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A Post-processing Approach in Moving Objects Detection via Feature Pyramid Networks 9

A Post-Processing Approach in Moving Objects Detection via F...

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

作者： Lin, Li Wang, Bin Gu, Yinjuan Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781538694039

Recent work has shown that Convolutional Neural Networks (CNNs) have great ability to deal with classification problems in pattern recognition field. Moving objects detection, regarding as a classification process, labels every pixel as a foreground pixel or a background pixel. In this paper, we proposed an effective post-processing approach, Residual Background Networks (ResBGNets), to improve the accuracy of moving objects detection in video sequences. Instead of learning the ground truth directly, our model learns the residual pictures between the results of existing methods and the ground truth. It benefits to understand the hidden character of each algorithm and correct the misclassification. Inside ResBGNets, we build Feature Pyramid Networks (FPN) to combine spatial information of the low-resolution level with semantical features of high-level of the high-resolution level. Evaluation performed on the 2014 CDnet dataset reveals that through our approach, most of the existing background subtraction methods can get better detection results and a significant higher FM score.

关键词： Moving objects detection convolutional neural networks background subtraction residual pictures feature pyramids

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IOP conference Series: Materials Science and Engineering

IOP Conference Series: Materials Science and Engineering

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2018 5th international conference on Advanced Engineering and Technology, ICAET 2018

the proceedings contain 35 papers. the topics discussed include: a technical solution to improve the existing river dikes for both flood prevention and transportation purposes;effects of geometric changes at the fan side of a TEFC on the flow and heat transfer: a numerical study;potential of traditional sago starch: life cycle assessment (LCA) perspective;shear block test performance of Melunak and Mengkulang parallel to glueline;ranking of seaweed agro-industry in indonesia using fuzzy OWA method;roadway embankment stabilization on permafrost using thermosyphons with Y-shaped evaporators;performances investigation of filter membrane coated;design of L1 adaptive control for PMSG wind turbine system;design of the system for automatic grain size analysis at ore mining and processing enterprises;and LED projector of pulse backlight for machine vision system.

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Pattern-based Automatic parallelization of Representative-based Clustering algorithms 5

Pattern-based Automatic Parallelization of Representative-ba...

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5th IEEE international conference on Data Science and Advanced Analytics (IEEE DSAA)

作者： Islam, Saiyedul Balasubramaniam, Sundar Gupta, Shruti Brajesh, Shikhar Badlani, Rohan Labhishetty, Nitin Baid, Abhinav Goyal, Poonam Goyal, Navneet Birla Inst Technol & Sci Pilani Dept Comp Sci & Informat Syst Adv Data Analyt & Parallel Technol Lab Pilani Campus Pilani Rajasthan India BITS Pilani Pilani Rajasthan India

ISBN: (纸本)9781538650905

Ease of programming and optimal parallel performance have historically been on the opposite side of a tradeoff, forcing the user to choose. With the advent of the Big Data era and rapid evolution of sequential algorithms, the data analytics community can no longer afford the tradeoff. We observed that several clustering algorithms often share common traits-particularly, algorithms belonging to same class of clustering exhibit significant overlap in processing steps. Here, we present our observation on domain patterns in Representative-based clustering algorithms and how they manifest as clearly identifiable programming patterns when mapped to a Domain Specific Language (DSL). We have integrated the signatures of these patterns in the DSL compiler for parallelism identification and automatic parallel code generation. Our experiments on different state-of-the-art parallelization frameworks shows that our system is able to achieve near-optimal speedup while requiring a fraction of the programming effort, making it an ideal choice for the data analytics community.

关键词： Clustering Domain Specific Language parallelizing Compiler High Performance Computing Programming Patterns

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Fast and parallel Computation of the Discrete Periodic Radon Transform on GPUs, multi-core CPUs and FPGAs 25

Fast and Parallel Computation of the Discrete Periodic Radon...

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25th IEEE international conference on Image processing (ICIP)

作者： Carranza, Cesar Pattichis, Marios Llamocca, Daniel Pontificia Univ Catolica Peru Secc Elect & Elect Lima 32 Peru Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Oakland Univ Dept Elect & Comp Engn Rochester MI 48063 USA

