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IEEE National Conference on Radar

作者： Kevin M. Scott Ted R. Dabrowski William C. Barott Braham Himed Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL Air Force Research Laboratory Wright Patterson Air Force Base Dayton OH

ISBN: (纸本)9781509008643

This paper compares results of a simulation study and experimental measurement campaign designed to evaluate the performance of a DTV-based passive radar. The simulations considered a tower-mounted receiver in Springfield, Ohio, and predicted the accuracy of localizing airborne targets using the illuminations of regional transmitters. The predicted coverage area was found to vary with the selection of the propagation loss model used by the simulator. Supporting experiments at the site did not favor any particular model, but showed 10 dB variations in both the direct path and clutter over several hours. These results illustrate the need to accurately model tropospheric radio propagation in passive radar performance predictions.

关键词： Radio transmitters Passive radar Antenna arrays Predictive models Atmospheric modeling

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Fast wide-area P-SAR/ISAR performance prediction

Fast wide-area P-SAR/ISAR performance prediction

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IEEE National Conference on Radar

作者： William C. Barott Braham Himed Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL Air Force Research Laboratory Wright-Patterson Air Force Base Dayton OH

ISBN: (纸本)9781509008643

This paper presents techniques for predicting the resolution of the radar imaging point spread function (PSF). These techniques are useful for predicting the performance of multistatic passive synthetic aperture radars (P-SARs), and passive inverse SARs (P-ISARs), for which the PSF depends on a multitude of factors. Greedy algorithms are used in combination with the fast techniques to optimize the PSF resolution for both incoherent and coherent multistatic imaging scenarios, subject to constraints on combining methods, sparsity, and target isotropy.

关键词： Image resolution Radar imaging Imaging Passive radar Transmitters Geometry

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Dimension reconstruction for visual exploration of subspace clusters in high-dimensional data

Dimension reconstruction for visual exploration of subspace ...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Fangfang Zhou Juncai Li Wei Huang Ying Zhao Xiaoru Yuan Xing Liang Yang Shi School of Information Science and Engineering Central South University School of Software Central South University Key Laboratory of Machine Perception (Ministry of Education) Peking University Informatics & Decision Systems Engineering Arizona State University

ISBN: (纸本)9781509014521

Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.

关键词： Data visualization Visualization Clustering algorithms Minerals Data mining Algorithm design and analysis Pipelines

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Filling fuzzy ontologies with people knowledge using fuzzy ontologies and group decision making methods

Filling fuzzy ontologies with people knowledge using fuzzy o...

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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Juan Antonio Morente-Molinera Ignacio Javier Pérez Francisco Javier Cabrerizo Sergio Alonso Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Information Science University of Cádiz Cádiz Spain Department of Software Engineering and Computer Systems National Distance Education University (UNED) Madrid Spain Department of Languages and Computer Systems University of Granada Granada Spain

Internet has experienced a deep change. Now, people can communicate among themselves and share information and experiences thanks to Web 2.0 technologies. One way of storing all these information in a sorted way implies the use of Fuzzy Ontologies. In this paper, a novel and automatic method that is capable of retrieving and storing information coming from Internet users has been designed. Thanks to our method, it is possible to create knowledge databases that store knowledge coming from the consensus of a set of users. Thanks to group decision making methods, it is possible for all these users to debate and reach an agreement. Fuzzy Ontologies provide us with a trustful environment that we can use to store all the retrieved information. They also allow other users to consult and get advantage of the stored knowledge. Multi-granular fuzzy linguistic modelling methods are used in order to create an user-friendly interface for the users to debate and perform queries.

关键词： Ontologies Decision making Pragmatics Internet Electronic mail Data mining computer science

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On Secrecy Performance of MISO SWIPT systems with TAS and Imperfect CSI

On Secrecy Performance of MISO SWIPT Systems with TAS and Im...

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作者： Pan, Gaofeng Lei, Hongjiang Deng, Yansha Fan, Lisheng Yang, Jing Chen, Yunfei Ding, Zhiguo Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing400715 China School of Computing and Communications Lancaster University LancasterLA14YW United Kingdom Chongqing Key Laboratory of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Centre for Telecommunications King's College London LondonWC2R2LS United Kingdom School of Computer Science and Educational Software Guangzhou University Guangzhou510000 China Department of Electronic Engineering Shantou University Guangdong515063 China School of Information Engineering Yangzhou University Yangzhou225009 China School of Engineering University of Warwick CoventryCV47AL United Kingdom

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and N (N > 1) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels. © 2016 IEEE.

关键词： Energy harvesting

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Quantifying and Evaluating the Technical Debt on Mobile Cloud-Based Service Level

Quantifying and Evaluating the Technical Debt on Mobile Clou...

