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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

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

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.

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A RIS-Assisted Multiuser MIMO Communication Method Based on Deep Reinforcement Learning

A RIS-Assisted Multiuser MIMO Communication Method Based on ...

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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Changlin Tian Hui Zhi Kui Tie Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Ceyear Technologies (AnHui) CO. LTD Bengbu China

In this paper, a RIS-assisted multiuser MIMO communication method based on deep reinforcement learning (RMMC-DRL) is proposed for multiuser scenarios. Our objective is to find the optimal transmit beamforming matrix of BS and optimal phase shift matrix of reflective intelligent surface (RIS) to maximize the sum rate of multiuser, this problem is reduced into a constrained optimization problem. It is a non-convex optimization problem, so we solve it through deep reinforcement learning (DRL) and then use the results for communication. In the DRL, a deep deterministic policy gradient (DDPG) framework that can handle continuous states and actions is designed, reward is set as optimization goal, and the transmit beamforming matrix and the phase shift matrix of RIS are obtained through the interaction with environment. Unlike the alternating optimization (AO) method, which solve the transmit beamforming matrix and the RIS phase shift matrix alternatively, the RMMC-DRL can obtain both transmit beamforming matrix and RIS phase shift matrix simultaneously as the output of DRL. Simulation results show that RMMC-DRL can learn and improve its behavior by interacting with the environment. Compared with AO method, RMMC-DRL can obtain higher sum rate and lower computational complexity.

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A 0.33–2.61-GHz Rectifier with Expanded Dynamic Input Power Range Using Microstrip Impedance Compression Circuit

TechRxiv

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TechRxiv 2023年

作者： Li, Yingsong Liu, Xin Zhang, Dawei Wu, Xiaohu Liu, Xiaoguang Huang, Zhixiang The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China Shenzhen518055 China

In this letter, an ultra-broadband rectifier with expanded dynamic input power range (IPR) for both wireless power transfer (WPT) and radio frequency energy-harvesting (RFEH) is proposed and analyzed. Expanded dynamic IPR and broadband impedance matching are realized by utilizing a microstrip transmission line impedance compression circuit (MTLICC) with the topology of the paralleled voltage doubler. A proof-of-concept prototype shows a dynamic IPR of 8.5–23.5 dBm, a frequency range of 0.33–2.61 GHz (fractional bandwidth of 155%), PCE is greater than 50%, and a peak PEC of 79.7% (achieved at an input-power level of 19 dBm). The measurement results are in good agreement with the simulation. Covering most 3G/4G/5G/ISM frequency bands, this design is promising for RF power transmission and energy harvesting in the age of the Internet of Things systems. © 2023, CC BY.

关键词： Timing circuits

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A Unified Affine-Projection-Like Adaptive Algorithm for System Identification

TechRxiv

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TechRxiv 2023年

作者： Li, Yingsong Fu, Yonglin Miao, Yongchun Huang, Zhixiang Diniz, Paulo S.R. The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China The Universidade Federal do Rio de Janeiro Rio de Janeiro21941-972 Brazil

A unified affine-projection-like adaptive (UAPLA) algorithm is deivised and verified for system identification. The UAPLA algorithm uses a generalized cost function encompassing some data-reusing methods to cope with colored input signals. Furthermore, the UAPLA algorithm is derived based on the new cost function to generalize multiple affine-projection algorithms. As a result, the proposed UAPLA algorithm includes some classical adaptive filters as exceptional cases while allowing flexibility to achieve low estimation errors under impulsive noises. The obtained results conducted by simulations help to corroborate the superiority of the proposed UAPLA algorithm over other popular AP algorithms. © 2023, CC BY.

关键词： Adaptive filters

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A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

arXiv

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arXiv 2019年

作者： Zhang, Dong Zhao, Shu Duan, Zhen Chen, Jie Zhang, Yanping Tang, Jie Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So, the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this paper, we propose a Multi-label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC. Firstly, we introduce a HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a Multi-label Classification (MLC) issue, whose multiple research labels exactly guide the learning process. It's flexible that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It's interesting that we also explore the paper-reviewer recom

关键词： Digital libraries

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Pyramid Convolution and Multi-Frequency Spatial Attention for Fine-Grained Visual Categorization

SSRN

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SSRN 2022年

作者： Xu, Qin Li, Yun Zhang, Mengquan Tao, Zhifu Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Big Data and Statistics Anhui University Hefei230601 China

Learning diverse detailed feature is crucial for fine-grained visual categorization (FGVC). However, most of existing methods for FGVC use the standard convolution for feature extraction which leads to the loss of many subtle but important features. Besides, in the existing attention models for FGVC, the features are aggregated by a simple global average pooling operation, which is unable to characterize complex feature information. To address these problems, we propose pyramid convolution and multi-frequency spatial attention (PCMFSA) for FGVC. To capture spatial context dependencies of different levels and avoid the loss of the subtle features, pyramid convolution, which uses convolution kernels with various sizes, is introduced in the backbone network. To reduce the influence of background noise and obtain diverse fine-grained feature information, we propose multi-frequency spatial attention (MFSA) module, in which two channel-wise grouping operations are designed and discrete cosine transform (DCT) in the frequency domain is adopted to enhance features of different spatial positions. Moreover, to mine more valuable regions, peak suppression is used to suppress high-response regions after MFSA. A large number of experimental results show that our method achieves state-of-the-art results on four popularly FGVC datasets. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Method for convergent projective non-negative matrix factorization

