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Cross-layer Joint Relay Selection and Power Allocation Scheme for Cooperative Relaying System

IOP Conference Series: Materials Science and Engineering 2018年第7期322卷

作者： Hui Zhi Mengmeng He Feiyue Wang Ziju Huang Key Lab of Computing Intelligent and Signal Processing (Ministry of Education) Anhui University Hefei China College of Electronics and Information Engineering Anhui University Hefei China

A novel cross-layer joint relay selection and power allocation (CL-JRSPA) scheme over physical layer and data-link layer is proposed for cooperative relaying system in this paper. Our goal is finding the optimal relay selection and power allocation scheme to maximize system achievable rate when satisfying total transmit power constraint in physical layer and statistical delay quality-of-service (QoS) demand in data-link layer. Using the concept of effective capacity (EC), our goal can be formulated into an optimal joint relay selection and power allocation (JRSPA) problem to maximize the EC when satisfying total transmit power limitation. We first solving optimal power allocation (PA) problem with Lagrange multiplier approach, and then solving optimal relay selection (RS) problem. Simulation results demonstrate that CL-JRSPA scheme gets larger EC than other schemes when satisfying delay QoS demand. In addition, the proposed CL-JRSPA scheme achieves the maximal EC when relay located approximately halfway between source and destination, and EC becomes smaller when the QoS exponent becomes larger.

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A novel measurement of uncertain information based on interval type-2 cross-entropy and its application

A novel measurement of uncertain information based on interv...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Qiang Ma Kongchun Mei Junjun Mao School of Mathematical Science. Anhui University Hefei PR China Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei PR China

ISBN: (纸本)9781538629024

The aim of this paper is to put forward a novel measurement of uncertain information to dispose of multiple attribute group decision making (MAGDM) based on interval type-2 cross-entropy. Firstly, some basic concepts and a ranking value formula to calculate the ranking value of interval type-2 fuzzy sets (IT2FSs) is briefly introduced. Meanwhile, a novel measurement based on interval type-2 cross-entropy to measure the uncertainty of the IT2FSs is investigated, the advantage of measurment can be interpreted as it not only considers the uncertainty of the element itself, but also considers the uncertainty caused by other elements. By using the measurement which has proposed, a method was presented to determine attribute weights based on the principle of minimization. Moreover, a decision making procedure based on measurement value is given to select the best alternative(s). Lastly, we apply the proposed decision making procedure based on measurement value to a simple practical example, which can illustrate the practicality and effectiveness.

关键词： C# languages

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DLTree: efficient and accurate phylogeny reconstruction using the dynamical language method

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Bioinformatics (Oxford, England) 2017年第14期33卷 2214-2215页

作者： Qi Wu Zu-Guo Yu Jianyi Yang Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan China. School of Mathematical Sciences Nankai University Tianjin China.

SUMMARY:A number of alignment-free methods have been proposed for phylogeny reconstruction over the past two decades. But there are some long-standing challenges in these methods, including requirement of huge computer memory and CPU time, and existence of duplicate computations. In this article, we address these challenges with the idea of compressed vector, fingerprint and scalable memory management. With these ideas we developed the DLTree algorithm for efficient implementation of the dynamical language model and whole genome-based phylogenetic analysis. The DLTree algorithm was compared with other alignment-free tools, demonstrating that it is more efficient and accurate for phylogeny reconstruction. AVAILABILITY AND IMPLEMENTATION:The DLTree algorithm is freely available at http://***. CONTACT:yuzuguo@*** or yangjy@***. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

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Superconvergence and asymptotic expansions for bilinear finite volume element approximation on non-uniform grids

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Journal of Computational and Applied Mathematics 2017年 321卷 323-335页

作者： Cunyun Nie Shi Shu Haiyuan Yu Wenhua Xia The School of Science Hunan Institute of Engineering 411104 China Hunan Key Laboratory for Computation & Simulation in Science and Engineering Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Hunan 411105 China

We initially derive an asymptotic expansion and a high accuracy combination formula of the derivatives in the sense of pointwise for the isoparametric bilinear finite volume element scheme by employing the energy-embedded method on some non-uniform grids. Furthermore, we prove that the approximate derivatives are convergent of order two. Numerical examples confirm theoretical results.

