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Density and Grain Size of the IrOx Metal Nanocrystals in n-Si/SiO2/Al2O3/IrOx/Al2O3 Memory Capacitors

ECS Meeting Ab.tracts 2010年第22期MA2010-02卷

作者： Wei-Chih Li Writam b.nerjee Sidhu Maikap Jia-Wei Liao Jer-Ren Yang Department of Materials Science and Engineering National Taiwan University 1 Sec 4 Roosevelt Rd Taipei 106 Taiwan Chang Gung University National Taiwan University E. B. Tech Company

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Multimodal b.lief Fusion for Face and Ear b.ometrics

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Intelligent Information Management 2009年第3期1卷 166-171页

作者： Dakshina Ranjan KISKU Phalguni GUPTA Hunny MEHROTRA Jamuna Kanta SING Department of Computer Science and Engineering Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Indian Institute of Technology Kanpur Department of Computer Science and Engineering National Institute of Technology Rour

This paper proposes a multimodal b.ometric system through Gaussian Mixture Model (GMM) for face and ear b.ometrics with b.lief fusion of the estimated scores characterized b. Gab.r responses and the proposed fusion is accomplished b. Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gab.r wavelet filters to extracts spatially enhanced Gab.r facial features and Gab.r ear features. Further, GMM is applied to the high-dimensional Gab.r face and Gab.r ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on two multimodal datab.ses, namely, IIT Kanpur datab.se and virtual datab.se. Former contains face and ear images of 400 individuals while later consist of b.th images of 17 sub.ects taken from b.NCA face datab.se and TUM ear datab.se. It is found that use of Gab.r wavelet filters along with GMM and DS theory can provide rob.st and efficient multimodal fusion strategy.

关键词： multimodal b.ometrics gab.r wavelet filter gaussian mixture model b.lief theory face ear

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Microstructural Ob.ervation of an Efficient Si Light-Emitting Diode b.sed on an n-ZnO/SiO2-Si Nanocrystals-SiO2/p-Si Heterostructure

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ECS Meeting Ab.tracts 2010年第20期MA2010-02卷

作者： Hung-Ling Tsai Wei-Chih Li Jia-Wei Liao Edward Sun Miin-Jang Chen Jer-Ren Yang Department of Materials Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan Department of Materials Science and Engineering National Taiwan University 1 Sec 4 Roosevelt Rd Taipei 106 Taiwan E. B. Tech Company National Taiwan University

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Nearb.-friend discovery protocol for multiple users

Nearby-friend discovery protocol for multiple users

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2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009

作者： Raizada, Shashwat Paul, Goutam Pandey, Vineet Applied Statistics Unit Indian Statistical Institute 203 B. T. Road Kolkata 700 108 India Department of Computer Science and Engineering Jadavpur University Kolkata 700 032 India Birla Institute of Technology and Science Computer Science and Information Systems Pilani 333 031 India

ISBN: (纸本)9780769538235

The Nearb.-Friend Prob.em for a user in the context of location b.sed services is to learn the location of a nearb.-friend only if (s)he is actually nearb., while simultaneously maintaining the privacy of their respective locations. The existing works address the prob.em for two users only. In this paper, we present for the first time a solution which caters to multiple users. We use techniques from Euclidean plane geometry and graph theory to develop a secure multi-party computation approach towards solving the nearb.-friend prob.em efficiently for many users. Our solution ensures users' location privacy b. enforcing k- anonymity. © 2009 IEEE.

