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Asia communications and Photonics Conference and Exhibition (ACP)

作者： Lingxin Meng Pan Dai Qilu Ban Kaichuan Xu Jiacheng Wang Zhen Li Feng Wang Jie Zeng Shaobo Li Xiang Ma Xiangfei Chen Key Laboratory of Intelligent Optical Sensing and Manipulation of the Ministry of Education & Engineering National Laboratory of Solid State Microstructures Research Center of Precision Photonics Integration and System Application of the Ministry of Education College of Engineering and Applied Sciences Nanjing University Nanjing China State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China Optical Communication Research and Development Center the 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

We present a four-channel ultrafast wavelength-swept distributed feedback (DFB) semiconductor laser array based on the reconstruction-equivalent-chirp (REC) technique using instantaneous injection current modulation. With the aid of our designed laser driving circuit system consists of the microcontroller unit (MCU), field-programmable gate array (FPGA) and negative metal-oxide-semiconductor field effect transistors (n-MOSFETs), the precise pulse driving current for the 4-channel wavelength-swept REC-DFB laser array can be generated and a fast tuning of 16.424 nm within 10 μs is achieved. To further investigate the tuning performance of the proposed wavelength-swept laser source, we then applied it to the fiber Bragg grating (FBG) interrogation system and four FBG temperature sensor arrays were successfully demodulated. The development of high-efficient optical sensing systems heavily relies on the significance of the ultrafast wavelength-swept laser source, which has the potential to greatly enhance FBG interrogator performance in the future.

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A Combined Alignment Method for Strapdown Inertial Navigation System on Stationary Base

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Journal of Harbin Institute of technology(New Series) 2020年第6期27卷 57-71页

作者： Shuai Chen Xinzhi Liu Qian Sun Ya Zhang School of Information and Control Engineering Liaoning Shihua UniversityFushun 113001LiaoningChina Department of Applied Mathematics University of WaterlooWaterloo N2L3G1Canada College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Electrical Engineering&Automation Harbin Institute of TechnologyHarbin 150001China

Owing to the weak observability of the azimuth misalignment angle,alignment accuracy and time are always the contradictory issues in the initial alignment process of Strapdown Inertial Navigation System(SINS),which requires a compromise between *** this paper,a combined alignment mechanism is proposed to construct an observable and controllable system model,which can effectively achieve higher azimuth alignment accuracy during the fixed time ***,the Reduced Order Kalman Filter(ROKF)alignment algorithm was utilized to calculate the misalignment angles in parallel with the classical gyrocompass alignment ***,the misalignment angles calculated by the gyrocompass alignment method were used to formulate the augmented measurement model with zero velocity ***,the zero velocity model of the ROKF method was switched into the augmented measurement model when the azimuth misalignment angle of the gyrocompass alignment method was close to steady *** combined alignment method was analyzed reasonably by the observability and the mathematical *** comparison results of the numerical simulation and the experimental data test showed that the combined method had good performance in terms of estimation accuracy and consistency of the alignment results.

关键词： inertial navigation instrumentation and measurement gyroscopes accelerometers Kalman filter measurement errors

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Review of X-ray pulsar spacecraft autonomous navigation

arXiv

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arXiv 2023年

作者： Wang, Yidi Zheng, Wei Zhang, Shuangnan Ge, Minyu Li, Liansheng Jiang, Kun Chen, Xiaoqian Zhang, Xiang Zheng, Shijie Lu, Fangjun College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Beijing Institute of Control Engineering Beijing100080 China Beijing Institute of Tracking and Communication Technology Beijing100091 China Aerospace Information Research Institute Chinese Academy of Science Beijing100091 China Academy of Military Science Beijing100091 China National Innovation Institute of Defense Technology Academy of Military Science Beijing100091 China

This article provides a review on X-ray pulsar-based navigation (XNAV). The review starts with the basic concept of XNAV, and briefly introduces the past, present and future projects concerning XNAV. This paper focuses on the advances of the key techniques supporting XNAV, including the navigation pulsar database, the X-ray detection system, and the pulse time of arrival estimation. Moreover, the methods to improve the estimation performance of XNAV are reviewed. Finally, some remarks on the future development of XNAV are provided. © 2023, CC BY-NC-ND.