关键词： Isoparametric bilinear finite volume element scheme Non-uniform grids Asymptotic expansion Superconvergence

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Experimental Combined Grouping Analysis Approach for Robust Battery pack design for Electric Vehicles with Higher Performance

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IOP Conference Series: Earth and Environmental Science 2019年第1期268卷

作者： Liu Yun Li Shui Liang Gao Zhun Fan C. Ruhatiya Chin-Tsan Wang Akhil Garg Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University Guangdong China State Key Lab of Digital Manufacturing Equipment & Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou 515063 China School of Engineering Sciences Mahindra École Centrale Hyderabad Telangana 500043 India Department of Mechanical and Electro-Mechanical Engineering National I Lan University ILan Taiwan

Battery consistency is an important factor for battery pack performance. Excellent battery consistency can make battery packs more energy efficient and electric vehicles can have longer mileage and higher safety. Thus, in this study a comprehensive intelligent clustering methodology for the design of Li-ion battery pack on the basis of uniformity and equalization criteria of the cell was proposed. Firstly, multiple parameters (capacity, voltage, temperature and resistance) test of single cell performance was performed. Secondly, a clustering method combine with self-organizing map neural network (SOM) was proposed. Furthermore, a validation experiment (pack level) was carried out to verify the accuracy of proposed clustering algorithm. It can be concluded that the battery pack formed from SOM sorting results perform better than the battery pack having random cells combination as well as the pack originally purchased from the manufacturer.

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Maxwell-hydrodynamic model for simulating nonlinear terahertz generation from plasmonic metasurfaces

arXiv

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arXiv 2017年

作者： Fang, Ming Huang, Zhixiang Sha, Wei E.I. Wu, Xianliang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Electronic Engineering University of Hong Kong

The interaction between electromagnetic field and plasmonic nanostructures leads to both strong linear and nonlinear behaviors. In this paper, a time-domain hydrodynamic model for describing the motion of electrons in plasmonic nanostructures is presented, in which both surface and bulk nonlinearity are considered. A coupled Maxwell-hydrodynamic system capturing full-wave physics and free electron dynamics is numerically solved with the parallel finite-difference time-domain (FDTD) method. The validation of the proposed method is presented by simulating a plasmonic metasurface. The linear response is compared with the Drude dispersion model and the nonlinear terahertz emission from a difference-frequency generation process is theoretically analyzed. The work is fundamentally important to design nonlinear plasmonic nanodevices, especially for efficient and broadband THz emitters. Copyright © 2017, The Authors. All rights reserved.

关键词： Plasmonics

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Graphene based functional devices: A short review

arXiv

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arXiv 2018年

作者： Wang, Rong Jiang, Lijun Ren, Xingang Yan, Ze Sha, Wei E.I. Shan, Guangcun Department of Electrical and Electronic Engineering University of Hong Kong Pokfulam Road Hong Kong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230039 China School of Instrumentation Science & Opto-electronics Engineering Beihang University Beijing100191 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China School of Instrumentation Science & Opto-electronics Engineering Beihang University Beijing100191 China California NanoSystem Institute Department of Chemistry & Biochemistry University of California Los AngelesCA90095 United States

Graphene is an ideal 2D material system bridging electronic and photonic devices. It also breaks the fundamental speed and size limits by electronics and photonics, respectively. Graphene offers multiple functions of signal transmission, emission, modulation, and detection in a broad band, high speed, compact size, and low loss. Here, we will have a brief view of graphene based functional devices at microwave, terahertz, and optical frequencies. Their fundamental physics and computational models will be discussed as well. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphene

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Fractal and multifractal analyses of bipartite networks

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Scientific reports 2017年第1期7卷 45588页

作者： Jin-Long Liu Jian Wang Zu-Guo Yu Xian-Hua Xie Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 China. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Multiple attribute group decision making based on interval type-2 fuzzy cross-entropy and ranking value

Multiple attribute group decision making based on interval t...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Liangli Du Ying Peng Junjun Mao Dengbao Yao School of Mathematical Science Anhui University Hefei PR China Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei PR China Economics School Anhui University Hefei PR China

ISBN: (纸本)9781538629024

In this paper, we focus our attention on the issue of MAGDM under interval type-2 fuzzy environment where all the information provided by the decision makers is characterized by IT2FSs and the information about the attribute weights are partly known and completely unknown. Firstly, some basic concepts are introduced. Then, a new definition of the cross entropy which consider three aspects of fuzziness, hesitancy and interval information is given. Interval type-2 fuzzy entropy is proposed on the basis of the cross entropy. Furthermore, two ranking-entropy optimal models of each alternative are built. A new approach to multiple attribute group decision making with IT2FSs based on interval type-2 fuzzy cross-entropy and ranking value is shown. Finally, we give a practical example to verify the feasibility of our method and make some comparisons with other previous methods. The results verify the validity and effectiveness of our proposed method.

关键词： decision making entropy fuzzy set theory

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