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Improving the Covertness in the Physical-Layer Authentication

China Communications 2021年第3期18卷 122-131页

作者： Ning Xie TianXing Hu College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security(Shenzhen University) Shenzhen 518060China

A well-designed Physical-Layer Authentication(PLA)scheme should consider three properties:covertness,robustness,and ***,the three properties always cause some dilemmas,e.g.,higher covertness leading to lower *** paper concerns the problem of improving the covertness without sacrificing the *** problem is important because of the following reasons:reducing the errors in recovered source message,improving the security,and ease of constructing a multi-factor authentication *** this paper,we propose three covert PLA schemes to address the *** the first scheme,we improve the covertness by reducing the modification ratio on the source message based on an encoding *** the second scheme,we improve the covertness by optimizing the superimposing angle,which maximizes the minimum distance between the tagged symbols and the boundary line of the demodulation decision for the source *** the third scheme,referred to as the hybrid scheme,we further improve the covertness by jointly using the advantages of both the above two *** experimental results show that when the SNR at a legitimate receiver is 25 dB,as compared with the prior scheme,the first scheme improves the covertness by 17:74%,the second scheme improves the covertness by 28:79%,and the third scheme improves the covertness by 32:09%,while they have similar robustness as Received:Aug.07,2020 Revised:Sep.08,2020 Editor:Nanrun Zhou that of the prior scheme.

关键词： physical-layer authentication encoding covertness superimposing angle security

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

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3rd International Conference on Bioinformatics and intelligent Computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior information of information Matrix

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Journal of Shanghai Jiaotong university(Science) 2022年第5期27卷 671-679页

作者： HUANG Yinghao WU Yi YAO Lixiu CAI Yunze Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai200240China Alibaba(China)Co. Ltd.Hangzhou311121China

The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking *** adaptive grid-expected-mode augmentation variable structure multiple model algorithm is *** adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model ***,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is *** the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.

关键词： radar network system variable structure multiple model consistency filtering information matrix particle swarm optimization

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image 16

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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16th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Xie, Shuo Ma, Yingjie Xu, Wei Qiu, Song Sun, Yifei East China Normal University School of Communication and Electronic Engineering China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350330755

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets. © 2023 IEEE.

关键词： Image enhancement

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Smoke Detection In Complex Scenery using Improved YOLOV5 8

Smoke Detection In Complex Scenery using Improved YOLOV5

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8th International Conference on intelligent Computing and Signal processing, ICSP 2023

作者： Nie, ZiJun Hu, YongQi Yu, ZiZheng Li, RuJia Yin, ZhiJian Zhan, HuaLin Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9798350302455

Considering smoke detection in a complex scene, high smoke object error rate and low detection efficiency, an improved YOLOV5 for smoke detection is proposed in this paper. To improve the effectiveness of smoke detection algorithm for smoke targets in complex scenes. First, a new lightweight convolution technique GSConv is introduced to replace the Conv layer in the neck layer for feature extraction, and the original C3 module is replaced by Cross Stage Partial Network (GSCSP) module VoVGSCSP. This improvement reduces the computation complexity of the algorithm while maintaining the detection accuracy. Second, CIoU is replaced by SIoU as the regression function of the prediction box to improve the prediction accuracy of the prediction box and reduce the rate of missed detection of the target. Finally, to improve the performance of the deep network, Dynamic ReLU is introduced as an activation function (dynamically adjusting the ReLU parameters depending on the input data). Experiments on the SM-dataset (smoke detection dataset) show that the improved model improves the accuracy and precision of target smoke detection compared to the YOLOV5 model. Its precision is increased by 1.8%, mAP@0.5 is increased by 1.0%, the complexity of the model is reduced by 23.8%. The improved algorithm proposed in this paper can effectively extract smoke features and more suitable for smoke detection tasks in complex scenes. © 2023 IEEE.

关键词： Smoke

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Stability Analysis of Load Frequency control System with Time-Varying Delays

Stability Analysis of Load Frequency Control System with Tim...

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2023 China Automation Congress, CAC 2023

作者： Fan, Chunling Chen, Chaoyang Zhou, Shaowu Lian, Honghai Chen, Zuguo Li, Pei School of Information and Electrical Engineering Hunan Provincial Key Laboratory of Intelligent Control and Maintenance of Complex Systems Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350303759

In this paper, the stability analysis of Load frequency control (LFC) systems with time-varying delay is conducted. Firstly, an augmented Lyapunov-Krasovskii (L-K) functional is designed to incorporate the relevant information of the delay state. Then, the functional derivatives are estimated using an auxiliary-function-based integral inequality in conjunction with an improved reciprocally convex inequality. Furthermore, the correlation function is processed through a quadratic function negative definite derivation. Finally, case studies established on a one-area LFC system are conducted to demonstrate the superiority of the method, and its accuracy is verified through a simulation case. © 2023 IEEE.

