检索结果-内蒙古大学图书馆

International Symposium on Quality Electronic Design

作者： Song Bian Michihiro Shintani Shumpei Morita Masayuki Hiromoto Takashi Sato Dept. of Communications and Computer Engineering School of Informatics Kyoto University Undergraduate School of Electrical and Electronic Engineering Faculty of Engineering Kyoto University

ISBN: (纸本)9781509012145

As technology further scales semiconductor devices, aging-induced device degradation has become one of the major threats to device reliability. Hence, taking aging-induced degradation into account during the design phase can greatly improve the reliability of the manufactured devices. However, considering instance-dependent V_(th) degradations for extremely large circuits, like processors, is time-consuming. In this research, we focus on the negative bias temperature instability (NBTI) as the aging-induced degradation mechanism, and propose a fast and efficient way of estimating NBTI-induced delay degradation by utilizing static-timing analysis (STA) and simulation-based lookup table (LUT). Probability-based delay model (PBDM) for each gate is characterized in advance, having input slews, output capacitances and signal probabilities as the table indices. Using the PBDM modeled, path delays of arbitrary circuits can be efficiently estimated. With a typical five-stage pipelined processor as the design target, by comparing the calculated delay from LUT with the reference delay calculated by a commercial circuit simulator, we achieved 4114 times speedup within 5.6% delay error.

关键词： Negative bias temperature instability design target circuit simulator design phases 4-nitrobenzylthioinosine equipment reliability capacitance Semiconductor devices Speeding delay PROCESSOR deterioration gates advance

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Polar Coding for Achieving Secrecy over State-Dependent Wiretap Channels Without Any Instantaneous CSI

Hierarchical Polar Coding for Achieving Secrecy over State-D...

引用

作者： Si, Hongbo Koyluoglu, Onur Ozan Vishwanath, Sriram Laboratory for Informatics Networks and Communications Wireless Networking and Communications Group University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States

This paper presents a polar coding scheme to achieve secrecy in block fading binary symmetric wiretap channels without the knowledge of instantaneous channel state information (CSI) at the transmitter. For this model, a coding scheme that hierarchically utilizes polar codes is presented. In particular, on the polarization of different binary symmetric channels over different fading blocks, each channel use is modeled as an appropriate binary erasure channel over fading blocks. Polar codes are constructed for both coding over channel uses for each fading block and coding over fading blocks for certain channel uses. In order to guarantee security, random bits are introduced at appropriate places to exhaust the observations of the eavesdropper. It is shown that this coding scheme, without instantaneous CSI at the transmitter, is secrecy capacity achieving for the simultaneous fading scenario. For the independent fading case, the capacity is achieved when the fading realizations for the eavesdropper channel are always degraded with respect to the receiver. For the remaining cases, the gap between lower and upper bounds is analyzed. Remarkably, for the scenarios where the secrecy capacity is achieved, the results imply that the instantaneous CSI does not increase the secrecy capacity. © 2016 IEEE.

关键词： Fading channels

来源：评论

学校读者我要写书评

暂无评论

Supervised entity tagger for indonesian labor strike tweets using oversampling technique and low resource features

引用

Telkomnika (Telecommunication Computing Electronics and Control) 2016年第4期14卷 1462-1471页

作者： Purwarianti, Ayu Madlberger, Lisa Ibrahim, Muhammad School of Electrical Engineering and Informatics Bandung Institute of Technology Jl. Ganesa No. 10 Bandung Indonesia Institute of Software Technology and Interactive Systems Vienna University of Technology Favoritenstrasse 9-11/188 ViennaA-1040 Austria Faculty of Computer Science University of Indonesia Kampus UI Depok16424 Indonesia

We propose an entity tagger for Indonesian tweets sent during labor strike events using supervised learning methods. The aim of the tagger is to extract the date, location and the person/organization involved in the strike. We use SMOTE (Synthetic Minority Oversampling Technique) as an oversampling technique and conducted several experiments using Twitter data to evaluate different settings with varying machine learning algorithms and training data sizes. In order to test the low resource features, we also conducted experiments for the system without employing the word list feature and the word normalization. Our results indicated that different treatment of different types of machine learning algorithms with low resource features can lead to a good accuracy score. Here, we tried Naïve Bayes, C4.5, Random Forest and SMO (Sequential Minimal Optimization) algorithms using Weka as the machine learning tools. For the Naïve Bayes, due to the data distribution based of the class probability, the best accuracy was achieved by removing data duplication. For C4.5 and Random Forest, SMOTE gave higher accuracy result compared to the original data and the data with data duplication removal. For SMO, there is no significant difference among various sizes of training data. © 2016 Universitas Ahmad Dahlan.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Variation Ignition Timing Against Environmentally Friendly Gas Exhaust Emissions on 7K Gasoline Engine and Its Dissemination Using Website

