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SSRN 2023年

作者： Feng, Naixing Yin, Jianxiang Wang, Huan Wang, Xuan Zhang, Yuxian Huang, Zhixiang Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China

As one kind of anisotropic two-dimensional (2-D) layered material, the monolayer black phosphorus (BP) has ability to excite the surface plasmon resonance in the infrared spectrum, thus providing sufficient conditions for the strong light-matter interaction, so that some potential applications can be shown in the field of optoelectronics. Based on those physical properties, we design the patterned monolayer BP to achieve extremely high infrared absorption, and meanwhile use the finite element method to model the effects of factors such as n-type doping concentration, dielectric thickness, and angle of incidence light. Moreover, due to the strong anisotropic in-plane properties of monolayer BP, we analyze detailedly the absorption spectra in both the armchair and zigzag directions separately, and then understand that low-angle narrow-band absorption in the zigzag direction could be captured in that structure. The structure could achieve perfect absorption in the zigzag direction at an incidence angle of 89.3° with n-type doping concentration n=3×1013 cm-2 and medium thickness d = 4.6 μm, and the maximum absorption can reach 99.98% with the full width at half peak (FWHM) of only 0.57 μm. Considering the case on surface oxidation, it may occur some possible effects for the perfect absorption, but with our structure, the proposed narrow-band absorption still has wide applications in the sensing and light body monitoring. © 2023, The Authors. All rights reserved.

关键词： Monolayers

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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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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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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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Self-Dual Hadamard Bent Sequences

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Journal of Systems Science & Complexity 2023年第2期36卷 894-908页

作者： SHI Minjia LI Yaya CHENG Wei CRNKOVIC Dean KROTOV Denis SOLéPatrick Key Laboratory of Intelligent Computing Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security(Institute of Information Engineering) Chinese Academy of SciencesBeijing 100093China. LTCI Télécom ParisParis91120 PalaiseauFrance Secure-IC S.A.S. 104 Bd du Montparnasse75014 Paris Faculty of Mathematics University of RijekaNovosibirsk 630090Russia Sobolev Institute of Mathematics Novosibirsk 630090Russia Aix Marseille Univ CNRSCentrale MarseilleI2MMarseilleFrance

A new notion of bent sequence related to Hadamard matrices was introduced recently,motivated by a security application(Solé,et al.,2021).The authors study the self-dual class in length at most *** authors use three competing methods of generation:Exhaustion,Linear Algebra and Gr?bner *** Hadamard matrices and Bush-type Hadamard matrices provide many *** authors conjecture that if v is an even perfect square,a self-dual bent sequence of length v always *** authors introduce the strong automorphism group of Hadamard matrices,which acts on their associated self-dual bent *** authors give an efficient algorithm to compute that group.

关键词： Bent sequences bush-type Hadamard matrices Hadanard matrices PUF functions regular Hadamard matrices

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Dual-Control Reconfigurable intelligent Metasurface: Design and Realization

Dual-Control Reconfigurable Intelligent Metasurface: Design ...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Kangtai Zheng Kwok L. Chung Yuzhong Li Guoming Lai Jianhai Wang Yingsong Li School of Computer Science and Engineering Huizhou University Huizhou China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A dual-control reconfigurable intelligent metasurface (DC-RIMS) composed of $24\times 22$ symmetric cells in the 5G mid-band is proposed. The DC-RIMS of non-artificial-magnetic-conductor type provides a smaller phase resolution of $60^{\mathrm{o}}$ with 2 bits under normal incidence. Angular sensitivities of the RIMS under oblique incidence are investigated. Our results show that the reflection responses at elevation angles are more sensitive than the azimuth angles, whereas the maximum phase differences are differed from each polarization. The horizontal one increases to $220^{\mathrm{o}}$ whereas the vertical one reduces to $150^\mathrm{o}$ as compared with overlapped $180^{\mathrm{o}}$ under normal incidence.

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Machine learning prediction of network dynamics with privacy protection

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Physical Review Research 2022年第4期4卷 043076-043076页

作者： Xin Xia Yansen Su Linyuan Lü Xingyi Zhang Ying-Cheng Lai Hai-Feng Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes, and the client has access to only partial time stamps of the whole set of time series data and the partial structure of the network. Due to privacy concerns or license-related issues, the data collected by different clients cannot be shared. Accurately predicting the network dynamics while protecting the privacy of different parties is a critical problem in modern times. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of state-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy.

关键词： Collective behavior in networks

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Pctlfusion: A Progressive Fusion Network Via Contextual Texture Learning for Infrared and Visible Image

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SSRN 2024年

作者： Lu, Yixiang Gong, Daiwei Zhao, Dawei Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Belarusian State University of Informatics and Radioelectronics Minsk Belarus

The objective of infrared and visible image fusion is to generate a fused image that contains rich texture details and salient targets. However, most of the existing fusion methods tend to focus on preserving texture details in visible image and salient targets in infrared image, ignoring the fact that infrared image have richer texture details and better visual effects than visible image under low-light conditions. In other words, differences in information sources under the condition of illumination imbalance is ignored in setting the task goal. In addition, existing network designs usually ignore multimodal information interactions during the coding phase, which may result in loss of information. In this work, we propose a progressive fusion network via contextual texture learning for infrared and visible image, termed as PCTLFusion. To enhance the limited texture information, we design a content and contextual texture learning module (CCTLM) to model the texture and content of both the source images, respectively. Then, an affine transform-based multimodal pre-fusion module (ATMPM) is designed to learn and pre-fuse the complementary and similar enhanced information, which achieves information interaction during encoding. To learn fully feature information using multilayers, we iterate CCTLM and ATMPM three times in the process of model forming. Finally, the fused image is reconstructed by decoding the output of the last ATMPM. Numerous experiments show that our proposed fusion algorithm is superior to the state-of-the-art existing methods in both subjective and objective evaluations. © 2024, The Authors. All rights reserved.

关键词： Textures

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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Constrained Squared Sine Derived Adaptive Algorithm: Performance and Analysis

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SSRN 2022年

作者： Li, Yingsong Chen, Yong Li, Liping Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China

A constrained squared sine derived adaptive (CSSDA) algorithm is proposed in this paper, which provides better steady-state behavior than existing algorithms in impulsive-noise environments. The devised CSSDA is constructed via constructing a constrained cost function on the basis of squared sine functioned system estimate error. Theoretical analysis of the CSSDA is presented and compared with the simulations. The simulation results show that the theoretical analysis agrees well with simulations, helping to verify the effectiveness and correctness of the analysises. Also, the performance of the CSSDA is superior to the recent popular constraint adaptive algorithms for system identifications in a variety of environments with non-Gaussian impulsive-noises. © 2022, The Authors. All rights reserved.

关键词： Adaptive filtering

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