关键词： Electric control equipment

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Augmented Reality Enabled Telepresence Framework for Intuitive Robotic Teleoperation

Augmented Reality Enabled Telepresence Framework for Intuiti...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xu, Zheng Lin, Zecai Liu, Huanghua Ren, Qingsong Gao, Anzhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China

ISBN: (纸本)9798350325706

In terms of telepresence for robotic teleoperation, the existing remote surgery provides the patient's side real-time videos to remote operators. Its effectiveness is limited due to the lack of depth information, occlusion, and restricted viewing angles. To address it, this paper develops a robotic telepresence framework integrating augmented reality (AR) and 3D reconstruction technology, providing a 3D virtual patient's side environment for remote operators. The real scene of the patient's side is reconstructed by a monocular camera with a manipulator before the operation, and the manipulator is teleoperated by the operator at the remote's side. The manipulator's pose is fused into the classic SVO+REMODE algorithm to improve the reconstructed accuracy. On the remote's side, an AR head-mounted display (HMD) is deployed to provide telepresence for operators under a similar environment, and the reconstructed scene of the patient's side is registered to the remote room, from which the operator can see the whole scene of the patient body and surrounding objects. An ArUco cube is used to evaluate the superimposed error between virtual and real displays, and the effectiveness is validated in an experimental platform for remote surgery. © 2023 IEEE.

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Pedestrian object detection algorithm based on lightweight YOLOv7 in complex street scenarios

Pedestrian object detection algorithm based on lightweight Y...

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2023 International Conference on Algorithm, Imaging processing, and Machine Vision, AIPMV 2023

作者： Cheng, Shangqi Niu, Hongxia Key Laboratory of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou730070 China School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510672444

In view of the problems of excessive parameter setting and large calculation of YOLOv7 in pedestrian object detection in complex street scenarios, this paper proposes a lightweight method to improve YOLOv7 algorithm. Under the YOLOv7 framework, Partial Convolution (PConv) is integrated into the convolution of the original algorithm, replacing part of the convolution in the original convolution layer, and the SEAttention attention module is introduced to ensure the detection accuracy of the lightweight algorithm. The experimental results on the home-made data set show that, compared with the original YOLOv7 algorithm, the number of model parameters decreased by 11.0% in the improved YOLOv7 algorithm, and the algorithm calculation volume decreased by 19.4%, while ensuring the high accuracy of the original YOLOv7 algorithm. In this paper, the algorithm reduces the number of parameters and calculations, and achieves the balance of lightweight and accuracy. © 2024 SPIE.

关键词： Convolution

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Weed detection based on improved Yolov5 8

Weed detection based on improved Yolov5

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8th International Conference on intelligent Computing and Signal processing, ICSP 2023

作者： Yu, ZiZheng Nie, ZiJun Hu, Yong Qi Yan, HuangBin Lou, LiJun Yin, Zhijian Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350302455

The scale of grain production affects human life and development. With the continuous expansion of cultivated land, the reproductive ability of weeds to mutiply gradually increases, which affects the growth of crops. If weeds are not treated properly managed, they can also allow spread indiscriminately in the field and reduce crop yields. The growth ability of weeds is stronger than that of crops, and there are many types of weeds with a wide of species diversity. To solve the problem that the existing weed detection methods cannot detect and classify weeds accurately and quickly, a deep learning method based on improved Yolov5 was designed for weed detection. By replacing the 3 × 3 convolution with multi-head self-attention (MHSA) in the Yolov5's backbone, the accuracy of weed detection is improved. The experimental results show that the improved Yolov5 weed detection algorithm achieves 51.4% accuracy. Compared with the original Yolov5 model, the calculation amount is 3 points less than the original, and total time required is slightly shorter, which improves the accuracy of weed classification and positioning. © 2023 IEEE.

关键词： Crops

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Plume Finding with Frontier-Based Planner 6

Plume Finding with Frontier-Based Planner

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Zhang, Xin-Kai Li, Ji-Gong Yang, Jing School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798350356618

Toxic/hazardous gas leaks seriously jeopardize public safety and the ecological environment, and how to utilize drones with on-board gas sensors to quickly detect gas plumes at an early stage of a gas leak and take timely measures can effectively prevent the expansion of the accident. Traditional plume finding methods have the disadvantages of poor targeting, long search times, and repeated exploration in some areas. In view of this, this paper proposes a method of plume finding with frontier-based planner, where a heuristic function is designed to select optimal observation points and thus plan path to search plume. Here an air-mass path model is constructed to estimate the region that would likely be observed by the UAV at a candidate observation site on the frontier, and the area of the unknown part in the region is considered as the environmental exploration gain. The change in flight direction and the distance traveled by the UAV are used as environmental exploration costs when it will fly from its current pose to the candidate observation site. Simulation results show that the proposed frontier-based exploration method for plume finding has good performance in odor source localization tasks in outdoor environments. © 2024 IEEE.

关键词： Direct air capture

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