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IEEE International Conference on Communications

作者： Zhiyuan Yin Fan Li Meng Shen Yu Wang Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology Department of Computer Science University of North Carolina at Charlotte

ISBN: (纸本)9781467364300

Recent advances in ambient energy-harvesting wireless sensor networks (WSNs) technologies have made it possible to power the network by energy generated from the environment and thereby increase its lifetime. Various energy sources including light, vibration and heat can be harvested by sensor nodes. However, time-varying energy harvesting also bring new design challenging for WSNs. In this paper, we study a fault-tolerant topology design problem for an energy-harvesting heterogeneous WSN, where multiple supernodes with rich resources are used to improve the performance. We first model the network as a directed and weighted space-time graph in which both spacial and temporal information are preserved. We then define the fault-tolerant topology problem which aims to build a sparser time-varying structure from the original space-time graph while maintaining k-connectivity for the fault-tolerant purpose. Six different algorithms are proposed to solve the problem. Simulation results demonstrate that our proposed methods can save up to around 80% costs.

关键词： Topology Fault tolerance Fault tolerant systems Wireless sensor networks Network topology Ad hoc networks Algorithm design and analysis

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Consistent data sharing in mobile ad hoc networks

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International Journal of Wireless and Mobile Computing 2015年第1期9卷 36-42页

作者： Chandrakala, C.B. Prema, K.V. Hareesha, K.S. Department of Information and Communication Manipal Institute of Technology Manipal University Karnataka India Department of Computer Science and Engineering Manipal Institute of Technology Manipal University Karnataka India Department of Computer and Applications Manipal Institute of Technology Manipal University Karnataka India

In Mobile Adhoc Networks (MANETs), data availability is lower in comparison to the conventional fixed networks because of their mobility and resource-constrained characteristics. Replication and caching have been adapted to improve the performance in these constrained environments. These techniques significantly improve the efficiency of information access in MANETs resulting in reduction of the access latency and bandwidth usage. The paper proposes a cluster-based caching and data consistency algorithm where the clustering is done employing learning automata to support stability. The caching related information is maintained by the clusters members and the cluster heads. Apart from the avoidance of cached data redundancy and the reduction in the average access latency, the proposed scheme also enables TTL-based cache invalidation to avoid the stale data availability. The network is simulated using NS-2 to evaluate the performance of the proposed algorithm. The simulation results show an improvement in the cache hit ratio and reduction in average latency compared with few existing caching schemes. © 2015 Inderscience Enterprises Ltd.

关键词： Reduction

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Indexing large class handwritten character database

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2014年 235卷 227-233页

作者： Guru, D.S. Aradhya, V. N. Manjunath Department of Studies in Computer Science University of Mysore Mysore India Department of Master of Computer Applications Sri Jayachamarajendra College of Engineering Mysore India

ISBN: (纸本)9783319017778

This paper proposes a method of indexing handwritten characters of a large number of classes by the use of Kd-tree. The Ridgelets and Gabor features are used for the purpose of representation. A multi dimensional feature vectors are further projected to a lower dimensional feature space using PCA. The reduced dimensional feature vectors are used to index the character database by Kd-tree. In a large class OCR system, the aim is to identify a character from a large class of characters. Interest behind this work is to have a quick reference to only those potential characters which can have a best match for given unknown character to be recognized without requiring scanning of the entire database. The proposed method can be used as a supplementary tool to speed up the task of identification. The proposed method is tested on handwritten Kannada character database consisting of 2000 images of 200 classes. Experimental results show that the approach yields a good Correct Index Power (CIP) and also depicts the effectiveness of the indexing approach. © Springer International Publishing Switzerland 2014.

关键词： Database systems

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Weighted Particle Swarm Optimization algorithm for Randomized unit testing

Weighted Particle Swarm Optimization algorithm for Randomize...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： K. Devika Rani Dhivya V.S Meenakshi Department of CA and SS Sri Krishna Arts and Science College Coimbatore India Department of Computer Applications Dr. Mahalingam College of Engineering & Technology Pollachi India

ISBN: (纸本)9781479960866

Randomized testing is an effective method for testing software units. Thoroughness of randomized unit testing is according to the settings of optimal parameters. Randomized testing uses randomization for some aspects of test input data. Designing Genetic algorithm (GA) is somewhat of a black art. The feature subset selection (FSS) tool is used with GA to assess and to reduce the size and the content of the test case. FSS can be used to find and remove unnecessary parts of the search control automatically. The existing system does not cover all test data in test cases for the reason that it can quickly generate many test cases and does not consider the target method. Thus GA for Randomized unit testing has not achieves high coverage and does not produce better optimal test data. In the proposed method, Particle Swarm Optimization (PSO) algorithm is used for randomized unit testing. PSO algorithm is used to evaluate the target method solutions for test coverage in test data. The main goal is to generate the optimal test parameter, to reduce the size of test case generation and to achieve high coverage of the units under test. PSO achieves high coverage and produce optimal value. PSO algorithm is enhanced weighted value. Weighted Particle Swarm Optimization (WPSO) algorithm uses weight value in calculating the mean best position for each particle. It improves the efficiency of the system and achieves high coverage of the units under test within 5% of the time with better accuracy.

