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Efficient polar coding scheme and implementation with shared information bits

Science China(Information Sciences) 2022年第10期65卷 309-310页

作者： Wenyue ZHOU Xiaofeng ZHOU Yifei SHEN Qiang LIU Liping LI Chuan ZHANG Laboratory of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education of China Anhui University

Dear editor,Polar codes invented by Ar?kan [1] can achieve the capacity of symmetric binary-input discrete memoryless channels(B-DMCs) with an infinite code length. However, it suffers from unsatisfactory performance with a finite code length [2]. The performance degradation of polar codes with a finite code length is caused by the inadequate polarization phenomenon;

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Remaining Useful Life Prediction of Bearing via a Double Attention-Based Deep Neural Network 19

Remaining Useful Life Prediction of Bearing via a Double Att...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yongkang Pan, Donghui Zhang, Haifeng Zhong, Kai Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui Hefei230601 China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China

ISBN: (纸本)9798350346558

Predicting the remaining useful life (RUL) of bearings is important for the secure running and reducing maintenance costs of rotating machinery. In this article, a novel feature extraction and RUL prediction framework of bearing is proposed by utilizing time-frequency representations (TFRs) and deep neural network with double attention (DA-DNN). Firstly, TFRs are employed to extract the degradation features associated with RUL prediction, which can effectively cope with the non-stationary properties of bearing vibration data. The reduced dimensional TFRs are then used as the DA-DNN model input. Afterwards, DA-DNN is developed by employing the double attention structure to extract the key degradation features more efficiently. The dual-attention structure has ability to capture the degradationrelated features from two aspects of channel attention and spatial attention, which enables the network to focus on high RUL-relevant information. Finally, the experimental results demonstrate the effectiveness of DA-DNN, which shows higher performance in terms of prediction accuracy compared to other deep learning (DL) methods. © 2022 IEEE.

关键词： Deep neural networks

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Super-Broadband Rectifier with Expanded Input Power Range for Microwave Power Transmission 5

Super-Broadband Rectifier with Expanded Input Power Range fo...

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

作者： Chen, Yan Liu, Xin Li, Yingsong Huang, Zhixiang The Fifth Research Institute of Miit Guangzhou511370 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A super-broadband rectifier with expended input power range(IPR) for wireless power transfer (WPT) and radio frequency energy harvesting (RFEH) is proposed and analyzed. Meanwhile, using a simple impedance compression network consisting of quadruple microstrip lines (QMLICN) with the topology of the dickson charge pump, high power conversion efficiency (PCE) and super-broadband impedance matching are achieved. In addition, the IPRs of the rectifier are also extended. The simulation results indicate that an IPR of 12 - 25 dBm with a frequency range of 0.4 - 2.5 GHz (fractional bandwidth over 144%), the PEC exceeds 50% are realized, while at an input power of 19 dBm, the rectifier reaches a peak PCE of 74.2%. The rectifier covers the GSM 900, GSM 1800, 3G 2100 and WIFI 2400 bands, which design would have significant potential applications for the development of microwave power transmission systems. © 2022 IEEE.

关键词： Energy transfer

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FastICA based single-channel radar signal separation algorithm

FastICA based single-channel radar signal separation algorit...

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IET International Radar Conference (IRC 2023)

作者： Xinyu Wang Jin Zhao Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei People's Republic of China

ISBN: (数字)9781837240982

Aiming at the traditional blind source separation for the received single-channel mixed radar signal makes it difficult to accurately recover the source signal, a single-channel radar signal comprehensive separation method based on the combination of the FastICA algorithm and the variational modal decomposition (VMD) algorithm is proposed. The method utilizes VMD to first perform modal separation of single-channel mixed Linear Frequency Modulation (LFM) signal, then selects the modal component signal with the largest correlation coefficient with the mixed signal and expands it with single-channel mixed LFM signal into a virtual multi-channel mixed signal, and finally inputs it into FastICA algorithm to obtain the source signal. The simulation experiment results show that the comprehensive separation algorithm can accurately separate the LFM signal, overcoming the limitation that the FastICA algorithm cannot directly separate the single-channel mixed radar signal.

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Optimized Statistical Model for Complex Chemical Processes Monitoring

Optimized Statistical Model for Complex Chemical Processes M...

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Data Driven Control and Learning Systems (DDCLS)

作者： Jian Zhang Xiaohe Qu Zhiwei Lei Haifeng Zhang Kai Zhong Datang East China Electric Power Test and Research Institute Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei Anhui China

The traditional kernel principal components analysis (KPCA) and linear discriminant analysis (LDA) have been verified to be two effective approaches for fault detection and diagnosis in recent years. Nevertheless, the conventional method and corresponding improved ones still exposed their deficiencies in some ways. Facing this dilemma, this paper presents a combination of optimized KPCA and modified LDA (OKPCA-MLDA), in which the OKPCA avoids the loss of original features after centralizing data in the eigenspace by adjusting covariance matrix's eigenvalue and transforming the distribution of variables thus providing representative and abundant principal components for the classifier. In addition, the MLDA maximizes a brand new objective function in feature space which achieves better classification performance than the conventional LDA, then utilizing diagnostic thresholds and similarity coefficients to identify the fault types. Based on the combined model, not only the fault detection and diagnosis can be realized simultaneously but also the accuracy of detection and diagnosis can be guaranteed. Furthermore, the simulation experiments on Tennessee Eastman (TE) benchmark process clearly illustrated the superiority of our proposed strategy.