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Journal of Information and Computational Science 2013年第6期10卷 1847-1854页

作者： Hu, Lirui Wu, Jianguo Wang, Lei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Nantong University Nantong 226019 China School of Computer Science and Technology Anhui University Hefei 230039 China

In order to solve the problem of algorithm convergence in Projective Non-negative Matrix Factorization (P-NMF), a method, called Convergent Projective Non-negative Matrix Factorization (CP-NMF), is proposed. In CP-NMF, an objective function of Frobenius norm is defined. The Taylor series expansion and the Newton iteration formula of solving root are used. An iterative algorithm for basis matrix is derived, and a proof of algorithm convergence is provided. Experimental results show that the convergence speed of the algorithm is higher, however it is affected by the initial value of the basis matrix;relative to Non-negative Matrix Factorization (NMF), the orthogonality and the sparseness of the basis matrix are better, however the reconstructed results of data show that the basis matrix is still approximately orthogonal;in face recognition, there is higher recognition accuracy. The method for CP-NMF is effective. Copyright © 2013 Binary Information Press.

关键词： Face recognition

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A general construction and encoder implementation of polar codes

arXiv

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arXiv 2019年

作者： Song, Wei Shen, Yifei Li, Liping Niu, Kai Zhang, Chuan Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Universal Wireless Communication Ministry of Education Beijing Univer- sity of Posts and Telecommunications Beijing100876 China

Similar to existing codes, puncturing and shortening are two general ways to obtain an arbitrary code length and code rate for polar codes. When some of the coded bits are punctured or shortened, it is equivalent to a situation in which the underlying channels of the polar codes are different. Therefore, the quality of bit channels with puncturing or shortening differ from the original qualities, which can greatly affect the construction of polar codes. In this paper, a general construction of polar codes is studied in two aspects: 1) the theoretical foundation of the construction;and 2) the hardware implementation of polar codes encoders. In contrast to the original identical and independent binary-input, memoryless, symmetric (BMS) channels, these underlying BMS channels can be different. For binary erasure channel (BEC) channels, recursive equations can be employed assuming independent BMS channels. For all other channel types, the proposed general construction of polar codes is based on the existing Tal-Vardy's procedure. The symmetric property and the degradation relationship are shown to be preserved under the general setting, rendering the possibility of a modification of Tal-Vardy's procedure. Simulation results clearly show improved error performance with re-ordering using the proposed new procedures. In terms of hardware, a novel pruned folded encoder architecture is proposed which saves the computation for the beginning frozen bits. Implementation results show the pruned encoder achieve 28% throughput improvement. Copyright © 2019, The Authors. All rights reserved.

关键词： signal encoding

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Application and method of feature extraction for approximate orthogonal non-negative matrix factorization

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Journal of Computational Information Systems 2013年第2期9卷 805-812页

作者： Hu, Lirui Wu, Jianguo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Nantong University Nantong 226019 China School of Computer Science and Technology Anhui University Hefei 230039 China

In order to get effectively feature vectors of samples, a method of feature extraction is proposed based on non-negative matrix factorization (NMF) with approximate orthogonal constraint. An objective function is defined to impose approximate orthogonal constraint, in addition to the non-negative constraint in the standard NMF. An algorithm of iterative update for basis matrix and weight matrix in non-negative matrix factorization is derived, and then computational method of feature vectors is gotten. A proof of the convergence of the algorithm is provided. Empirical results of handwritten character recognition show that there is high recognition accuracy if only the rank of the basis matrix is set correctly. The method of feature extraction is useful to get essential feature of sample, and is effective to recognition when the structures of the handwritten characters are obviously different. Copyright © 2013 Binary Information Press.

关键词： Feature extraction

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An Efficient Authentication and key Agreement Scheme for CAV Internal Applications 19th

An Efficient Authentication and Key Agreement Scheme for C...

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19th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Li, Yang Zhang, Qingyang Cao, Wenwen Cui, Jie Zhong, Hong Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei230037 China School of Computer Science and Technology Anhui University Hefei230039 China Anhui Engineering Laboratory of IoT Security Technologies Anhui University Hefei230039 China Institute of Physical Science and Information Technology Anhui University Hefei230039 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China

ISBN: (纸本)9783031545276

The data of applications in connected and autonomous vehicles are important, which is usually collected by service providers to improve their services, such as object detection model. But, wireless communication is susceptible to various kinds of attacks. Thus, the data of the application module needs to be securely shared to the corresponding service provider. However, current schemes are with limited performance while a service provider collects multiple application data at the same time. By adopting signcryption and chaotic map, an efficient authentication and key agreement scheme is proposed, while batch authentication is achieved for efficient message authentication of multiple applications, and the efficient revocation is realized based on Chinese remainder theorem under the assistance of trusted execution environment supported vehicle computing/communication unit. The formal security proof shows that the scheme is secure under the random oracle model, and the experiment results shows that the scheme is more efficient than related schemes and can meet the requirements of CAV. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Authentication

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