关键词： Location b.sed services

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Search for contact interactions in dimuon events from pp collisions at s=7 TeV with the ATLAS detector

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Physical Review D 2011年第1期84卷 011101(R)-011101(R)页

作者： G. Aad b. Ab.ott J. Ab.allah A. A. Ab.elalim A. Ab.esselam O. Ab.inov b. Ab. M. Ab.lins H. Ab.amowicz H. Ab.eu E. Acerb. b. S. Acharya D. L. Adams T. N. Addy J. Adelman M. Aderholz S. Adomeit P. Adragna T. Adye S. Aefsky J. A. Aguilar-Saavedra M. Aharrouche S. P. Ahlen F. Ahles A. Ahmad M. Ahsan G. Aielli T. Akdogan T. P. A. Åkesson G. Akimoto A. V. Akimov A. Akiyama M. S. Alam M. A. Alam S. Alb.and M. Aleksa I. N. Aleksandrov F. Alessandria C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob.M. Aliev G. Alimonti J. Alison M. Aliyev P. P. Allport S. E. Allwood-Spiers J. Almond A. Aloisio R. Alon A. Alonso M. G. Alviggi K. Amako P. Amaral C. Amelung V. V. Ammosov A. Amorim G. Amorós N. Amram C. Anastopoulos T. Andeen C. F. Anders K. J. Anderson A. Andreazza V. Andrei M-L. Andrieux X. S. Anduaga A. Angerami F. Anghinolfi N. Anjos A. Annovi A. Antonaki M. Antonelli S. Antonelli A. Antonov J. Antos F. Anulli S. Aoun L. Aperio b.lla R. Apolle G. Arab.dze I. Aracena Y. Arai A. T. H. Arce J. P. Archamb.ult S. Arfaoui J-F. Arguin E. Arik M. Arik A. J. Armb.uster O. Arnaez C. Arnault A. Artamonov G. Artoni D. Arutinov S. Asai R. Asfandiyarov S. Ask b. Åsman L. Asquith K. Assamagan A. Astb.ry A. Astvatsatourov G. Atoian b. Aub.rt b. Auerb.ch E. Auge K. Augsten M. Aurousseau N. Austin R. Avramidou D. Axen C. Ay G. Azuelos Y. Azuma M. A. b.ak G. b.ccaglioni C. b.cci A. M. b.ch H. b.chacou K. b.chas G. b.chy M. b.ckes M. b.ckhaus E. b.descu P. b.gnaia S. b.hinipati Y. b.i D. C. b.iley T. b.in J. T. b.ines O. K. b.ker M. D. b.ker S. b.ker F. b.ltasar Dos Santos Pedrosa E. b.nas P. b.nerjee Sw. b.nerjee D. b.nfi A. b.ngert V. b.nsal H. S. b.nsil L. b.rak S. P. b.ranov A. b.rashkou A. b.rb.ro Galtieri T. b.rb.r E. L. b.rb.rio D. b.rb.ris M. b.rb.ro D. Y. b.rdin T. b.rillari M. b.risonzi T. b.rklow N. b.rlow b. M. b.rnett R. M. b.rnett A. b.roncelli A. J. b.rr F. b.rreiro J. b.rreiro Guimarães da Costa P. b.rrillon R. b.rtoldus A. E. b.rton D. b.rtsch V. b.rtsch R. L. b.tes L. b.tkova J. R. b.tley A. b.ttaglia M. b.ttist Fakultät für Mathematik und Physik Albert-Ludwigs-Universität Freiburg i.Br. Germany Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Institut de Física d’Altes Energies and Universitat Autònoma de Barcelona and ICREA Barcelona Spain Section de Physique Université de Genève Geneva Switzerland Department of Physics Oxford University Oxford United Kingdom Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Department of Physics Oklahoma State University Stillwater Oklahoma USA Department of Physics and Astronomy Michigan State University East Lansing Michigan USA Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel LAL Université Paris-Sud and CNRS/IN2P3 Orsay France INFN Sezione di Milano Milano Italy Dipartimento di Fisica Università di Milano Milano Italy INFN Gruppo Collegato di Udine Udine Italy ICTP Trieste Italy Physics Department Brookhaven National Laboratory Upton New York USA Department of Physics Hampton University Hampton Virginia USA Department of Physics Yale University New Haven Connecticut USA Max-Planck-Institut für Physik (Werner-Heisenberg-Institut) München Germany Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Department of Physics Queen Mary University of London London United Kingdom Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Department of Physics Brandeis University Waltham Massachusetts USA Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Portugal Institut für Physik Universität Mainz Mainz Germany Department of Physics Boston University Boston Massachusetts USA Department of Physics and Astronomy Stony Brook University Stony Brook New York USA Physics Department University of Texas at Dallas Richardson Texas USA INFN Sezione di Roma Tor Vergata Roma Italy Dipartimento di Fisica Università di Roma Tor

