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International conference on Information Systems and Computer Networks (ISCON)

作者： Rashmi Sharma Shubham Yadav Suman Kumar Saha Electronics and Communication National Institute of Technology Raipur Raipur Chhattisgarh India

This paper employs the Honey Badger Algorithm (HBA) to find the optimized coefficients of FIR linear phase filters, i.e., Band Pass Filter (BPF), Band Stop Filter (BSF), Low Pass Filter (LPF), and high Pass Filter (HPF). With its quick convergence and reduced number of tuning parameters, HBA is a promising new metaheuristic method. HBA, which imitates the foraging behavior of the honey badger is likely to maintain the balance between the two phrases of exploration and exploitation. HBA computes the optimal coefficients of the specified filter using the digging and honey-searching modes. As a consequence, the provided work is able to accomplish the ideal frequency performance characteristics with the maximum stop band attenuation and the negligible pass band ripple. In other words, the HBA designed filter can perform better than filters using other methods because it has fewer ripples in passband and narrower transition width, which enhances its frequency response.

关键词： Band-pass filters Maximum likelihood detection Finite impulse response filters Low-pass filters nonlinear filters Filtering algorithms Attenuation

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Scale the Data Plane of software-Defined Networks: a Lazy Rule Placement Approach

Scale the Data Plane of Software-Defined Networks: a Lazy Ru...

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International conference on Distributed Computing Systems

作者： Qing Li Nanyang Huang Yong Jiang Richard Sinnott Mingwei Xu Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory PCL Research Center of Networks and Communications Shenzhen China Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China University of Melbourne Melbourne Australia Tsinghua University Beijing China

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Data plane programming languages enable administrators of software-Defined Networks (SDNs) to perform fine-grained flow control by compiling high-level policies into low-level rules and deploying rules in the data plane. However, it is difficult to scale the data plane with the dynamics of network traffic and the limited storage space of switches. In this paper, we propose a lazy OpenFlow Rule Placement (ORP) framework to enforce control polices and scale the SDN data plane by placing and reusing wildcard rules. We provide an offline rule placement scheme to meet performance objectives under real-world constraints. To handle dynamic traffic and perform incremental rule updates, we design an online matching rule deployment algorithm to place rules in polynomial time and prove it to be conditionally-optimal. Furthermore, to address the rule dependency problem during online rule placement, we extend the algorithm to deploy dependent rules and present lightweight heuristics to guarantee the fast reaction to the new flows. Extensive experiments are conducted on diverse network topologies and datasets to show that the lazy ORP framework significantly reduces the storage cost, improves data plane scalability and is flexible enough to accomplish different optimization goals.

关键词： Network topology Heuristic algorithms Scalability Telecommunication traffic Aerospace electronics Topology optimization

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Large Margin Pursuit for a Conic Section Classifier

Large Margin Pursuit for a Conic Section Classifier

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26th IEEE conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.2

作者： Santhosh Kodipaka Arunava Banerjee Baba C. Vemuri Department of Computer & Information Science && Engineering University of Florida Gainesville FL USA

Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. This is often the case in computer vision and medical diagnosis applications. A novel Conic Section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus, directrix and eccentricity. The discriminant boundary was the locus of all points that are equi-eccentric relative to each class-representative conic section. Simpler boundaries were preferred for the sake of generalizability. In this paper, we improve the performance of the two-class classifier via a large margin pursuit. When formulated as a non-linear optimization problem, the margin computation is demonstrated to be hard, especially due to the high dimensionality of the data. Instead, we present a geometric algorithm to compute the distance of a point to the nonlinear discriminant boundary generated by the CSC in the input space. We then introduce a large margin pursuit in the learning phase so as to enhance the generalization capacity of the classifier. We validate the algorithm on real datasets and show favorable classification rates in comparison to many existing state-of-the-art binary classifiers as well as the CSC without margin pursuit.

关键词： Virtual colonoscopy Cancer Computer vision Application software Kernel Supervised learning Information science Biomedical engineering Data engineering Medical diagnosis

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"CERE": A CachE Recommendation Engine: Efficient Evolutionary Cache Hierarchy Design Space Exploration

"CERE": A CachE Recommendation Engine: Efficient Evolutionar...

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IEEE International conference on high performance Computing and Communications (HPCC)

作者： Gabriel Yessin Abdel Hameed A. Badawy Vikram Narayana David Mayhew Tarek El Ghazawi Electrical & Computer Engineering Department The George Washington University Washington DC Electrical Engineering Department Arkansas Tech University Russellville AR George Washington University Law School Washington DC US Arizona State University Tempe AZ US

Design Space Exploration is a critical step in chip design. Unfortunately, it takes significant amounts of time and resources to explore a fraction of the design space. Herein, we present a heuristic, evolutionary approach (Genetic Algorithm) to exploration that significantly cuts down on the time and resources, obtaining a near optimal design. We demonstrate the real-world utility of our tool-chain "CERE" by rapidly and efficiently designing the cache hierarchy which maximizes the performance of a web-browser navigating to a set of famous websites running on a single ARM core. We rapidly traverse 134,136 possible configurations. At about two days per simulation on the Gem5 full system simulator, the entire space would have taken 268,272 CPU-Days or ≃734 CPU-Years (≃3.7 Years on a cluster of a hundred dual core machines) to brute force search. "CERE" provided results in ≃4.5 days and used ≃17.5 CPU-Days on our cluster. We ran the configurations that "CERE" chose through Gem5 to verify that "CERE" made the right choices and we were able to observe a 17.1% speedup going from the "best" hierarchy relative to the "Worst" hierarchy.