关键词： Navigation

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A two-mode offspring generation selection mechanism with co-evolution for sparse large-scale multiobjective optimization

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Information Sciences 2025年 718卷

作者： Xiaodong Huang Jian Wang Gaige Wang Yong Zhang Dunwei Gong Yaochu Jin Nikhil R. Pal College of Control Science and Engineering China University of Petroleum (East China) Qingdao 266580 China College of Science China University of Petroleum (East China) Qingdao 266580 China School of Computer Science and Technology Ocean University of China Qingdao Shandong 266580 China School of Information and Control Engineering China University of Mining and Technology Xuzhou Jiangsu 221000 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong 266061 China College of Engineering Westlake University Hangzhou Zhejiang 310024 China Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

Sparse large-scale multiobjective optimization problems (LSMOPs) have a wide range of practical applications. In recent years, numerous multiobjective evolutionary algorithms (MOEAs) have been developed to address the complexities of these problems. However, many existing MOEAs designed to solve sparse LSMOPs typically rely on fixed, experience-based vectors to guide offspring generation, which often makes it challenging to determine the optimal guiding vectors for different population states, leading to premature convergence and loss of population diversity. To some extent, this leads to a subjective selection of the vector used for offspring generation. To address this issue, we propose a two-mode offspring generation selection mechanism (TOGSM) that incorporates diversified sparse knowledge into the offspring generation process. The switching between these two modes is based on a designed offspring performance indicator. We also divide the population into two subpopulations by employing techniques of Pareto dominance relationship and fitness values. In each generation, the loser subpopulation generates offspring solutions during the reproduction process, under the guidance of the winner subpopulation. Experimental results confirm that TOGSM incorporating two-mode mechanism and co-evolution strategy can generate higher quality Pareto optimal solutions with faster convergence speed than the state-of-the-art (SOTA) comparative algorithms.

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Exploiting Active RIS in NOMA Networks with Hardware Impairments

arXiv

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arXiv 2023年

作者： Yue, Xinwei Song, Meiqi Ouyang, Chongjun Liu, Yuanwei Li, Tian Hou, Tianwei The Key Laboratory of Information and Communication Systems Ministry of Information Industry China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Electrical and Electronic Engineering University College Dublin DublinD04 V1W8 Ireland The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The 54th Research Institute of China Electronics Technology Group Corporation Hebei Shijiazhuang050081 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Erlangen91054 Germany

Active reconfigurable intelligent surface (ARIS) is a promising way to compensate for multiplicative fading attenuation by amplifying and reflecting event signals to selected users. This paper investigates the performance of ARIS assisted non-orthogonal multiple access (NOMA) networks over cascaded Nakagami-m fading channels. The effects of hardware impairments (HIS) and reflection coefficients on ARIS-NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) are considered. More specifically, we develop new precise and asymptotic expressions of outage probability and ergodic data rate with ipSIC/pSIC for ARIS-NOMA-HIS networks. According to the approximated analyses, the diversity orders and multiplexing gains for couple of non-orthogonal users are attained in detail. Additionally, the energy efficiency of ARIS-NOMA-HIS networks is surveyed in delay-limited and delay-tolerant transmission schemes. The simulation findings are presented to demonstrate that: i) The outage behaviors and ergodic data rates of ARIS-NOMA-HIS networks precede that of ARIS aided orthogonal multiple access (OMA) and passive reconfigurable intelligent surface (PRIS) aided OMA;ii) As the reflection coefficient of ARIS increases, ARIS-NOMA-HIS networks have the ability to provide the strengthened outage performance;and iii) ARIS-NOMA-HIS networks are more energy efficient than ARIS/PRIS-OMA networks and conventional cooperative schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Feature Explainable Deep Classification for Signal Modulation Recognition

Feature Explainable Deep Classification for Signal Modulatio...