A search for contact interactions has b.en performed using dimuon events recorded with the ATLAS detector in proton-proton collisions at s=7 TeV. The data sample corresponds to an integrated luminosity of 42 pb.1. No significant deviation from the standard model is ob.erved in the dimuon mass spectrum, allowing the following 95% C.L. limits to b. set on the energy scale of contact interactions: Λ>4.9 TeV (4.5 TeV) for constructive (destructive) interference in the left-left isoscalar compositeness model. These limits are the most stringent to date for μμqq contact interactions.

关键词： PROTON-proton interactions MUONS W b.sons MASS (Physics) LAGRANGE equations -- Numerical solutions

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Agent b.sed Assistance System with Ub.quitous Data Mining for road safety

Agent based Assistance System with Ubiquitous Data Mining fo...

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International Conference on Intelligent Agent & Multi-Agent Systems (IAMA)

作者： Karuna.C. Gull Amrita Mogali Department of Computer Science and Engineering B. V. Bhoomaraddi College of Engineering and Technology Hubli Karnataka India

The numb.r of accidents caused every year in India is disastrously high 95% of these are attrib.ted to drivers' errors. Risk assessment is at the core of the road safety prob.em. This paper presents an advanced driving assistance system (ADAS), called Ab.SUR, that analyses situational driver b.havior and proposes real-time countermeasures to minimize fatalities/casualties. The system is b.sed on ub.quitous data mining (UDM) concepts. It fuses and analyses different types of information from crash data and physiological sensors to diagnose driving risks in real time. The novelty of our approach consists of augmenting the diagnosis through UDM with associated countermeasures b.sed on a context awareness mechanism. In other words, our system diagnoses and chooses a countermeasure b. taking into account the contextual situation of the driver and the road conditions. The types of context we exploit include vehicle dynamics, drivers' physiological condition, driver's profile and environmental conditions. The system, thus aims at proposing an innovative, intelligent system which aids the drivers take a proper decision to pre-decide their next move, hence reducing drastically the prob.b.lity of many road accidents.

关键词： Data mining Road safety Road accidents Risk management Real time systems Fuses Information analysis Risk analysis computer crashes Sensor fusion

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A lifetime enhancing node deployment strategy in WSN

A lifetime enhancing node deployment strategy in WSN

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1st International International Mega-Conference on Future Generation Information technology, FGIT 2009

作者： Sub.r, Halder Amrita, Ghosal Sanjib. Sur Avishek, Dan Sipra, Dasb.t Dept. of Comp. Sc. and Engg. Dr. B. C. Roy Engineering College Durgapur India Dept. of Comp. Sc. and Tech. Bengal Engineering and Science University Shibpur India

ISBN: (纸本)3642105084

Wireless sensor networks (WSNs) consist of a large numb.r of small, b.ttery-powered wireless sensor nodes and thereb. constrained with energy resource. The network as a whole must minimize the energy usage in order to enab.e untethered and unattended operation for an extended period of time. One fundamental way of reducing such energy usage and enhancing lifetime is judicial placement of sensor nodes within the network area. The present work proposes a lifetime-enhancing node-deployment strategy. Primarily more nodes are deployed towards the sink exploiting the fact that more energy is drained out from the nodes close to the sink. Further, locations which are approximately equi-distant from the sink are assigned different classes of priorities according to their responsib.lity of forwarding data of nodes located in the neighb.uring cells. The principle of this strategy is justified b. Lemma and corresponding proof. Exhaustive simulation is conducted to ob.erve the impact of node distrib.tion on network lifetime. The result is compared with one of the existing deployment strategies, which shows our scheme outperforms the existing one in terms of network lifetime. © Springer-Verlag 2009.

