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Circuit, Systems and communication (ICCSC), International Conference on

作者： Naima Dihaji Hanane Nasraoui Sudipta Das Jamal El Aoufi Mustapha Kchikach Power Electronics and System Control Lab Higher National School of Mines Rabat Laboratory of Aeronautical Telecommunication Mohammed VI International Academy of Civil Aviation Rabat Morocco Department of Electronics and Communication Engineering IMPS College of Engineering and Technology Chandipur West Bengal India Laboratory of Aeronautical Telecommunication Mohammed VI International Academy of Civil Aviation Nouaceur Morocco Power Electronics and System Control Lab Higher National School of Mines Rabat Rabat Morocco

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

In this article, a $2 \times 1$ UWB-MIMO flower bouquet has been discussed using a low-cost FR-4-epoxy material for wireless communication systems. This antenna provides a wider bandwidth impedance of $[3.42-14.64] \mathrm{GHz}$. The reflection coefficient of the proposed work achieves $\mathbf{- 3 5 . 0 4} \mathrm{dB}$. For the aims of reducing mutual coupling, a CSRR was etched on the top and the bottom of the radiation element and the isolation achieves 44.58 dB at 11.63 GHz. Furthermore, theECC parameter is less than 0.002 and the diversity gain is close to 10 dB. Moreover, the TARC parameter is low of $\mathbf{- 1 0} \mathbf{d B}$ throughout the operating bandwidth. Therefore, the proposed design is a good choice for wireless communication systems.

关键词： Wireless communication Mutual coupling Bandwidth Flowering plants Diversity methods Reflection coefficient Impedance

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MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE TRANSFER

MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE T...

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GLOBECOM 2022 - 2022 IEEE Global communications Conference

作者： Huaji Zhou Xu Wang Jing Bai Zhu Xiao Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Artificial Intelligence Xidian University Xi'an China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781665435413

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.

关键词： Training Deep learning Transfer learning Data acquisition Modulation Global communication Knowledge transfer

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Improved GNSS-IR height measurement method based on locally weighted regression

Improved GNSS-IR height measurement method based on locally ...

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9th International Symposium on Test Automation & Instrumentation (ISTAI 2022)

作者： Z. Luo J. Shang F. Li R. Pang Y. Wang College of Telecommunication Engineering Hangzhou Dianzi University Hangzhou 310018 People's Republic of China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033 People's Republic of China

Global Navigation Satellite System Interferometric reflectance measurement (GNSS-IR) is a new remote sensing technology which uses satellite reflectance signals for monitoring. Aiming at the problems that the current GNSS-IR altimetry in land scenes is rarely applied and the accuracy of the traditional method is low under the reflection surface with high roughness, the traditional GNSS-IR altimetry is improved based on the local weighted regression method. A general navigation receiver method is proposed here, and does not need to use expensive special receiver or geodesic receiver. The local weighted regression method is used to separate the reflected signals to solve the problem of low local fitting accuracy of traditional methods when dealing with complex SNR changes in land scenes. The normalized Lomb-Scargle is used for spectrum analysis to improve the stability when there are few valid observations. The simulation and test results show that the proposed method has a significant improvement in the measurement accuracy compared with the traditional GNSS-IR method in the high-roughness ground scene. The single satellite measurement accuracy can reach within 10cm, and the root mean square error of the dual-frequency and multi-satellite combined altimetry measurement result is 2.62cm.

关键词： height measurement mean square error methods radio receivers regression analysis remote sensing satellite navigation

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Fast Coding Mode Decision for Intra Prediction in VVC SCC

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IEEE Transactions on Broadcasting 2025年第2期71卷 506-516页

作者： Dayong Wang Weihong Liu Zeyu Zhou Xin Lu Jinhua Liu Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Computer Science and Informatics De Montfort University Leicester U.K. School of Digital Technology Industry Shangrao Normal University Shangrao China Glasgow College and the School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Currently, screen content video applications are widely used in our daily lives. As the latest Screen Content Coding (SCC) standard, Versatile Video Coding (VVC) SCC employs a quad-tree plus nested multi-type tree (QTMT) coding structure and various screen content coding modes (CMs). This design enhances the coding efficiency of VVC SCC but also results in a highly complex coding process, which significantly hinders the broader adoption of screen content video technology. Consequently, improving the coding speed of VVC SCC is highly desirable. In this paper, we propose a fast CM and transform decision algorithm for Intra prediction in VVC SCC. Specifically, we initially use Convolutional Neural Networks (CNNs) to predict content types for all Coding Units (CUs). Subsequently, we predict candidate CMs for CUs based on the CM distributions of different content types. We then select the Sum of Absolute Transformed Difference (SATD) as a feature and use a naive Bayes classifier to skip unlikely Intra mode early. Finally, we terminate Block-based Differential Pulse-Code Modulation (BDPCM) early and then select the best transform type in Intra mode prediction to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed by an average of 39.28%, with the BDBR increasing by 0.80%.