关键词： Space exploration Genetic algorithms Sociology Statistics Computer architecture Benchmark testing optimization

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Task Partitioning Oriented Architecture Exploration Method for Dynamic Reconfigurable Architectures

Task Partitioning Oriented Architecture Exploration Method f...

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IFIP International conference on Very Large Scale Integration (VLSI-SoC)

作者： Ittetsu Taniguchi Kyoko Ueda Keishi Sakanushi Yoshinori Takeuchi Masaharu Imai Graduate School of Information Science and Technology Osaka University Osaka Japan

Many dynamic reconfigurable processors are evolving rapidly that offer fast and run-time reconfiguration. To efficiently utilize run-time reconfiguration, we must consider additional memory access cycles due to reconfiguration. In general, dynamic reconfigurable processors read input data from external memory after reconfiguration and write output data to external memory before the next reconfiguration. To realize high performance, we must schedule tasks considering the total execution cycles that include reconfiguration time and memory access overhead. In this paper, we propose a parameterized reconfigurable processor model (PRP-model) and a task partitioning optimization algorithm for the architecture exploration of multicontext dynamic reconfigurable processors. The proposed task partitioning algorithm corresponds to various dynamic reconfigurable architectures by changing parameters and can realize the evaluation of a lot of reconfigurable architectures. Experimental results showed that the proposed algorithm can quickly find near optimal solutions. Using the proposed algorithm, designers can easily evaluate the performance of various dynamic reconfigurable architectures for specific applications

关键词： Reconfigurable architectures Partitioning algorithms Memory architecture Application software Runtime Read-write memory Information systems Hardware Circuits Field programmable gate arrays

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Trading off Charging and Sensing for Stochastic Events Monitoring in WRSNs

Trading off Charging and Sensing for Stochastic Events Monit...

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International conference on Network Protocols

作者： Yu Sun Chi Lin Haipeng Dai Qiang Lin Lei Wang Guowei Wu School of Software Technology Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Dalian University of Science and Technology Dalian China Acoustics Science and Technology Laboratory Harbin Engineering University Harbin China

ISBN: (数字)9781728169927

ISBN: (纸本)9781728169934

As an epoch-making technology, wireless power transfer incredibly achieves energy transmission wirelessly, enabling reliable energy supplement for wireless rechargeable sensor networks (WRSNs). Existing methods mainly concentrate on performance improvement theoretically, neglecting the fact that most Commercial Off-The-Shelf (COTS) rechargeable sensors (e.g., WISP and Powercast) are not allowed to conduct sensing and energy harvesting tasks simultaneously, termed charging exclusivity. Therefore, their schemes are not feasible for practical applications. In this paper, we focus on the charging exclusivity issue in stochastic events monitoring while improving network performance. In specific, we pay close attention to trading off charging and sensing tasks and formulate a combinatorial optimization problem with routing constraints. We introduce novel discretization techniques and investigate the routing problem to reformulate the original problem into the maximization of a submodular function. With a slightly relaxed budget, the output of our proposed algorithm is better than (1 1/e)/2 of the optimal solution to the original problem with a -smaller charging radius (1 - ξ)D c . Through extensive simulations, numerical results show that in terms of charging utility, our algorithm outperforms baseline algorithms by 21.3% on average. Moreover, we conduct test-bed experiments to demonstrate the feasibility of our scheme in real scenarios.

关键词： Sensors Monitoring Wireless sensor networks Task analysis Stochastic processes Wireless communication software

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Energy-aware decoders: A case study based on an RVC-CAL specification

Energy-aware decoders: A case study based on an RVC-CAL spec...