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ZTrans: A new Transformer for Speech Enhancement 4

ZTrans: A new Transformer for Speech Enhancement

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4th International Conference on Information Communication and signal processing, ICICSP 2021

作者： Guo, Yucheng Yu, Weiwei Zhou, Jian Key Laboratory of Intelligent Computing and Signal Processing Anhui University Anhui University HeFei China

ISBN: (纸本)9781665407571

At present, Transformer architectures based on the self-attention mechanism have attracted wide attention due to their excellent performance in many fields, such as natural language processing and computer vision. However, in the field of speech processing, the potential of Transformer architectures has not been fully utilized. The main reason is that the position embedding mechanism in the Transformer cannot effectively perform sequence modeling in speech tasks, and the Transformer itself is challenging to train and converge. Inspired by recent research, we propose a new speech enhancement in this paper. Specifically, a recurrent neural network is used to replace the position embedding in Transformer, and the fully connected layer in the Transformer feed forward layer is replaced with one-dimensional convolution. In addition, the model also introduces a weighted residual connection mechanism. Experiment results show that compared with baseline models, the new model performs better in speech quality and speech intelligibility. At the same time, the convergence and inference speed are also much improved. Compared with the baseline model, the processing speed of the proposed method is increased by 4.27 times. © 2021 IEEE.

关键词： Recurrent neural networks

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A Through-hole Antenna Array for Joint Optical and Millimeter Sensing Applications

A Through-hole Antenna Array for Joint Optical and Millimete...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Sixian Qian Lulu Meng Zhixiang Huang Yingsong Li Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Electromagnetic & Electrochemical Technologies Department National Physical Laboratory Middlesex United Kingdom

A novel millimeter wave sensing antenna array is proposed for application in miniature detection and sensing devices suitable for operating in multiple scenarios, e.g., collecting in the face of video and infrared, and other device materials in complex electromagnetic environments to achieve delightful sensing results. The proposed antenna array is designed to work at 30 GHz which is considered to integrate the optical devices and millimeter radar together by using a number of holes in the millimeter antenna array. Two different excitation modes are considered in the constructed design, where many holes are installed in the millimeter array, which aims to integrate the optical imaging cameras. In this design, the presented array serves as a radar to detect and get sensing results. Since the optical components are not metal and have a little effect on the performance of the antenna array, we use the holes to model the optical cameras to investigate the performance of the radiation patterns and impedance bandwidth.

关键词： Millimeter wave devices Laser radar Radar imaging Radar antennas Optical imaging Cameras Optical sensors

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A Spectrum Based Algorithm for Image Classification

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Chinese Journal of Electronics 2023年第3期18卷 427-430页

作者： Nian Wang Jiang Zhang Jun Tang Yizheng Fan Dong Liang Key Laboratory of Intelligent Computing and Signal Processing Anhui University Education Ministry Hefei China Hefei Teachers College Hefei China Department of Mathematics Anhui University Hefei China

In this paper, a novel algorithm for image classification is presented which uses the projective value of adjacency spectrum as classified samples. Firstly, the eigenvalues of adjacency matrices constructed on the feature point-sets of images are obtained by singular value decomposition. Secondly, the eigenvalues are projected onto the eigenspace by means of the covariance matrix. Finally, image classification is performed by adopting RBF and PNN neural networks as classifiers respectively. Mean-while, some theoretical analyses are given to support the proposed method.

关键词： Neural networks Education Eigenvalues and eigenfunctions Classification algorithms Matrix decomposition Covariance matrices Image classification

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A Unified Stream and Batch Graph computing Model for Community Detection 17th

A Unified Stream and Batch Graph Computing Model for Commu...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Dai, Jinkun Wu, Ling Guo, Kun Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

An essential challenge in graph data analysis and mining is to simply and effectively deal with large-scale network data that is expanding dynamically. Although batch-based parallel graph computation frameworks have better accuracy, they cannot process incremental data on time and need to be recomputed. To process real-time data, stream processing applications need to be redeveloped, which increases the redundancy of work, and some existing dynamic graph computation schemes are not generalizable. This paper proposes a unified stream and batch graph computing model(USBGM). The model is compatible with both stream and batch graph computing. Graph operators and algorithms developed based on the model can handle stream and batch graph data in a unified manner. The experiments on real-world and artificial networks verified the effectiveness and efficiency of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Systematic review of digital twin technology and applications

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Visual computing for Industry,Biomedicine,and Art 2023年第1期6卷 119-138页

作者： Jun-Feng Yao Yong Yang Xue-Cheng Wang Xiao-Peng Zhang Center for Digital Media Computing School of FilmXiamen UniversityXiamen 361005China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and TourismXiamen 361005China State Key Laboratory of Multimodal Artificial Intelligence Systems the Institute of AutomationChinese Academy of SciencesBeijing 101408China

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application *** phenomenon has been widely studied in academia and *** this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are *** internal association between DT and related technologies is *** four stages of DT development history are *** fundamentals of the technology,evaluation indexes,and model frameworks are ***,a conceptual ternary model of DT based on time,space,and logic is *** technology and application status of typical DT systems are ***,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

关键词： Digital twin Digitalization Virtual modeling Information technology Simulation

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