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Ad-Hoc and Sensor Networks Symposium

作者： Georgios Skourletopoulos Constandinos X. Mavromoustakis Jordi Mongay Batalla George Mastorakis Evangelos Pallis Georgios Kormentzas Mobile Systems (MoSys) Laboratory Department of Computer Science University of Nicosia National Institute of Telecommunications and Seidor S.A. Department of Informatics Engineering Technological Educational Institute of Crete Department of Information and Communication Systems Engineering University of the Aegean

ISBN: (纸本)9781479966653

As network bandwidth and coverage continue to increase, the adoption rates of mobile devices are growing over time and the mobile technology is becoming increasingly industrialized. In mobile cloud marketplaces, the cloud-supported mobile services can be leased off. However, the mobile service selection may introduce technical debt (TD), which is essential to be predicted and quantified. In this context, this paper examines the incurrence of technical debt in the future when leasing cloud-based mobile services by proposing a novel quantitative model, which adopts a linear and symmetric approach as a linear growth in the number of users is predicted. The formulation of the problem is based on a cost-benefit analysis, elaborating on the potential profit that could be obtained if the number of users would be equal to the maximum value. The probability of overutilization of the selected service in the long run is also researched. Finally, a quantification tool has been developed as a proof of concept (PoC), which initiates the technical debt analysis and optimization on mobile cloud-based service level and aims to provide insights into the overutilization or underutilization of a web service when a linear increase in the number of users occurs.

关键词： Technical debt quantification Technical debt evaluation Quantitative model Service level selection Linearity Decision making

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Big data in smart cities

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Science China(information Sciences) 2015年第10期58卷 179-190页

作者： LI DeRen CAO JianJun YAO Yuan State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Collaborative Innovation Center of Geospatial Technology Geomatics Center of Gansu Province National Engineering Research Center for Multimedia Software Computer School Wuhan University

In this paper, we discuss the concept of the smart city, summarize its development, analyze the motivation and goals of building smart cities in China, and illustrate the supporting technologies of the smart city. Next, we propose a smart city infrastructure that is based on the concept of the digital city and integrates the Internet of Things and cloud computing technologies, thereby achieving automatic control and intelligence services for people and logistics in physical cities. We analyze the big data of smart cities according to ubiquitous sensor networks. Facing the problems and challenges caused by big data of smart cities, we propose a strategy to handle big data that focuses on cloud computing and data mining. In addition, we present a basic framework for cloud computing platforms and propose the establishment of an operating center for smart cities. In closing,we discuss the bright prospects of smart cities.

关键词： smart city big data digital city Internet of Things cloud computing data mining intelligence service

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Smart Grid Data Analysis and Prediction Modeling

Smart Grid Data Analysis and Prediction Modeling

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Hang Yang Ping Li Anum Masood Yuning Xiao Bin Sheng Qichen Yu Information Department China Southern Power Grid Department of Mathematics and Information Technology The Education University of Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science COMSATS Institute of Information Technology The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Department of electrical engineering and automation Shanghai Jiao Tong University

With the rapiddevelopment of power grid, prediction ofelectric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity,time, and the number of people. On account of the standards of support vector machine(SVM) and the HBase platform,we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidityhardly have any effects.

关键词： power prediction data mining HBase data storage machine learning SVM

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Quantum State Tomography via Reduced Density Matrices

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Physical Review Letters 2017年第2期118卷 020401-020401页

作者： Tao Xin Dawei Lu Joel Klassen Nengkun Yu Zhengfeng Ji Jianxin Chen Xian Ma Guilu Long Bei Zeng Raymond Laflamme State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics University of Guelph Guelph N1G 2W1 Ontario Canada Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph N1G 2W1 Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences 100190 Beijing China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland USA Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Canadian Institute for Advanced Research Toronto M5G 1Z8 Ontario Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements.

关键词： Quantum entanglement Quantum information processing Quantum tomography Random matrix theory

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A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3722-3733页

作者： Yuan, Weitao Liang, Xiaodan Chen, Hanning Na, Lin Zou, Tao School of Computer Science and Software Engineering Tianjin Polytechnic University Tanjin300387 China Beijing Shenzhou Aerospace Software Technology Company Limited Beijing110000 China Laboratory of Information Service and Intelligent Control Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Robust Principal Component Analysis (RPCA), which is a popular parsimony model, is becoming increasingly important for researchers to do data analysis and prediction. The RPCA formulation is made of two components: sparse penalty and low rank penalty. These two competing terms are balanced with one parameter, which is essential for the effectiveness of RPCA. However, in real-world applications, the lack of data adaptive methods for choosing the right parameter hinders the popularization of RPCA. In this work, RPCA is generalized to a multiobjective optimization problem without any balancing parameter. The new model is named as Multiobjective Robust Principal Component Analysis (MRPCA). We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve RPCA problem, which is a high dimensional convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. To handle this dimensionality problem, we introduce a novel mutation, termed as Alternating Direction Method of Multipliers mutation (ADMM mutation), that works well in high dimensional decision space. Numerical experiments show that this modified NSGA-II, which converges much faster than the standard one, can deal with the curse of dimensionality well. Furthermore, numerical image reconstruction test confirms that the reconstruction performance of our modified NSGA-II is better than the traditional proximal algorithm, which is usually used to solve RPCA problem. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Multiobjective optimization

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