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5th IEEE International Conference on Electronic Information and communication Technology, ICEICT 2022

作者： Xin, Wei Xu, Lin Gao, Jincheng Li, Yingsong Huang, Zhixiang College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Wuhan Second Ship Design and Research Institute Wuhan China Zhaojin Mining Industry Co. Ltd. Zhaoyuan China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A modified coprime array (CA) with a name of extended CA (ECA) is presented and constructed to reduce over-lapping sensors between sum and difference coarray using conventional CA (CCA) to get a sum-difference coarray with high degrees- of-freedoms (DOFs). The devised ECA is to shift a sub array of CCA to reduce redundancy and then to flip part of shifted sub array around zero point that is regarded as symmetric center. The onstructed ECA expands the inter-element separations with lower mutual coupling. Numerical simulation are provided to verify superiority of the devised ECA. © 2022 IEEE.

关键词： Direction of arrival

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Design methods for polymorphic combinational logic circuits based on the Bi-Decomposition approach

arXiv

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arXiv 2017年

作者： Li, Zhifang Luo, Wenjian Yue, Lihua Wang, Xufa School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230027 China Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei Anhui230027 China

Polymorphic circuits are a special kind of digital logic components, which possess multiple build-in functions. In different environments, a polymorphic circuit would perform different functions. Evolutionary Algorithms, Binary Decision Diagrams (BDD) and the multiplex method have been adopted to design polymorphic circuits. However, the evolutionary methods face the scalable problem. The BDD method consumes too much gate resource. The polymorphic circuit built by the multiplex method rarely contains polymorphic gates. In this paper, based on the traditional Bi-Decomposition circuit design approach, two methods, *** Poly-Bi-Decomposition method and the TransformationBi-Decomposition method, are proposed for designing polymorphic circuits. The Poly_Bi-Decomposition method can design relatively large and gate-efficient polymorphic circuits with a higher percentage of polymorphic gates. The TransformationBi-Decomposition method can use the traditional circuit design approaches and tools, e.g. Bi-Decomposition, to design polymorphic circuits directly. The experimental results show the good performance of the proposed methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Timing circuits

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Semi-supervised learning with local and global consistency by geodesic distance and sparse representation 1

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3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data engineering, IScIDE 2012

作者： Gui, Jie Zhao, Zhongqiu Hu, Rongxiang Jia, Wei Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui 230031 China State Key Laboratory for Novel Software Technology Nanjing University China College of Computer Science and Information Engineering Hefei University of Technology China Institue of Energy Safety Chinese Academy of Sciences Hefei Anhui 230031 China

ISBN: (数字)9783642366697

ISBN: (纸本)9783642366680

In many practical data mining applications, such as web categorization, key gene selection, etc., generally, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, in recent years, semi-supervised learning algorithms such as graph-based methods have attracted much attention. However, most of these traditional methods adopted a Gaussian function to calculate the edge weights of the graph. In this paper, a novel weight for semi-supervised graph-based methods is proposed. The new method adds the label information from problem into the target function, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting the calculation. In addition, we also add class prior knowledge from problem into semi-supervised learning algorithm. Here we address the problem of learning with local and global consistency (LGC). It was found that the effect of class prior knowledge was probably different between under high label rate and low label rate. Furthermore, we integrate sparse representation (SR) in LGC algorithm. Experiments on a UCI data set show that our proposed method outperforms the original algorithms. © Springer-Verlag 2013.

关键词： Learning algorithms

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Fisher+Kernel Criterion for discriminant analysis

Fisher+Kernel Criterion for discriminant analysis

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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

作者： Yang, Shu Yan, Shuicheng Xu, Dong Tang, Xiaoou Zhang, Chao National Laboratory on Machine Perception Peking University Beijing China Department of Information Engineering Chinese University of Hong Kong Shatin Hong Kong MOE-Microsoft Key Laboratory of Multimedia Computing and Communication Department of EEIS University of Science and Technology of China Hefei Anhui China Microsoft Research Asia Beijing China

ISBN: (纸本)769523722

We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an efficient procedure is derived to optimize this new criterion in an iterative manner. More specifically, original input vector is first transformed into a higher dimensional feature matrix through a battery of nonlinear mappings involved in different kernels. Then, based on the feature matrices, FKC is presented within two coupled projection spaces: one projection space is used to search for the optimal combinations of kernels; while the other encodes the optimal nonlinear discriminating projection directions. Our proposed method is a unified framework for both kernel selection and nonlinear discriminant analysis. Besides, the algorithm potentially alleviates overfitting problem existing in traditional KDA and has no singularity problems in most cases. The effectiveness of our proposed algorithm is validated by extensive face recognition experiments on several datasets. © 2005 IEEE.

关键词： Pattern recognition

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Joint Sink Mobility and Data Diffusion for Lifetime Optimization in Wireless Sensor Networks

Joint Sink Mobility and Data Diffusion for Lifetime Optimiza...

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IEEE Asia-Pacific Conference on Services computing (APSCC)

作者： Yu Gu Hengchang Liu Fei Song Baohua Zhao Department of Computer Science Anhui Province Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei Anhui China Department of Computer Science University of Virginia Charlottesville VA USA

In this paper, we address the problem of lifetime optimization under storage constraint for wireless sensor networks with a mobile sink node. The problem is particularly challenging since we need to consider both mobility scheme and storage constraint. Previous works suggest to use a simple single-hop routing model in which source nodes can only communication with the sink node directly in those mobile networks. However, we notice that this statement is unsuitable for sensor networks with storage constraint because we prove it is a NP-complete problem under single-hop routing model by reducing the traveling salesman problem (TSP) to it in polynomial time. Hence, we try a different way. First we analyze this problem and give a lifetime upperbound, so whether this upperbound is tight is what we concern mostly. Thus, we first construct a 2-approximation O(n 2 ) algorithm to solve the TSP problem, then a novel data diffusion mechanism is built to achieve this upperbound. We prove that under some reasonable assumptions, our algorithm can output this optimal lifetime.