关键词： Genetic algorithms Software Software algorithms Heuristic algorithms Accuracy Optimization Generators

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Off-line signature verification based on principal component analysis and multi-layer perceptrons

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2014年 235卷 101-109页

作者： Shekar, B.H. Bharathi, R.K. Department of Computer Science Mangalore University Mangalore India Department of Master of Computer Applications S.J. College of Engineering MysoreKarnataka India

ISBN: (纸本)9783319017778

The off-line signature verification approach based on Principal Component Analysis (PCA) and Muti-Layer Perceptrons (MLP) is proposed in this work. The proposed approach involves three major phases. In the first phase, the signature image is subjected to preprocessing, such as binarization, noise elimination, skew correction followed by normalising the image by a series of thinning and dilating with a structuring element of size 3X3. The principal component analysis is employed on the preprocessed signature samples and the features extracted are termed as eigen-sign feature vectors, in second phase. The multilayer perceptrons, a neural network based approach is trained with eigen-sign feature vector and subsequently used for verification of signature samples. Extensive experimentation has been conducted on the publicly available signature datasets namely, CEDAR, GPDS-100 and MUKOS, a regional language dataset. The state-of-art off-line signature verification methods are considered for comparative study and objective analysis through experimental results is provided to justify the accuracy of the proposed approach. © Springer International Publishing Switzerland 2014.

关键词： Principal component analysis

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Reducing Degeneracy in Maximum Entropy Models of Networks

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Physical Review Letters 2015年第15期114卷 158701-158701页

作者： Szabolcs Horvát Éva Czabarka Zoltán Toroczkai Department of Physics and Interdisciplinary Center for Network Science & Applications University of Notre Dame Notre Dame Indiana 46556 USA Department of Mathematics University of South Carolina Columbia South Carolina 29208 USA Department of Computer Science and Engineering University of Notre Dame Notre Dame Indiana 46556 USA

Based on Jaynes’s maximum entropy principle, exponential random graphs provide a family of principled models that allow the prediction of network properties as constrained by empirical data (observables). However, their use is often hindered by the degeneracy problem characterized by spontaneous symmetry breaking, where predictions fail. Here we show that degeneracy appears when the corresponding density of states function is not log-concave, which is typically the consequence of nonlinear relationships between the constraining observables. Exploiting these nonlinear relationships here we propose a solution to the degeneracy problem for a large class of systems via transformations that render the density of states function log-concave. The effectiveness of the method is demonstrated on examples.

关键词： Mathematical transformations

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Performance Evaluation of Cloud Services with Profit Optimization

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Procedia computer science 2015年 54卷 24-30页

作者： M. Jaiganesh B. Ramadoss A. Vincent Antony Kumar S. Mercy Department of Information Technology PSNA College of Engg. and Tech Dindigul Tamilnadu India Department of Computer Applications National Institute of Technology Thiruchirappalli Tamilnadu India Department of Information Science & Engineering Banglore Institute of Technology Banglore Karnataka India

Cloud computing is a promising computing paradigm which allows distribution of services from a pool of resources. The services are required by the clients through on-demand via pay and use method. The greatest utilization of resources and maximum profits with scheduling is the main goal of the cloud service providers. The major issue in cloud computing is scheduling of services with improved global throughput and job scheduling. Since, cloud computing is a service based one, the performance evaluation is an important criteria to be dealt with. In this paper, we propose a Priority based Queuing model to evaluate the services leased by the cloud service providers. We consider general service time and response time for arriving requests and the waiting requests are stored in the queue. The services are considered to be SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service). We construct this using Queuing model with markovian arrival rate, general service rate, ‘m’ number of servers, priority queue discipline and a buffer of size ‘r’. The advantage of the proposed analytical model is within the time span, the cloud service provider schedules his services to result in maximum profit.

关键词： Performance evaluation Cloud computing Profit optimization Queuing model.

