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Image defogging based on amended dark channel prior and 4-directional L₁ regularisation

IET IMAGE PROCESSING 2021年第11期15卷 2454-2477页

作者： Yang, Yuliang Long, Wei Li, Yanyan Shi, Xiaoqiu Gao, Lin Sichuan Univ Sch Mech Engn Yihuan Rd Chengdu 610065 Peoples R China Shaanxi Inst Technol 8 Renmin Rd Huyi Dist Xian Peoples R China Southwest Univ Sci & Technol Sch Mfg Sci & Engn Mianyang Sichuan Peoples R China Hubei Minzu Univ Sch Informat Engn Enshi Peoples R China

The dark channel prior (DCP) algorithm has been widely used in the field of image defogging because of its simple theory and clear restoration result. However, the DCP algorithm has significant limitations. This study clarifies the relationship between halo artfacts and the size of the dark channel patch of the DCP algorithm and analyses the reason why the colour of close-range white objects appears distorted in the restored images. An amended DCP method is then proposed to solve these problems, utilising a locally variable weighted 4-directional L-1 regularisation and a corresponding parallel algorithm to optimise the transmission. A deep neural network, 4DL(1)R-net, is then trained to further enhance the processing speed. Extensive experiments demonstrate that this method is effective. The proposed method can obtain clear details, maintain the natural clarity of images, and achieve significant improvements over state-of-the-art methods.

关键词： close-range white objects dark channel prior Computer vision and image processing techniques images restored image colour analysis image restoration DCP algorithm parallel algorithms 4-directional L1 regularisation deep neural network Optical, image and video signal processing image defogging image enhancement 4DL1R-net deep learning (artificial intelligence) parallel algorithm

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Sampling arborescences in parallel 12

Sampling arborescences in parallel

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12th Innovations in Theoretical Computer Science Conference, ITCS 2021

作者： Anari, Nima Hu, Nathan Saberi, Amin Schild, Aaron Stanford University CA United States University of Washington SeattleWA United States

ISBN: (纸本)9783959771771

We study the problem of sampling a uniformly random directed rooted spanning tree, also known as an arborescence, from a possibly weighted directed graph. Classically, this problem has long been known to be polynomial-time solvable;the exact number of arborescences can be computed by a determinant [33], and sampling can be reduced to counting [18, 16]. However, the classic reduction from sampling to counting seems to be inherently sequential. This raises the question of designing efficient parallel algorithms for sampling. We show that sampling arborescences can be done in RNC. For several well-studied combinatorial structures, counting can be reduced to the computation of a determinant, which is known to be in NC [9]. These include arborescences, planar graph perfect matchings, Eulerian tours in digraphs, and determinantal point processes. However, not much is known about efficient parallel sampling of these structures. Our work is a step towards resolving this mystery. © Nima Anari, Nathan Hu, Amin Saberi, and Aaron Schild.

关键词： parallel algorithms

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Interest points reduction using evolutionary algorithms and CBIR for face recognition

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VISUAL COMPUTER 2021年第7期37卷 1883-1897页

作者： Villegas-Cortez, Juan Benavides-Alvarez, Cesar Aviles-Cruz, Carlos Roman-Alonso, Graciela de Vega, Francisco Fernandez Chavez, Francisco Cordero-Sanchez, Salomon Univ Autonoma Metropolitana Dept Sistemas Av San Pablo Xalpa 180 Mexico City 02200 DF Mexico Univ Autonoma Metropolitana Dept Elect Av San Pablo Xalpa 180 Mexico City 02200 DF Mexico Univ Autonoma Metropolitana Dept Ingn Elect San Rafael Atlixco 186 Mexico City 09340 DF Mexico Univ Extremadura Dept Comp Sci C Santa Teresa Jornet 38 Merida 06800 Spain Univ Autonoma Metropolitana Dept Quim San Rafael Atlixco 186 Mexico City 09340 DF Mexico

Face recognition has become a fundamental biometric tool that ensures identification of people. Besides a high computational cost, it constitutes an open problem for identifying faces under ideal conditions as well as those under general conditions. Though the advent of high memory and inexpensive computer technologies has made the implementation of face recognition possible in several devices and authentication systems, achieving 100% face recognition in real time is still a challenging task. This paper implements an evolutionary computer genetic algorithm for optimizing the number of interest points on faces, intended to get a quick and precise facial recognition using local analysis texture technique applied to CBIR methodology. Our approach was evaluated using different databases, getting an efficient facial recognition of up to 100% considering only seven interest points from a total of 54 cited in the literature. The interest points reduction was possible through a parallel implementation of our approach using a 54-processor cluster that executes the similar task up to 300% more faster.