ISBN: (纸本)9781479970612

the Discrete Periodic Radon Transform (DPRT) has many important applications in reconstructing images from their projections and has recently been used in fast and scalable architectures for computing 2D convolutions. Unfortunately, the direct computation of the DPRT involves O(N-3) additions and memory accesses that can be very costly in single-core architectures. the current paper presents new and efficient algorithms for computing the DPRT and its inverse on multi-core CPUs and GPUs. the results are compared against specialized hardware implementations (FPGAs/ASICs). the results provide significant evidence of the success of the new algorithms. On an 8-core CPU (Intel Xeon), with support for two threads per core, FastDirDPRT and FastDirInvDPRT achieve a speedup of approximately 10x (up to 12:83x) over the single-core CPU implementation. On a 2048-core GPU (GTX 980), FastRayDPRT and FastRayInvDPRT achieve speedups in the range of 526 (for 127 x 127) to 873 (for 1021 x 1021), which approximate ideal speedups of what can be achieved. the DPRT can be computed exactly and in real-time (30 frames per second) for 1471x1471 images using FastRayDPRT on the GPU. Furthermore, the GPU algorithms approximate the performance of an efficient FPGA implementation using 2N parallel cores at 100MHz.

关键词： Discrete Periodic Radon Transform parallel Architecture Multi-core CPU GPU FPGA

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15th international Symposium on Applied Reconfigurable Computing, ARC 2019

15th International Symposium on Applied Reconfigurable Compu...

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15th international Symposium on Applied Reconfigurable Computing, ARC 2019

ISBN: (纸本)9783030172268

the proceedings contain 28 papers. the special focus in this conference is on Applied Reconfigurable Computing. the topics include: Proof-Carrying Hardware Versus the Stealthy Malicious LUT Hardware Trojan;secure Local Configuration of Intellectual Property Without a Trusted third Party;HiFlipVX: An Open Source High-Level Synthesis FPGA Library for Image processing;Real-Time FPGA Implementation of Connected Component Labelling for a 4K Video Stream;A Scalable FPGA-Based Architecture for Depth Estimation in SLAM;Evaluating LULESH Kernels on OpenCL FPGA;the TaPaSCo Open-Source Toolflow for the Automated Composition of Task-Based parallel Reconfigurable Computing Systems;Graph-Based Code Restructuring Targeting HLS for FPGAs;UltraSynth: Integration of a CGRA into a Control Engineering Environment;exploiting Reconfigurable Vector processing for Energy-Efficient Computation in 3D-Stacked Memories;Optimizing CNN-Based Hyperspectral Image Classification on FPGAs;Automatic Toolflow for VCGRA Generation to Enable CGRA Evaluation for Arithmetic algorithms;reM: A Reconfigurable Multipotent Cell for New Distributed Reconfigurable architectures;Update or Invalidate: Influence of Coherence Protocols on Configurable HW Accelerators;hybrid Prototyping for Manycore Design and Validation;Evaluation of FPGA Partitioning Schemes for Time and Space Sharing of Heterogeneous Tasks;third Party CAD Tools for FPGA Design—A Survey of the Current Landscape;Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation;Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs;Faster Convolutional Neural Networks in Low Density FPGAs Using Block Pruning;Supporting Columnar In-memory Formats on FPGA: the Hardware Design of Fletcher for Apache Arrow;A Novel Encoder for TDCs.

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Research on Speaker Verification Based on Cloud Computing Platform

Research on Speaker Verification Based on Cloud Computing Pl...

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IEEE international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Mengiun Zeng Nanfeng Xiao Chao Ye Ming Xiao School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

At present, the accuracy of speaker verification is not high and the training speed is slow. In order to improve the accuracy of speaker verification, this paper uses transfer learning and score fusion technology. Experimental results show that with the knowledge learned from the VoxCelebl dataset, transfer learning reduced the equal error rate (ERR) by 0.67% on the aishell-l dataset. By fusing the convolutional neural network-based model CNN and the DenseNet-BiGRUbased model DR, the ERR is reduced above 0.63%. In the aspect of training speed improvement, this paper uses data parallel technology based on the cloud platform, and studies the impact of the communication window value of Downpour mechanism and starting multiple workers in each cloud node on training speed and accuracy. the experimental results show that starting multiple workers in each cloud node can effectively improve the training acceleration ratio. this method has a more significant effect on the model DR of lower communication overhead. At the same time, the Downpour mechanism can also improve the training acceleration ratio, and it has a better acceleration effect on the model CNN of higher communication overhead. In the case where the cloud platform has 3 nodes, the acceleration ratio of the DR model is 3.94, which exceeds the linear acceleration ratio. Meanwhile, the impact on the speaker verification accuracy is less than 1% compared with stand-alone training. the acceleration ratio of the CNN model is 2.69, which is close to the linear acceleration ratio.