关键词： Encoding Copper Prediction algorithms Classification algorithms Transforms Electronic mail Standards Complexity theory Training Partitioning algorithms

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Concept, Principle, and Modeling of Driving Risk Entropy Based on Human-Vehicle-Road Coupling Model for Autonomous Vehicle

SSRN

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SSRN 2024年

作者： Gao, Hongbo Yang, Hanqing Zhu, Juping Su, Huiping Wang, Ning Xie, Liping Li, Keqiang Department of Automation University of Science and Technology of China Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Chongqing College of Mobile Communication Chongqing400044 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

Driving risk entropy, based on entropy law, is an innovative concept proposed for intelligent driving systems. The concept deals with the driving risks caused by the human-vehicle-road system from the driving information obtained;however, driving risks are difficult to assess due to many influencing factors and great uncertainty. The risk entropy model of vehicles is visualized by introducing the concept of basic isotropic risk elliptic line (BIREL). Further incorporating the concept of information entropy, risk entropy models for vehicle interaction and vulnerable participants are established, and the risk entropy model for roads is developed using raster coordinates. Ultimately, a three-dimensional driving risk entropy model is constructed based on the human-vehicle-road coupling model, with appropriate weights assigned to each parameter through the DDPG algorithm. Experimental results demonstrate the effectiveness of the driving risk entropy model. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Thai Beef Cattle Recognition for Fattening Farms: An Empirical Study of Cattle Cooperative Practices in Chiang Rai

Thai Beef Cattle Recognition for Fattening Farms: An Empiric...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering (ECTI-NCON)

作者： Ant Maw Khon Myint Myat Pradorn Sureephong Qing Fan Santichai Wicha School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand Key Laboratory of China(Guangxi)-ASEAN CrossBorder Animal Disease Prevention and Control Ministry of Agriculture and Rural Affairs of China Guangxi Veterinary Research Institute Guangxi China Computer and Communication Engineering for Capacity Building (CCC) School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research focuses on developing an automated system to identify the breed and sex of Thai beef cattle using advanced computer vision technology. By using the YOLOv11 (You Only Look Once) model, a state-of-the-art object detection tool, this system can quickly and accurately identify cattle breeds and determine their sex. High-resolution image datasets were collected and augmented, yielding 81.2% accuracy for breed detection and 95.0% for sex classification, with mAP@50 scores of 85.1% and 99.3%, respectively. However, limited data for certain breeds, like Thai Native and Japanese cattle, reduced the model’s performance for those categories. To overcome this, we aim to expand the dataset from CCTV footage in future studies. The system will include user-friendly features, such as real-time alerts and dashboards, making it practical for farmers to use. This project demonstrates how AI can improve cattle farming by saving time, reducing errors, and supporting better decisions. Future improvements will explore applications in disease detection and behavior monitoring.

关键词： YOLO Deep learning Computer vision Cows Real-time systems Data models Object recognition Monitoring Farming Diseases

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ASTARS Aided NOMA Covert communication Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Yue, Xinwei Song, Xinlong Zhao, Jingjing Huang, Chongwen Jin, Hongxu Xiang, Wei Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing102206 China Beijing Information Science and Technology University Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing102206 China Beihang University School of Electronic and Information Engineering Beijing100191 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Hangzhou310027 China Zunyi Normal University School of Information Engineering Zunyi563006 China La Trobe University School of Computing Engineering and Mathmatical Sciences MelbourneVIC3086 Australia

The active simultaneously transmitting and reflecting surface (STARS) has been proposed as a complement of passive STARS (PSTARS) to inhibit the double path-loss. This paper applies the active STARS (ASTARS) to aid in non-orthogonal multiple access (NOMA) covert communications over cascaded rice fading channels, where two wardens are located on the opposite sides of ASTARS to monitor the transmission information. More specifically, we derive approximate expressions for detection error probability of two wardens. The optimal detection threshold and minimum detection error probability are presented in the worst covert scenarios. The covert transmission rate expressions for a coupe of covert users are further deduced by taking into account imperfect/perfect successive interference cancellation. The experimental simulation results are presented to confirm that: 1) The detection error probability of ASTARS-NOMA networks is superior to that of ASTARS assisted-orthogonal multiple access (ASTARS-OMA) and PSTARS aided NOMA networks;2) As the number of active elements increases, the covert transmission rates of ASTARS-NOMA networks become larger relative to ASTARS-OMA;and 3) ASTARS-NOMA has ability to provide the enhanced system throughput in delay-tolerant transmission mode. © 2025 IEEE.

关键词： Fading channels

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Channel Estimation Based on Lorentzian Adaptive Algorithm in a-stable Noise Environment 9

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： Cherednichenko, Aleksey Huang, Xinqi Wang, Yanyan Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781728198057

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel ina-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons. © 2020 IEEE.

关键词： Channel estimation

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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A Face Mask Detection Model Based on Improved Genetic Algorithm-Optimized Convolution Neural Network in Covid-19 Pandemic

SSRN

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SSRN 2023年

作者： Liu, Lulin Wang, Xu Du, Sheng School of Future Technology China University of Geosciences Wuhan430074 China College of Communication Engineering Jilin University Changchun130000 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Wearing masks is an essential means of ensuring public safety under the pandemic of COVID-19. Detecting whether people wear masks correctly is vital for preventing and controlling new crown epidemics. In response to the poor effectiveness of current basic deep learning methods for mask-wearing detection, this paper proposes a convolutional neural network optimized by an improved genetic algorithm to detect face masks. An improved genetic algorithm is used to optimize the parameters in the convolutional neural network, and then the effect is compared with those of other papers. The result shows that the proposed method is more effective in detecting the wearing of masks with a correct rate of up to 99.125% and can provide practical help and guidance for preventing and controlling the COVID-19 epidemic. © 2023, The Authors. All rights reserved.

关键词： Object detection

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