引用

Journal of Physics: Conference Series 2018年第1期1114卷

作者： Rudi Adolf Hotman Sihombing Fatolosa Telaumbanua Hotler Manurung H Hartono Dahlan Abdullah Cut Ita Erliana Ricardo Freedom Nanuru Parwito Suwarni Karona Cahya Susena Indah Aprilianasari Abdussakir A A Saleh Muhammad Ikhsan Setiawan S Sriadhi Department of Mechanical Engineering Sekolah Tinggi Teknologi Immanuel Medan Indonesia Department of Mechanical Engineering Akademi Maritim Indonesia Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Universitas Kristen Indonesia Maluku. Ambon Indonesia Universitas Ratu Samban Bengkulu Indonesia Departement of Management Universitas Dehasen Bengkulu Indonesia Departement of Industrial Engineering Universitas 45 Surabaya Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia

The purpose of this research is to know the exhaust emissions generated due to changes in ignition and type of fuel. And expected to get various advantages, among others, in terms of environmentally friendly and health terms. In this research is to test directly by using tools that have been provided with mancatat data obtained from the test results and make observations. The results that can be in carrying out this research are the results of different exhaust emissions. However, the results of the study of exhaust emissions generated pertamax (Gasoline RON 92) fuel is less than the emissions of gas produced in gasoline RON 88, ranging from 1% - 3%. The results of this research will be disseminated using the website.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sensitive spin detection using an on-chip SQUID-waveguide resonator

arXiv

引用

arXiv 2017年

作者： Yue, G. Chen, L. Barreda, J. Bevara, V. Hu, L. Wu, L. Wang, Z. Andrei, P. Bertaina, S. Chiorescu, I. Department of Physics Florida State University TallahasseeFL32310 United States National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States CENSE State Key Laboratory of Functional Material for Informatics SIMIT Chinese Academy of Sciences Shanghai200050 China Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL32310 Marseil le France

Precise detection of spin resonance is of paramount importance to achieve coherent spin control in quantum computing. We present a novel setup for spin resonance measurements, which uses a dc-SQUID flux detector coupled to an antenna from a coplanar waveguide. The SQUID and the waveguide are fabricated from 20 nm Nb thin film, allowing high magnetic field operation with the field applied parallel to the chip. We observe a resonance signal between the first and third excited states of Gd spins S = 7/2 in a CaWO4crystal, relevant for state control in multi-level systems. Copyright © 2017, The Authors. All rights reserved.

关键词： Coplanar waveguides

来源：评论

学校读者我要写书评

暂无评论

Retraction: Variation Ignition Timing Against Environmentally Friendly Gas Exhaust Emissions on 7K Gasoline Engine and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012054)

引用

Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

关键词：

来源：评论

学校读者我要写书评

暂无评论

Special Section Guest Editorial: Color in Texture and Material Recognition

引用

Journal of Electronic Imaging 2016年第6期25卷

作者： Schettini, Raimondo Van De Weijer, Joost Cusano, Claudio Napoletano, Paolo University of Milano-Bicocca Department of Informatics Systems and Communication Viale Sarca 336 Milan20126 Italy Universitat Autònoma de Barcelona Computer Vision Center Edifici O Campus UAB Cerdanyola08193 Spain Università degli Studi di Pavia Department of Electrical Computer and Biomedical Engineering Via Ferrata 1 Pavia27100 Italy

The November/December 2016 special section of Journal of Electronic Imaging presents papers and theoretical advances in color and texture recognition. Cusano and colleagues show that features obtained from deep learning methods obtain excellent results for color texture classification. Sun and colleagues compare both hand-crafted and deep features for the task of facial expression recognition. An extension to the color domain of the popular local binary patterns (LBP) texture descriptor is proposed by Ledoux and colleagues Contourlets have been applied by Fang and colleagues to improve C-V active contour model to preserve detailed information in remote sensing images. Polec and colleagues propose a new feature that is based on the orthogonal transform, which is especially good for periodic textures. Martino and colleagues evaluate several texture/shape features for material recognition on time-of-flight cameras. Furthermore, they show that combining multiple transforms increases performance. Bello-Cerezo and colleagues evaluate several color spaces for material classification. They find that CIELAB outperforms many of the other color spaces for this task. Singh and Singh evaluate a large set of classifiers for the task of color prediction of the rice paddy plant leaf. Finally, Ciocca and colleagues apply genetic programming for the task of estimating the complexity of texture images. Chen and Wang propose a method for the detection of color-band resistors. The paper also addresses specular reflection suppression. Trujillo and colleagues study color features for the automatic screening of Salmonella strains.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Designing Data Mining Applications with Rough Set Algorithm for Provision of Recommendations in the Selection of Training Topics on Online Learning