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Annual Conference of Industrial Electronics Society

作者： Jinyin Chen Shenghuan Miao Haibin Zheng Shilian Zheng College of Information Engineering Zhejiang University of Technology Hangzhou China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

Signal modulation recognition plays a critical role in many fields to identify the modulation type of wireless signals. Since the deep learning based models have achieved great success in classification tasks, more deep neural networks are proposed for signal modulation recognition. In this paper, we explore the use of different deep neural networks in both macro network architecture level and micro cell size and layer level to compare and understand their effect on classification performance. We also bring up a feature explainable deep neural network by visualizing the critical features in the deep neural models. We visually show and compare the commonality and differences of hidden layer characteristics extracted by different network structure to explain and analyze the reason why some models can achieve better classification results than the others. We believe it is an effective way to explain how deep neural model based signal classification work. Thus the explanation will help users establish appropriate understand and trust in predictions from deep modulation recognition networks.

关键词： Feature extraction Modulation Computer architecture Computational modeling Visualization Convolution Signal to noise ratio

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Omnidirectional and Broadband Metamaterial Absorber Using Multilayer Graphene Ribbons 5

Omnidirectional and Broadband Metamaterial Absorber Using Mu...

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5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Cai, Yijun Hang, Yanfen Cao, Le Zhou, Yuanguo School of Opto-electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China College of Communication and Information Engineering Xi'An University of Science and Technology Xi'an710054 China College of Electric and Control Engineering Xi'An University of Science and Technology Xi'an710054 China

ISBN: (纸本)9781538671115

A broadband metamaterial absorber is proposed utilizing multilayer graphene ribbons sandwiched between dielectric layers. The absorption spectrum can be further widen by stacking graphene ribbons of different widths layer by layer. Moreover, the graphene-based absorber can tolerate a wide range of incident angles. Our study can be applied in the systems of sensing, communication and imaging. © 2019 IEEE.

关键词： Graphene

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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

arXiv

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arXiv 2022年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Shenzhen518066 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Utron Technology Company Ltd. Hangzhou310056 China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Due to the decentralized and public nature of the Blockchain ecosystem, the malicious activities on the Ethereum platform impose immeasurable losses for the users. Existing phishing scam detection methods mostly rely only on the analysis of original transaction networks, which is difficult to dig deeply into the transaction patterns hidden in the network structure of transaction interaction. In this paper, we propose a Transaction SubGraph Network (TSGN) based phishing accounts identification framework for Ethereum. We first extract transaction subgraphs for target accounts and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. In order to make our model incorporate more important information about real transactions, we encode the transaction attributes into the modeling process of TSGNs, yielding two variants of TSGN, i.e., Directed-TSGN and Temporal-TSGN, which can be applied to the different attributed networks. Especially, by introducing TSGN into multi-edge transaction networks, the Multiple-TSGN model proposed is able to preserve the temporal transaction flow information and capture the significant topological pattern of phishing scams, while reducing the time complexity of modeling large-scale networks. Extensive experimental results show that TSGN models can provide more potential information to improve the performance of phishing detection by incorporating graph representation learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Real-time watermark reconstruction for the identification of source information based on deep neural network

Real-time watermark reconstruction for the identification of...

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作者： Sinhal, Rishi Ansari, Irshad Ahmad Jain, Deepak Kumar Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing JabalpurMP482005 India Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

A novel deep neural network-based image watermarking method is presented to identify the source of digital data that is shared/forwarded on the internet using various messenger apps. The app that is used to share/communicate the image at the very first time is also identified in the proposed method. The ten-digit mobile number of the source (user) and identification data of particular messenger app (i.e. WhatsApp, Snapchat, Kik, Facebook messenger, etc.) is combined to get the text watermark signal. The part of the watermarksignal representing specific mobile-based messenger application is obtained by randomizing the Walsh orthogonal codes using secret keys. To embed the watermark, the host image (shared/forwarded) is divided into blocks of equal size and then, slantlet transform is applied on each block. To get high reliability, three copies of the source information (user and app) are embedded during watermark embedding. Watermark extraction is performed using trained multilayer deep neural network. Furthermore, an optimal block selection logic is used to get improved results for real-time applications. The method is examined against various signal-processing attacks and high robustness with significant imperceptibility is attained. The method is also found to be fast enough for real-time applications. The prime objective of identifying the first user (source) and the shared/forwarded status (app detection) is successfully accomplished. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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