关键词： Multi-objective Face recognition parallel algorithms CBIR Genetic algorithm

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On parallel Calculation of All-Terminal Network Reliability 17

On Parallel Calculation of All-Terminal Network Reliability

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17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021

作者： Sergeev, Kirill Migov, Denis Novosibirsk State University Novosibirsk Russia Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia

ISBN: (纸本)9781665405621

The paper presents parallel algorithms for calculating the exact value of all-terminal reliability of a network with unreliable edges and absolutely reliable nodes. A random graph is used as a model of such network. The algorithms are based on the factorization procedure which is a well-known sequential method of a reliability calculation. parallelization of the algorithms consists in sending subgraphs, arising during the factorization of a network on the master process, to the rest of the processes that perform sequential calculation by the factorization. The basic idea of the parallel algorithms proposed is to distinguish those subgraphs, arising during the factorization in the work processes, that are relatively hard for reliability calculation. These subgraphs are sent back to the master process, which runs the computation of their reliability in a recursive way, i.e. according to the same scheme as with an initial graph. The results of the numerical experiments are given. © 2021 IEEE

关键词： parallel algorithms

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Graph Decomposition algorithms for Analyzing Social and Large Complex Networks

Graph Decomposition Algorithms for Analyzing Social and Larg...

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作者： Abdullah, Wali Mohammad University of Lethbridge (Canada)

学位级别：Ph.D., Doctor of Philosophy

Graphs are often used to model or represent large and sparse networks with billions of vertices and edges and store extensive amounts of structural and semantic information. Therefore, analyzing characteristics in networked data, such as graphs that can yield important information on the modelled structure, is challenging due to their linked nature and size. A common way to uncover this high-quality information is by analyzing subgraphs to get a deeper understanding of the data, which are helpful for classification, clustering, and knowledge discovery. This thesis proposes using a compact network data representation based on sparse matrix data structures. We will consider the enumeration of subgraphs (edge clique cover problem) with some ordering schemes. Finally, we benefit from the linear algebraic approach to graph algorithms for counting triangles, triangle enumeration, the k-count algorithm, and triangle centrality calculation. This thesis will present both serial and parallel algorithms for solving these problems.

关键词： Clique cover Graph decomposition algorithms Intersection matrix parallel algorithms Sparse graph Triangle count and enumeration

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ACCL: Architecting Highly Scalable Distributed Training Systems With Highly Efficient Collective Communication Library

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IEEE MICRO 2021年第5期41卷 85-92页

作者： Dong, Jianbo Wang, Shaochuang Feng, Fei Cao, Zheng Pan, Heng Tang, Lingbo Li, Pengcheng Li, Hao Ran, Qianyuan Guo, Yiqun Gao, Shanyuan Long, Xin Zhang, Jie Li, Yong Xia, Zhisheng Song, Liuyihan Zhang, Yingya Pan, Pan Wang, Guohui Jiang, Xiaowei Alibaba Grp Hangzhou 310052 Zhejiang Peoples R China

Distributed systems have been widely adopted for deep neural networks model training. However, the scalability of distributed training systems is largely bounded by the communication cost. We design a highly efficient collective communication library, namely Alibaba Collective Communication Library (ACCL), to build distributed training systems with linear scalability. ACCL provides optimized algorithms to fully make use of heterogeneous interconnects simultaneously. And the experimental results show significant performance improvement.

关键词： Servers Training Bandwidth Routing Fabrics Payloads parallel algorithms

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Shader-Like Computations in WebGL for Advanced Graphics and General Purposes

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COMPUTING IN SCIENCE & ENGINEERING 2021年第2期23卷 54-63页

作者： Watters, Aaron R. Flatiron Inst Comp Core New York NY 10010 USA

The feedWebGL2 software package enables parallel computations using a feature of HTML5/WebGL2 called Transform/Feedback. The Transform/Feedback mechanism can be used within Jupyter widgets or in other web components and applications for a special class of parallel computations called shader-like calculations, which can solve many problems well, but are unsuitable for some classes of parallel algorithms. This article describes the characteristics of shader-like calculations and how they can be created using the feedWebGL2 software package. The example programs use shader-like calculations for special purpose graphical stages in combination with standard WebGL graphics pipelines, and also use them for general purposes, such as matrix computations.