关键词： Wireless sensor networks Routing Computer networks Constraint optimization Computer science Mobile communication NP-complete problem Traveling salesman problems Polynomials Monitoring

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Compact Broadband Rectifier With Wide Input Power Range Based on Adaptive Input Power Distribution

TechRxiv

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TechRxiv 2022年

作者： Liu, Xin Li, Yingsong Zhang, Dawei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

This paper presents a novel compact rectifier array with both broadband and wide input power range characteristics. The rectifier array consists of two rectifier units operating at low-power and high-power levels, respectively, and an adaptive input power distribution network (AIPDN) enabling automatic distribution of radio frequency (RF) energy at different power levels. In addition, a matching inductance is introduced to counteract the capacitance of the diode. As a result, the high RF-DC power conversion efficiency (PCE) is achieved in both broadband and wide input power range. For demonstration, a prototype of the presented rectifier array is fabricated and measured. The measured results are in good accordance with the simulated results. With an input power range of 8 – 25 dBm and a bandwidth of 1.82 – 2.75 GHz (fractional bandwidth of 40.7%), the PCE is greater than 50%. At 2.4 GHz, the input power ranges from -5.5 to 27.5 dBm with PCE greater than 50%. Besides, a peak PCE of 72.1% is obtained at an input power of 25 dBm. This work can lay the foundation for the development of a new RF energy harvest systems with wide frequency band and dynamic input power. © 2022, CC BY.

关键词： Horn antennas

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Evolutionary Design of Relatively Large Combinational Circuits with an Extended Stepwise Dimension Reduction

Evolutionary Design of Relatively Large Combinational Circui...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zhifang Li Wenjian Luo Lihua Yue Xufa Wang School of Computer Science and Technology University of Science and Technology Hefei Anhui China Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei Anhui China University of Science and Technology of China Hefei Anhui CN

In this paper, an eXtended Stepwise Dimension Reduction approach (XSDR) to evolutionary design of relatively large combinational logic circuits is proposed. In our previous work, a Stepwise Dimension Reduction approach (SDR) is introduced. The SDR divides a circuit into several layers. The layers are evolved one after another. However, some layers are difficult to be evolved. The XSDR improves the SDR by decomposing the original truth table of a layer to two truth tables. The new truth tables after decomposing are easy to be evolved. The proposed method has been tested with multipliers and the circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The experimental results demonstrate that the XSDR extensively improves the performance of the SDR in terms of the number of fitness evaluations and the computational time.

关键词： Combinational circuits Hardware Circuit testing Evolutionary computation Electronic circuits Computer science Laboratories Assembly Microelectronics Benchmark testing

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Link-weighted and distance-constrained channel assignment in single-radio wireless mesh networks

Link-weighted and distance-constrained channel assignment in...

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Conference on Local Computer Networks (LCN)

作者： Junfeng Jin Baohua Zhao Hao Zhou School of Computer Science University of Science and Technology Hefei Anhui China Province Key Laboratory of Software in Computing and Communication Hefei Anhui China School of Computer Science University of Science and Technology of China Hefei Anhui

In this work, we study a link-weighted and distance-constrained channel assignment problem in multi-channel mesh networks with stationary router nodes, such as community wireless networks. A good channel assignment is given as the one which can minimize interference of each link from its neighboring links and subsequently improve the network throughput. In response to it, we introduce an interference metric, namely, min-max i-value of an edge (MMIE), to explicitly accounts for interference among links that are at distance one. In addition, we further show that the link-weighted and distance-constrained channel assignment problem with respect to the interference metric is NP-hard in computation. This guides us to propose a new heuristic channel assignment algorithm called LD-CA algorithm. We study the performance of our algorithm by implementing it in a wireless simulation environment consisting of 25 nodes, each equipped with one 802.11 wireless card. The ns-2 simulation results show that in a multi-channel environment, the LD-CA algorithm significantly outperforms previously proposed channel assignment schemes by minimizing MMIE of all links.

关键词： Wireless mesh networks Interference Mesh networks Throughput Spread spectrum communication Laboratories Computational modeling Switches Conferences Computer network management

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Removal of Baseline Wander from Dynamic Electrocardiogram Signals

Removal of Baseline Wander from Dynamic Electrocardiogram Si...

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Congress on Image and Signal Processing, CISP

作者： Min Dai Shi-Liu Lian Tianjin Key Laboratory of Intelligent Computing & Novel Software Technology School of Computer and Communication Engineering Key Laboratory of Computer Vision and System Ministry of Education School of Computer and Communication Engineering Tianjin University of Technology Tianjin China

The traditional method based on moving average filter can remove the baseline wander in electrocardiogram signals, but also causes the loss of motive ECG signals, which makes distortions of filtered ECG signals. In this paper, a modified moving average filter is proposed to selectively capture the low-frequency baseline wander noise and remove it from the detected signals in order to recover true ECG. The interval sampling data is taken into consideration when calculate the moving average in order to reduce the loss of useful ECG signals and distortions. The algorithm is developed for computer implementation using MATLAB. To validate the proposed methods, the recording from MIT/BIH database is used. Experimental results demonstrate the improvement of the proposed moving average filter over the traditional one in removing baseline wander without introducing distortion to the ECG signals.

关键词： Electrocardiography Frequency Distortion Finite impulse response filter IIR filters Computer vision Clinical diagnosis Electrodes Adaptive filters Low-frequency noise

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