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conference on Design and Architectures for Signal and Image Processing

作者： R. Ren E. Juárez C. Sanz M. Raulet F. Pescador Centro de Investigación en Tecnologias del Software y Sistemas Multimedia para la Sostenibilidad (CITSEM) Universidad Politecnica de Madrid Madrid Spain IETR/INSA UMR CNRS 6164 Rennes France

Along with the pervasive development of video coding standards, new algorithms have suffered increasing complexities. An MPEG Reconfigurable Video Coding (RVC) framework has been proposed to efficiently support multiple codec specifications and facilitate innovation in codec design. Meanwhile, an energy-aware design is expected to be included into video coding standards for an energy optimization of multimedia applications, which are known to have highly computational load. This paper proposes an energy-aware decoder manager jointly based on a Just-In-Time Adaptive Decoder Engine (Jade) and an energy estimation model. The model is driven by performance Monitoring Counters (PMCs) and fitted by the multivariate adaptive regression splines (MARS) method and demonstrates its estimation accuracy is less than 10% when applies on the high Efficiency Video Coding (HEVC) decoder. In addition, this manager introduces 4.58% decoder performance decrease in the worst case. By combining the estimation model into the RVC framework, the experimental result shows a good potential of energy efficiency improvement with increase in 5.16% based on energy consideration.

关键词： Decoding Standards Estimation Transform coding Codecs Video coding Monitoring

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Applying Ant Lion optimization Technique to Enhance Power Converters performance via Effective Controller Tuning

Applying Ant Lion Optimization Technique to Enhance Power Co...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International conference on

作者： Muhammad Ahmad Khan Muhammad Zain Yousaf Saqib Khalid Daaraab Fashihi Syed Arslan Haider Bokhari Bacha Khan Insafmal Ghulam Abbas School of Electrical and Information Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan China Department of Electrical Engineering The University of Lahore Lahore Pakistan PV Design and Presales Manager Reon Energy Limited Lahore Pakistan Technical Services and Material Inspection Lahore Electric Supply Company Lahore Pakistan

The electrical power industries are constantly seeking innovative ways to improve the reliability and efficiency of the power network while simultaneously grappling with increasing energy demands and technical challenges. Recent advances in the field of metaheuristic algorithms have given researchers powerful tools to address different power system problems. Among these breakthroughs, the Voltage Source Converter (VSC) stands out, capitalizing on advancements in power electronics and semiconductor technology. VSCs offer enormous potential for ushering in smart grids, integrating renewable energy, and facilitating high-Voltage Direct Current (HVDC) transmission systems. Conventionally, the manual tuning of Proportional-Integral Derivative (PID) for VSCs has relied on a trial-and-error approach or the expertise of design engineers. This manual approach often fails to achieve optimal performance. The complexity is further amplified when dealing with multiple grids, such as VSC-based Multi-Terminal DC (MTDC) grids. This research article focuses on the application of the Ant Lion optimization (ALO) technique to fine-tune VSC control parameters. The improved results in dynamic response when these controllers encounter faults and unequal loads. To assess the effectiveness of this novel approach, a four-terminal Multi-Terminal high-Voltage Direct Current (MTDC) system is constructed within the PSCAD/EMTDC software environment. In a direct comparison with the classical tuning method, the proposed technique is subjected to various fault and unequal load scenarios. Through this comprehensive evaluation, the article aims to highlight the superior performance of the ALO-based approach in optimizing VSC controller parameters, resulting in an enhanced system response when faced with challenging operational conditions.

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A modified PSO algorithm for task scheduling optimization in cloud computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第24期30卷 e4970.1-e4970.11页

作者： Zhou, Zhou Chang, Jian Hu, Zhigang Yu, Junyang Li, Fangmin Changsha Univ Sch Comp Engn & Appl Math Changsha 410081 Hunan Peoples R China Hunan Univ Dept Comp Sci Changsha 410082 Hunan Peoples R China Cent S Univ Sch Software Changsha 410083 Peoples R China Henan Univ Sch Software Kaifeng 475001 Peoples R China CERNET Ltd Co Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M-PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M-PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M-PSO can efficiently reduce total cost compared with other algorithms.

关键词： cloud computing inertia weight particle swarm optimization task scheduling

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A Distributed Algorithm to Enumerate All Maximal Cliques in MapReduce

A Distributed Algorithm to Enumerate All Maximal Cliques in ...

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Bin Wu Shengqi Yang Haizhou Zhao Bai Wang Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

Structure mining plays an important part in the researches in biology, physics, Internet or telecommunications in recently emerging network science. As a main task in this area, the problem of maximal clique enumeration has attracted much interest and been studied in variant avenues in prior works. However, most of these works mainly rely on single chip computational capacity and have been constrained by local optimization. Thus it is an impossible mission for these methods to process terabytes datasets. In this paper, to extract maximal cliques from graphs, we propose a general enumeration process in a distributed manner on cluster system with the help of MapReduce. Graph is firstly split into small subgraphs automatically. Then a novel key-based clique enumeration algorithm is proposed based on subgraphs. We demonstrate that our algorithm has a high parallelism and a prominent performance on extremely huge graphs. Our method is implemented to fully utilize MapReduce execution mechanism and the experiments are soundly discussed as using such a powerful distributed platform. However we not only show the scalability and efficiency of the algorithm but also share some critical experience in using MapReduce computing model.

关键词： Distributed algorithms Clustering algorithms Parallel processing Biological system modeling Power system modeling Delay effects Concurrent computing Scheduling Computer science Laboratories

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