关键词： Internet parallel algorithms Rendering Computer Graphics Software Packages Transform Feedback Matrix Computations Standard Web GL Graphics Pipelines Special Purpose Graphical Stages parallel algorithms Shader Like Calculations Web Components Jupyter Widgets parallel Computations Feed Web GL 2 Software Package General Purposes Advanced Graphics Shader Like Computations Pipelines Isosurfaces Context Modeling Python Image Color Analysis Graphics Processing Units Software Packages

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An Efficient Algorithm for Finding Modules in Fault Trees

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IEEE TRANSACTIONS ON RELIABILITY 2021年第3期70卷 862-874页

作者： Chen, Mei Xiao, Ning-Cong Zuo, Ming J. Ding, Yi Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Alberta Dept Mech Engn Edmonton AB T6G 2G8 Canada Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310007 Peoples R China

A module of a fault tree is an independent subtree that has no input from the rest of the tree and no output to the rest, except the top events. Modularization is an important technique to reduce the computation cost for large, complex fault tree analysis. This article presents a new linear-time algorithm that is more efficient and easier to code for finding modules existing in fault trees. Two main stages are included in the proposed algorithm: branching and transforming. To demonstrate the efficiency and applicability of the proposed algorithm, comparisons are performed between the proposed algorithm and other linear-time algorithms for finding modules in fault trees. Results have shown the superiority and effectiveness of the proposed algorithm.

关键词： Fault trees Logic gates Software algorithms parallel algorithms Indexes Terminology Reliability Coherent fault trees linear-time algorithm lowest common ancestor modules

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Integrated Energy and Trust-Based Semi-Markov Prediction for Lifetime Maximization in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期118卷 505-522页

作者： Famila, S. Jawahar, A. Vimalraj, S. Leones Sherwin Lydia, J. SSN Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India Panimalar Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India Easwari Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India

Many of today's computing and communication models are distributed systems that are composed of autonomous computational entities that communicate with each other, usually by passing messages. Distributed systems encompass a variety of applications and wireless sensor networks (WSN) is an important application of it. The tiny, multiple functionality and low power sensor nodes are considered to be interconnected in the WSN for efficient process of aggregating and transmitting the data to the base station. The clustering-based schemes of sensor networks are capable of organizing the network through the utilization of a specifically designated node termed as the cluster head for the objective of energy conservation and data aggregation. Further, the cluster head is responsible for conveying potential information collected by the cluster member nodes and aggregate them before transmitting it to the base station. In this paper, a Reliable Cluster Head Selection Technique using Integrated Energy and Trust-based Semi-Markov Prediction (RCHST-IETSMP) is proposed with the view to extend the lifetime of sensor networks. This proposed RCHST-IETSMP incorporated two significant parameters associated with energy and trust for effective selection of cluster head facilitated through the merits of Semi-Markoc prediction integrated with the Hyper Erlang distribution process. The simulation results of the proposed RCHST-IETSMP scheme is proving to be efficient in upholding the residual energy of the network and the throughput to a maximum level of 23% and 19% predominant to the trust and energy-based clustering schemes considered for investigation.

关键词： Distributed algorithms Clustering Semi-Markov prediction Data aggregation parallel algorithms

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Improved MPC algorithms for Edit Distance and Ulam Distance

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2021年第11期32卷 2764-2776页

作者： Boroujeni, Mahdi Ghodsi, Mohammad Seddighin, Saeed Sharif Univ Technol Dept Comp Engn Tehran *** Iran Inst Res Fundamental Sci Sch Comp Sci Tehran *** Iran Toyota Technol Inst Chicago Chicago IL 60637 USA

Edit distance is one of the most fundamental problems in combinatorial optimization to measure the similarity between strings. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an almost optimal MPC (massively parallel computation) algorithm for Ulam distance and improve MPC algorithms for edit distance. Our algorithm for Ulam distance is almost optimal in the sense that (1) the approximation factor of our algorithm is 1 + epsilon, (2) the round complexity of our algorithm is constant, (3) the total memory of our algorithm is almost linear ((O) over tilde (epsilon)(n)), and (4) the overall running time of our algorithm is almost linear which is the best known for Ulam distance. We also improve the work of Hajiaghayi et al. for edit distance in terms of total memory. The best previously known MPC algorithm for edit distance requires (O) over tilde (n(2x))machines when the memory of each machine is bounded by (O) over tilde (n(1-x)). In this work, we improve the number of machines to (O) over tilde (n((9/5x)) while keeping the memory limit intact. Moreover, the round complexity of our algorithm is constant and the total running time of our algorithm is truly subquadratic. However, our improvement comes at the expense of a constant factor in the approximation guarantee of the algorithm. This improvement is inspired by the recent techniques of Boroujeni et al. and Chakraborty et al. for obtaining truly subquadratic time algorithms for edit distance.

关键词： MapReduce parallel algorithms approximation algorithms ulam distance edit distance

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