关键词： Training Machine learning Computational modeling Cloud computing Acceleration parallel processing Servers

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Accelerating FM-index Search for Genomic Data processing 18

Accelerating FM-index Search for Genomic Data Processing

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47th international conference on parallel processing (ICPP)

作者： Wang, Yuanrong Li, Xueqi Zang, Dawei Tan, Guangming Sun, Ninghui Univ Chinese Acad Sci CAS ICT SKL Comp Architecture Beijing Peoples R China Chinese Acad Sci SKL Comp Architecture ICT Beijing Peoples R China

ISBN: (纸本)9781450365109

the deluge of genomics data is incurring prohibitively high computational costs. As an important building block for genomic data processing algorithms, FM-index search occupies most of execution time in sequence alignment. Due to massive random streaming memory references relative to only small amount of computations, FM-index search algorithm exhibits extremely low efficiency on conventional architectures. this paper proposes Niubility, an accelerator for FM-index search in genomic sequence alignment. Based on our algorithm-architecture co-design analysis, we found that conventional architectures exploit low memory-level parallelism so that the available memory bandwidth cannot be fully utilized. Niubility accelerator customizes bit-wise operations and exploit data-level parallelism, that produces maximal concurrent memory accesses to saturate memory bandwidth. We implement an accelerator ASIC in a ST 28nm process that achieves up to 990x speedup over the state-of-the-art software.

关键词： Energy efficiency Hardware-acceleration Genome alignment

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A Malleable Vectorized Auction Algorithm for Modern Multicore architectures 20

A Malleable Vectorized Auction Algorithm for Modern Multicor...

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20th IEEE international conference on High Performance Computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on Data Science and Systems (DSS)

作者： Sena, A. C. Marzulo, L. A. J. Nascimento, A. P. Vasconcelos, C. N. Univ Estado Rio De Janeiro Inst Matemat & Estat Rio De Janeiro Brazil Univ Fed Fluminense Inst Comp Niteroi RJ Brazil

ISBN: (纸本)9781538666142

the auction algorithm has been widely used to solve the bipartite graph matching problem and its parallel implementation is employed to find solutions in a reasonable computational time. Moreover, the new multicore architectures, besides its various cores, have a SIMD instruction set that can increase application performance when exactly the same operations are to be performed on multiple data objects. the aim of this paper is to efficiently execute the auction algorithm on these architectures. To achieve that, a vectorized version was implemented and evaluated. these versions were then run in parallel using the OpenMP library. Finally, to optimize the number of threads used during the execution, a malleable strategy is proposed and evaluated. Results show that the vectorized version outperforms the sequential one by a factor of 10, while the malleable vectorized version was able to adapt its execution to exploit the full potential of multicore architectures.

关键词： Multicore processing Heuristic algorithms Program processors Indexes Bipartite graph conferences

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Based GPU Approach to Accelerate Spectroscopy Raman Spectrum processing 5

Based GPU Approach to Accelerate Spectroscopy Raman Spectrum...

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5th international conference on Electrical and Electronics Engineering (ICEEE)

作者： Zouaneb, Imane Belarbi, Mostefa Chouarfia, Abdellah Ibn Khldoun Univ LIM Res Lab Tiaret Algeria Univ Sci & Technol Mohamed LIM Res Lab Boudiaf Oran Algeria

ISBN: (纸本)9781538663929

Raman spectrometry is a technique that allows detecting chemical products through a number of representative peaks found in an image spectrum or numeric series of data. the Raman spectrum machine generates a CSV file or an image as a curve which is the result of the diagnosis product. the analysis of the spectrum peaks permits to detect the chemical origin of the concerned product. Scientists do this operation manually, which makes it hard and long in terms of time. Graphics processing Units (GPUs) allow us to make the processing faster and more efficient, thanks to its multicore architecture. the aim of the present paper is to propose a new GPU based approach to automate the molecule detection operation using image-processing techniques with OpenCL parallel implementation. We propose two parallel solutions, which will be compared to each other. We apply the exploited approach to analyze ionic liquids and biomaterials samples.

关键词： raman spectroscopy raman image processing prallelism GPU OpenCL

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Permutation Algorithm Analysis and Updation

Permutation Algorithm Analysis and Updation

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international conference for Convergence of Technology (I2CT)

作者： Anil Kumar Gupta Amarjeet Sharma Sumedh Salvi Ashank Saini Nishad Tardalkar Ratanraj Sankeshwari HPC-I&E C-DAC Pune Department of Computer Engineering Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

this paper provides a new way to generate permutations of a given input in lesser time using the concept of parallel programming. More than forty algorithms have been proposed to date for generating permutations of N elements. Few of these algorithms have been implemented using C++ language and the time taken for processing the input has been compared for these algorithms. Also, the problems and drawbacks of these algorithms have been explained which are related to the code and execution time. this report is not only a survey of permutation methods but also provides a way to compare different algorithms for a given input using some time-related functions.

关键词： parallel programming Indexes Pattern analysis Program processors C++ languages Encryption

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