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F-PAC: A novel soft index based cluster head validation & gateway election mechanism for Ad Hoc Network

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WSEAS Transactions on computers 2014年 13卷 99-106页

作者： Thirmurugan, S. George Dharma Prakash Raj, E. Department of Computer Applications J.J. College of Engg and Tech. Tiruchirapalli India Department of Computer Science and Engineering Bharathidasan University Tiruchirapalli India

In this dynamic scenario the communication no longer happens in predetermined manner. The network as a platform for communication comes with high infrastructure may likely to waste the resources. Thus, the ad hoc scenario network came into existence. This network functionality has been enhanced through clustering mechanism. These clusters need to be perfect to sustain the efficient functionality of the network. Thus, this paper proposes F-PAC as a fuzzy logic based cluster validation technique to authenticate the cluster head identified by the existing cluster formation mechanisms. This procedure also helps to elect the gateway node for each cluster. This study has been shown using OMNET++ as simulator.

关键词： Fuzzy logic

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Performance Analysis of an Intrusion Detection System using Panjab University Intrusion Dataset

Performance Analysis of an Intrusion Detection System using ...

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International Conference on Recent Advances in engineering & Computational sciences

作者： Raman Singh Harish Kumar R. K. Singla Chitkara University Research and Innovation Network (CRIN) Chitkara University University Institute of Engineering and Technology Panjab University Department of Computer Science and Applications Panjab University

ISBN: (纸本)9781467382540

Intrusion Detection System (IDS) can be used to detect malware by its network activities or behavioral profiles. Common challenges for IDS are large amount of data to process, low detection rate and high rate of false alarms. Online Sequential Extreme Learning Machine (OS-ELM) based IDS with network traffic profiling is tested on Panjab University - Intrusion DataSet (PU-IDataSet). This IDS is known as alpha-FST-Beta IDS. The training connections are first categorized on the basis of protocol and service features. This categorization is named as alpha profiling. It increases the scalability and reduces the time complexity of IDS. Large feature set of network traffic dataset is reduced using ensemble of three feature selection techniques. Beta profiling is used to reduce the size of training dataset. Various parameters like accuracy, true positive rate, false positive rate, true negative rate, false negative rate, precision, F1-score and detection time is used to evaluate the performance. The results obtained encourage the integration of this system in intrusion detection models.

关键词： Intrusion Detection System Network Traffic Profiling Sampling Feature Selection Technique Online Sequential Extreme Learning Machine PU-IDataSet

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Finding high-redshift strong lenses in DES using convolutional neural networks

arXiv

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arXiv 2018年

作者： Jacobs, C. Collett, T. Glazebrook, K. McCarthy, C. Qin, A.K. Abbott, T.M.C. Abdalla, F.B. Annis, J. Avila, S. Bechtol, K. Bertin, E. Brooks, D. Buckley-Geer, E. Burke, D.L. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. da Costa, L.N. Davis, C. de Vicente, J. Desai, S. Diehl, H.T. Doel, P. Eifler, T.F. Flaugher, B. Frieman, J. García-Bellido, J. Gaztanaga, E. Gerdes, D.W. Goldstein, D.A. Gruen, D. Gruendl, R.A. Gschwend, J. Gutierrez, G. Hartley, W.G. Hollowood, D.L. Honscheid, K. Hoyle, B. James, D.J. Kuehn, K. Kuropatkin, N. Lahav, O. Li, T.S. Lima, M. Lin, H. Maia, M.A.G. Martini, P. Miller, C.J. Miquel, R. Nord, B. Plazas, A.A. Sanchez, E. Scarpine, V. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Smith, M. Soares-Santos, M. Sobreira, F. Suchyta, E. Swanson, M.E.C. Tarle, G. Vikram, V. Walker, A.R. Zhang, Y. Zuntz, J. Centre for Astrophysics & Supercomputing Swinburne University of Technology Victoria3122 Australia Institute of Cosmology & Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom School of Software and Electrical Engineering Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Cerro Tololo Inter-American Observatory National Optical Astronomy Observatory Casilla 603 La Serena Chile Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Physics and Electronics Rhodes University PO Box 94 Grahamstown6140 South Africa Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States LSST 933 North Cherry Avenue TucsonAZ85721 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Kavli Institute for Particle Astrophysics & Cosmology Stanford University P. O. Box 2450 StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ20921-400 Brazil Observatório Nacional Rua Gal. José Cristino 77 Rio de Janeiro RJ20921-400 Brazil Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Barcelona Institute of Science and Technology Campus UAB Bellaterra [Barcelona08193 Spain Madrid Spain Department of Physics IIT Hyderabad KandiTelangana502285 India Department of Astronomy/Steward Observatory 933 North Cherry Avenue TucsonAZ85721-0065 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91109 United States Kavli Institute for Cosmological Physics

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250,000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 19, g_mag > 20 and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7,301 galaxies. During visual inspection we rate 84 as "probably" or "definitely" lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9,428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range. Copyright © 2018, The Authors. All rights reserved.

关键词： Lenses

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