引用

Journal of Physics: Conference Series 2018年第1期1114卷

作者： Hotler Manurung Erianto Ongko Astro Julida Harahap H Hartono Dahlan Abdullah Cut Ita Erliana S Sriadhi Aditya Halim Perdana Kusuma Putra Muslim Hamzah Ahmad Ricardo Freedom Nanuru A A Saleh Asmara Indahingwati Citra Kurniawan Ida Bagus Ary Indra Iswara Abdurrozzaq Hasibuan Eni Wuryani Wiwien Hadikurniawati Edy Winarno Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Management STIM Lasharan Jaya Makassar Indonesia Department of Accounting Universitas Muslim Indonesia Makassar Indonesia Universitas Kristen Indonesia Maluku. Ambon Indonesia Statistics Financial & Social Sciences Research Group Indonesia Manajement School of Economics Indonesia 'STIESIA' Surabaya Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Malang Indonesia Informatics Department STMIK STIKOM Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Accounting Universitas Negeri Surabaya Surabaya Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Online Learning is a learning model that utilizes internet network. As one of the institutions providing vocational education, ATI Immanuel is planned to implement online learning for improving the quality of education. help learners in choosing learning topics. The concept of data mining itself is a process of analysis of the database of students learning ATI Immanuel which has been collected and will be obtained patterns of student behavior in learning. The results of the study are expected to help online learning participants in this case ATI Immanuel students so they can choose the best training topics. Data mining applications with this rough set algorithm will be designed using PHP Programming Language and using System Development Life Cycle (SDLC) design method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial neural networks-based machine learning for wireless networks: A tutorial

arXiv

引用

arXiv 2017年

作者： Chen, Mingzhe Challita, Ursula Saad, Walid Yin, Changchuan Debbah, Mérouane Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Future Network of Intelligence Institute Chinese University of Hong Kong Shenzhen China Department of Electrical Engineering Princeton University PrincetonNJ United States School of Informatics University of Edinburgh Edinburgh United Kingdom Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Mathematical and Algorithmic Sciences Lab Huawei France R & D Paris France

In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based machine learning algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important for wireless network design. Such examples include echo state networks, liquid state machine, and long short term memory. And then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks and edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this article constitutes the first holistic tutorial on the development of ANN-based machine learning techniques tailored to the needs of future wireless networks. Copyright © 2017, The Authors. All rights reserved.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Search for and production in the final state using proton-proton collisions at

引用

The European Physical Journal C 2024年第7期84卷 1-33页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P. S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, T. Van Mechelen, P. Bols, E. S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Amarilo, K. Mota Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I. S. Giammanco, A. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Coelho, E. Hensel, C. De Oliveira, T. Menezes Moraes, A. Teles, P. Rebello Soeiro, M. Júnior, W. L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. De Souza, S. Fonseca De Souza, R. Gomes Martins, J. Herrera, C. Mora Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Pereira, A. Vilela Bernardes, C. A. Calligaris, L. Tomei, T. R. Fernandez Perez Gregores, E. M. Mercadante, P. G. Novaes, S. F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Kapoor, A. Liao, H. Liu, Z. -A. Sharma, R. Song, J. N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Levin, A Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerp Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Brussels Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City USA Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai China Zhejiang University Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechan

A search for $${\text {Z}{}{}} {\text {Z}{}{}} $$ and $${\text {Z}{}{}} {\text {H}{}{}} $$ production in the $${\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} {\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} $$ final state is presented, where H is the standard model (SM) Higgs boson. The search uses an event sample of proton-proton collisions corresponding to an integrated luminosity of 133 $$\,\text {fb}^{-1}$$ collected at a center-of-mass energy of 13 $$\,\text {Te}\hspace{-.08em}\text {V}$$ with the CMS detector at the CERN LHC. The analysis introduces several novel techniques for deriving and validating a multi-dimensional background model based on control samples in data. A multiclass multivariate classifier customized for the $${\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} {\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} $$ final state is developed to derive the background model and extract the signal. The data are found to be consistent, within uncertainties, with the SM predictions. The observed (expected) upper limits at 95% confidence level are found to be 3.8 (3.8) and 5.0 (2.9) times the SM prediction for the $${\text {Z}{}{}} {\text {Z}{}{}} $$ and $${\text {Z}{}{}} {\text {H}{}{}} $$ production cross sections, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：