave wireless communication systems. In this work, a parallel coupled line band-pass filter is proposed using defected ground structure (DGS) for WiMAX applications, which not only reduces the size but also the mutual coupling between the resonators is enhanced. The parallel coupled band-pass filter is implemented by introducing DGS in the ground plane. Effective inductance and capacitance of a transmission line are dependent on DGS as observed from results. The entire proposed filter is designed and fabricated on 1.6-mm-thick FR-4 substrate with tangent loss of 0.02 by using 50 Ω resistance. For validation of fabricated design, fabricated filter is measured using vector network analyzer and also compared with simulated results. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Bandpass filters

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Research on parallel computing Method of Hydrological Data Cube 1

引用

8th international Congress on Big Data (BigData) held as Part of the Services conference Federation (SCF)

作者： Wang, Rong Wan, Dingsheng Zhang, Zixuan Zhao, Qun Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Bank Jiangsu Nanjing 211100 Peoples R China

ISBN: (数字)9783030235512

ISBN: (纸本)9783030235512;9783030235505

Hydrological data is characterized by large data volume and high dimension. In view of non-hierarchical dimension data in hydrological data, this paper proposed Segmented parallel Dwarf Cube (SPD-Cube). Firstly, a high-dimensional cube is divided into several low-dimensional segments. Then by calculating the sub-dwarf of each low-dimensional segment, the storage space required for cube materialization is greatly reduced. Finally, combining Map/Reduce technology to construct, store and query several sub-dwarfs in parallel. Experiments show that SPD-Cube not only realizes highly compressed storage of data, but also realizes efficient query by combining Map/Reduce distributed parallel architecture.

关键词： Hydrological data Dwarf cube Map/Reduce

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