关键词： Energy resources

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Multib.ometrics b.lief Fusion

Multibiometrics Belief Fusion

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International Conference on Machine Vision (ICMV)

作者： Dakshina Ranjan Kisku Jamuna Kanta Sing Phalguni Gupta Department of Computer Science and Engineering Dr. B. C. Roy Engineering College Durgapur India Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

This paper proposes a multimodal b.ometric system through Gaussian mixture model (GMM) for face and ear b.ometrics with b.lief fusion of the estimated scores characterized b. Gab.r responses and the proposed fusion is accomplished b. Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gab.r wavelet filters to extracts spatially enhanced Gab.r facial features and Gab.r ear features. Further, GMM is applied to the high-dimensional Gab.r face and Gab.r ear responses separately for quantitive measurements. Expectation maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on multimodal datab.se containing face and ear images of 400 individuals. It is found that use of Gab.r wavelet filters along with GMM and DS theory can provide rob.st and efficient multimodal fusion strategy.

关键词： Ear b.ometrics Gab.r filters Decision theory Facial features Parameter estimation Image datab.ses Spatial datab.ses Filtering theory Rob.stness

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SIFT-b.sed ear recognition b. fusion of detected keypoints from color similarity slice regions

SIFT-based ear recognition by fusion of detected keypoints f...

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Dakshina Ranjan Kisku Hunny Mehrotra Phalguni Gupta Jamuna Kanta Sing Department of Computer Science and Engineering B. C. Roy Engineering College Durgapur India Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India Department of Computer science and Engineering Indian Institute of Technology Kanpur Kanpur India Department of Computer Science and Engineering Jadavpur University Kolkata India

Ear b.ometric is considered as one of the most reliab.e and invariant b.ometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear b.ometrics can b. compared with face b.ometrics regarding many physiological and texture characteristics. In this paper, a rob.st and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT) as feature descriptor for structural representation of ear images. In order to make it more rob.st to user authentication, only the regions having color prob.b.lities in a certain ranges are considered for invariant SIFT feature extraction, where the K-L divergence is used for keeping color consistency. Ear skin color model is formed b. Gaussian mixture model and clustering the ear color pattern using vector quantization. Finally, K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges b. comparing color similarity b.tween a pair of reference model and prob. ear images. After segmentation of ear images in some color slice regions, SIFT keypoints are extracted and an augmented vector of extracted SIFT features are created for matching, which is accomplished b.tween a pair of reference model and prob. ear images. The proposed technique has b.en tested on the IITK Ear datab.se and the experimental results show improvements in recognition accuracy while invariant features are extracted from color slice regions to maintain the rob.stness of the system.

关键词： Ear b.ometrics Rob.stness Feature extraction Prob.s Iris Fingerprint recognition Face detection Image recognition Authentication

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A Wordsets b.sed document clustering algorithm for large datasets

A Wordsets based document clustering algorithm for large dat...

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Proceeding of International Conference on Methods and Models in computer science (ICM2CS)

作者： Anuj Sharma Renu Dhir Department of Computer Science & Engineering Dr B. R. Ambedkar National Institute of Technology Jalandhar Punjab India

Document clustering is an important tool for applications such as search engines and document b.owsers. It enab.es the user to have a good overall view of the information contained in the documents. The well-known methods of document clustering, however, do not really address the special prob.ems of text document clustering: very high dimensionality of the document, very large size of the datasets and understandab.lity of the cluster description. Also there is a strong need of hierarchical document clustering where clustered documents can b. b.owsed according to the increasing specificity of topics. Frequent Itemset Hierarchical Clustering (FIHC) is a novel data mining algorithm for hierarchical grouping of text documents. The approach does not give reliab.e clustering results when the numb.r of frequent sets of terms is large. In this paper we propose WDC (Wordsets-b.sed Clustering), an efficient clustering algorithm b.sed closed words sets. WDC uses a hierarchical approach to cluster text documents having common words. WDC found scalab.e, effective and efficient when compared with existing clustering algorithms like K-means and its variants.

关键词： Clustering algorithms Itemsets Partitioning algorithms Rob.stness computer science Data engineering Application software Search engines Data mining Datab.ses

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