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Proceedings of the Annual Hawaii International Conference on System Sciences 2014年 905-905页

作者： Pasi, Gabriella Pedrycz, Witold Dept. of Informatics Systems and Communication Università degli Studi di Milano Bicocca Viale Sarca 336 20126 Milan Italy Dept. of Electrical and Computer Engineering University of Alberta Edmonton T6R 2V4 AB Canada

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A comparative study of various microstrip bandpass filters topologies for UWB applications

A comparative study of various microstrip bandpass filters t...

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International Design and Test Workshop

作者： A. A. Saadi M. C. E. Yagoub R. Touhami Faculty of Electronics and Informatics U.S.T.H.B Algiers Algeria Centre de Développement des Technologies Avancées Algiers Algeria School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada

Because of their large spectrum, Ultra-Wide Band (UWB) systems are requested to maintain a very low power spectral density, making them vulnerable to higher power narrowband signals. To mitigate any in-band narrowband interference (NBI), which may degrade the overall UWB system performance, filtering is an inescapable solution. The aim of this paper is to compare the performance of various microstrip planar filters which are designed based on a specific electric model in order to provide UWB filter designers with key design tools to efficiently select the most suitable filter based on their particular needs.

关键词： Band-pass filters Matched filters Microstrip filters Chebyshev approximation Bandwidth Topology Tuning

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Prediction of coal face gas concentration by multi-scale selective ensemble hybrid modeling

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Journal of engineering Science and Technology Review 2014年第2期7卷 53-59页

作者： Wu, Xiang Yang, Zhong-Ru Li, Zhang Silvia, Pilati School of Information and Electrical Engineering China University Mining and Technology Xuzhou Jiangsu 221116 China China Huajin Coal Energy Co. Ltd Hejin Shanxi 043300 China School of Medical Informatics Xuzhou Medical College Xuzhou Jiangsu 221116 China School of Medical Imaging Xuzhou Medical College Xuzhou Jiangsu 221009 China Department of Electrical and Computer Engineering University of Massachusetts Amherst MA 01003 United States

A selective ensemble hybrid modeling prediction method based on wavelet transformation is proposed to improve the fitting and generalization capability of the existing prediction models of the coal face gas concentration, which has a strong stochastic volatility. Mallat algorithm was employed for the multi-scale decomposition and single-scale reconstruction of the gas concentration time series. Then, it predicted every subsequence by sparsely weighted multi unstable ELM(extreme learning machine) predictor within method SERELM(sparse ensemble regressors of ELM). At last, it superimposed the predicted values of these models to obtain the predicted values of the original sequence. The proposed method takes advantage of characteristics of multi scale analysis of wavelet transformation, accuracy and fast characteristics of ELM prediction and the generalization ability of L1 regularized selective ensemble learning method. The results show that the forecast accuracy has large increase by using the proposed method. The average relative error is 0.65%, the maximum relative error is 4.16% and the probability of relative error less than 1% reaches 0.785. © 2014 Kavala Institute of Technology.

关键词： Learning systems

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An intelligent flower analyzing system for medicinal plants 21

An intelligent flower analyzing system for medicinal plants

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21st International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2013

作者： Rodrigo, Ranga Samarawickrame, Kalani Mindya, Sheron School of Electrical and Computer Engineering University of Moratuwa Sri Lanka Informatics Institute of Technology Sri Lanka

ISBN: (纸本)9788086943763

The natural sciences have been transformed with the incorporation of advanced technologies, and the current technological wave of change is also revolutionizing biological science. With the wide use of herbal medicines in traditional medical systems, the demand for medicinal plants has increased enormously. Though the current trend has made the use of medicinal plants more popular, we still require better methods to distinguish between medicinal plants and plants which do not possess medicinal value. Manual methods of plant recognition rely on a plants flower information. However, these manual processes of flower recognition are not straightforward for lay persons unless expert guidance is provided. Motivated by this reasoning, an intelligent flower analyzing system has been developed to recognize medicinal plants. Various tests have been performed on 160 images taken from four types of flowers, to recognize flowers based on their colour and shape features. The experiment was carried out using Support Vector Machine (SVM) and Principal Component Analysis (PCA) methodologies respectively for colour and shape extraction. Acquiring an average of 65% recognition rate implies that the applicability PCA and SVM in the specified domain is a valuable step forward.

关键词： Principal component analysis

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Educational cooperation across countries by shared course materials: More than 10 years of experience

Educational cooperation across countries by shared course ma...

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International Conference on Interactive Collaborative Learning (ICL)

作者： Klaus Bothe Zoran Budimac Zoran Putnik Mirjana Ivanovic Stanimir Stoyanov Asya Stoyanova-Doyceva Katerina Zdravkova Boro Jakimovski Ioan Jurca Novica Nosovic Damir Kalpic Betim Cico Elinda Kajo Sanja Čandrlić Institute of Computer Science Humboldt University Berlin Berlin Germany Institute of Mathematics and Informatics University of Novi Sad Novi Sad Serbia Institute of Informatics University “Paisii Hilendarski” Plovdiv Bulgaria Institute of Informatics University “Paisii Hilendarski” Bulgaria Institute of Informatics University “Sts. Cyril and Methodius” Skopje FYR Macedonia Institute of Informatics Technical University Timisoara Timisoara Romania University of Sarajevo Sarajevo Bosnia Herzegovina Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Faculty of Contemporary Sciences and Technologies South East European University Tetovo Macedonia Faculty of Electrical Engineering Polytechnic University Tirana Tirana Albania University of Rijeka Rijeka Croatia

At the beginning of the new century, we started with an educational project aimed at the joint development and shared use of teaching materials for software engineering education. The aim was to transfer knowledge, as well as to save expenses. Over the years, our cooperation did not only cover the development of the course, but also its delivery, e.g. by guest lecturing. This paper reports on the experience gained in a multi-country project. Both success factors and problems are outlined.

关键词： Materials Educational institutions Project management Web sites Collaborative work informatics

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A Novel Method for Extrinsic Parameters Estimation between a Single-Line Scan LiDAR and a Camera

A Novel Method for Extrinsic Parameters Estimation between a...

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IEEE Intelligent Vehicles Symposium

作者： Pakapoj Tulsuk Panu Srestasathiern Miti Ruchanurucks Teera Phatrapornnant Hiroshi Nagahashi Faculty of Electrical Engineering Kasetsart University Thailand Geo-Informatics and Space Technology Development Agency Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Department of Information Processing Tokyo Institute of Technology Japan

ISBN: (纸本)9781479936397

This paper presents a novel method for extrinsic parameters estimation of a single line scan LiDAR and a camera. Using a checkerboard, the calibration setup is simple and practical. Particularly, the proposed calibration method is based on resolving geometry of the checkerboard that visible to the camera and the LiDAR. The calibration setup geometry is described by planes, lines and points. Our novelty is a new hypothesis of the geometry which is the orthogonal distances between LiDAR points and the line from the intersection between the checkerboard and LiDAR scan plane. To evaluate the performance of the proposed method, we compared our proposed method with the state of the art method i.e. Zhang and Pless [1]. The experimental results showed that the proposed method yielded better results.

关键词： planes lines points

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ICEPOLE: High-speed, hardware-oriented authenticated encryption

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8731卷 392-413页

作者： Morawiecki, Pawel Gaj, Kris Homsirikamol, Ekawat Matusiewicz, Krystian Pieprzyk, Josef Rogawski, Marcin Srebrny, Marian Wójcik, Marcin Institute of Computer Science Polish Academy of Sciences Poland Section of Informatics University of Commerce Kielce Poland Department of Computing Macquarie University Australia Electrical Engineering and Computer Science School Queensland University of Technology Brisbane Australia Cryptographic Engineering Research Group George Mason University United States Cryptography and Information Security Group University of Bristol United Kingdom Cadence Design Systems San Jose United States Intel Gdańsk Poland

This paper introduces our dedicated authenticated encryption scheme ICEPOLE. ICEPOLE is a high-speed hardware-oriented scheme, suitable for high-throughput network nodes or generally any environment where specialized hardware (such as FPGAs or ASICs) can be used to provide high data processing rates. ICEPOLE-128 (the primary ICEPOLE variant) is very fast. On the modern FPGA device Virtex 6, a basic iterative architecture of ICEPOLE reaches 41 Gbits/s, which is over 10 times faster than the equivalent implementation of AES-128-GCM. The throughput-to-area ratio is also substantially better when compared to AES-128-GCM. We have carefully examined the security of the algorithm through a range of cryptanalytic techniques and our findings indicate that ICEPOLE offers high security level. © International Association for Cryptologic Research 2014.

关键词： Field programmable gate arrays (FPGA)

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Inside Back Cover: Design Principles for Peptide‐Amphiphile‐Induced Liposomal Receptor‐Targeting with Intracellular Thermosensitivity (ChemNanoMat 1/2016)

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ChemNanoMat 2015年第1期2卷

作者： Kamlesh Shroff Dalong Liu Rajagopal N. Aravalli Colleen L. Forster Thomas Pengo Mark A. Sanders Emad S. Ebbini Efrosini Kokkoli Department of Chemical Engineering and Materials Science University of Minnesota Minneapolis MN 55455 USA Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN 55455 USA Department of Radiology University of Minnesota Minneapolis MN 55455 USA BioNet Clinical and Translational Science Institute University of Minnesota Minneapolis MN 55455 USA University of Minnesota Informatics Institute University of Minnesota Minneapolis MN 55455 USA University Imaging Centers University of Minnesota Minneapolis MN 55455 USA

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Erratum to: Search for heavy resonances decaying into a W or Z boson and a Higgs boson in final states with leptons and b-jets in 36 fb−1 of TeV pp collisions with the ATLAS detector

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Journal of High Energy Physics 2018年第11期2018卷 1-18页

作者： Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agatonovic-Jovin, T. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Bakker, P. J. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M-S Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovs Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene United States of America Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas – LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston United States of America Joi

One correction is noted for the paper. The exclusion limits for the gluon-gluon fusion production of an A boson reported in the conclusions did not correspond to the exclusion plots shown in figure 6a.

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AN EFFECTIVE EXAMPLE-BASED LEARNING METHOD FOR DENOISING OF MEDICAL IMAGES CORRUPTED BY HEAVY GAUSSIAN NOISE AND POISSON NOISE

AN EFFECTIVE EXAMPLE-BASED LEARNING METHOD FOR DENOISING OF ...

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IEEE International Conference on Image Processing

作者： Dinh-Hoan Trinh Marie Luong Francoise Dibos Jean-Marie Rocchisani Canh-Duong Pham Nguyen Linh-Trung Truong Q. Nguyen Center for Informatics and Computing VAST L2TI Universite Paris 13 99 Avenue Jean-Baptiste Clement LAGA Universite Paris 13 99 Avenue Jean-Baptiste Clement Fac. Electronics & Telecom. University of Engineering and Technology Vietnam National University Dept. Electrical & Computer Engineering University of California

ISBN: (纸本)9781479957521

Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.

关键词： Denoising Learning Medical imaging Gaussian noise Poisson noise

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