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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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VLM-EMO: Context-Aware Emotion Classification with CLIP 5

VLM-EMO: Context-Aware Emotion Classification with CLIP

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5th International Seminar on Artificial Intelligence, Networking and information technology, AINIT 2024

作者： Yao, Yanyin Mei, Xue Xu, Jianping Sun, Zhichuang Zeng, Cheng Chen, Yuming Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Science and Technology On Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9798350385557

Emotion recognition is a pivotal component in various sectors, including medical care, education, service industries, and public safety, due to its potential to enhance interaction and understanding within these contexts. Historically, traditional methods of emotion recognition have primarily concentrated on analyzing facial expressions. While effective to a degree, this approach offers restricted capacity for encoding context, and unable to capture the breadth and depth of emotional responses. In this paper, we proposed the VLM-EMO Model, a novel approach that enhances the capabilities of VLM (Visual Language Model) in the domain of emotion recognition. While VLM itself is a powerful tool for understanding the correlation between images and text, the VLM-EMO Model fine-tunes this ability to focus more intricately on emotional aspects within visual data. This is achieved by integrating emotional intelligence into the model's architecture, allowing it to discern subtle emotional cues and contexts within images. © 2024 IEEE.

关键词： Emotion Recognition

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Empowering Internet of Underwater Things: An Integrated Simulator for Underwater Magneto-Coupled Wireless Sensor Networks

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IEEE Internet of Things Magazine 2025年第1期8卷 109-114页

作者： Songzuo, Liu Ullah, Irfan Muzzammil, Muhammad Ahmed, Niaz Wahid, Abdul Zhen, Shao Ministry of Industry and Information Technology Key Laboratory of Marine Information China College of Underwater Acoustic Engineering Harbin Engineering University China Sanya Nanhai Innovation and Development Base of Harbin Engineering University China FAST National University of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Campus Pakistan

This research paper presents an integration of finite element method (FEM) based models into MATLAB for developing an end-to-end magneto-coupled (MC) communication system tailored for Internet of Underwater Things (IoUT)-based applications. Magneto-coupled communication has emerged as a promising solution for extreme environments like underwater where conventional wireless acoustic communication poses significant challenges. The article exploits the strengths of two software tools: MATLAB and COMSOL Multiphysics, to seamlessly integrate communication protocols with theoretical insights into magnetic channels. The article also demonstrates the development and evaluation of an end-to-end communication system with both simulation and experimental setup. We believe that this integrated approach is highly effective in achieving reliable communication performance in underwater scenarios, thereby advancing the capabilities of IoUT for various real-world applications. © 2025 IEEE.

关键词： MATLAB

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Unsupervised Action Recognition Using Spatiotemporal, Adaptive, and Attention-Guided Refining-Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1460-1471页

作者： Yin, Xinpeng Zhang, Cheng Huang, ZiXu He, Zhihai Cao, Wenming Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Department of Electronics and Information Engineering Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China

Previous articles on unsupervised skeleton-based action recognition primarily focused on strategies for utilizing features to drive model optimization through methods like contrastive learning and reconstruction. However, designing application-level strategies poses challenges. This article shifts the focus to the generation-level modelings and introduces the spatiotemporal adaptively attentions-guided refining network (AgRNet). AgRNet approaches the reduction of costs and enhancement of efficiency by constructing the adaptive activity-guided attention (AAGA) and adaptive dominant-guided attenuation (ADGA) modules. The AAGA leverages the sparsity of the correlation matrix in the attention mechanism to adaptively filter and retain the active components of the sequence during the modeling process. The ADGA embeds the local dominant features of the sequence, obtained through convolutional distillation, into the globally dominant features under the attention mechanism, guided by the defined attenuation factor. Additionally, the progressive feature modeling (PFM) module is introduced to complement the progressive features in motion sequences that were overlooked by AAGA and ADGA. AgRNet shows efficiency on three public datasets, NTU-RGBD 60, NTU-RGBD 120, and UWA3D. © 2020 IEEE.

关键词： Time series analysis

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CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

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Computers, Materials & Continua 2024年第8期80卷 2857-2872页

作者： Chun-Ming Wu Mei-Ling Ren Jin Lei Zi-Mu Jiang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationSchool of Electrical EngineeringNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China School of Electronic Information Engineering Bozhou UniversityBozhou236800China

To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is *** region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature *** the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image *** study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional *** methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other *** algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)*** experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.

关键词： Image fusion deep learning auto-encoder(AE) infrared visible light

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Modality-experts coordinated adaptation for large multimodal models

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Science China(information Sciences) 2024年第12期67卷 79-96页

作者： Yan ZHANG Zhong JI Yanwei PANG Jungong HAN Xuelong LI School of Electrical and Information Engineering Tianjin Key Laboratory of Brain-Inspired Intelligence Technology Tianjin University Shanghai Artificial Intelligence Laboratory Department of Automation Tsinghua University Institute of Artificial Intelligence (TeleAI) China Telecom Corporation Limited

Driven by the expansion of foundation models and the increasing variety of downstream tasks,parameter-efficient fine-tuning（PEFT） methods have exhibited remarkable efficacy in the unimodal domain,effectively mitigating the consumption of computational resources. Although recent research has shifted attention to the multimodal domain and achieved efficient parametric adaptation of large multimodal models（LMMs） for downstream tasks, they still encounter two limitations:（1） low performance;（2） poor compatibility. This work proposes a modality-experts coordinated adaptation（ModeX） method for the multimodal domain, offering an effective, plug-and-play, and lightweight adaptation architecture for diverse ***, ModeX adaptively coordinates different modality experts in terms of the types of network structure and input data. Besides, an effective coordinator equipped with a routing algorithm is developed for generating corresponding weights, which centers on leveraging the synergy among multimodal data. Extensive experiments on 15 multimodal downstream benchmarks and five LMMs demonstrate that ModeX is capable of seamlessly adapting to diverse LMMs, outperforms the state-of-the-art PEFT methods and even exhibits superior performance compared with full fine-tuning methods. Notably, on NLVR2task, ModeX achieves 84.06% accuracy with only 12.0M trainable parameters, outperforming the full fine-tuning by 1.63%.Moreover, our ModeX method demonstrates superior stability and offers higher training efficiency, both in terms of training parameters and training duration. Our source code has been released at https://***/zhangy0822/ModeX.

关键词： large multimodal model multimodal learning vision-language pretraining parameter-efficient fine-tuning adapter modality expert

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Robust Aerodynamic Parameter Estimation of Unmanned Aircraft Based on Two-step Identification

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2023年第4期67卷 394-402页

作者： Kimathi, Stephen Lantos, Bela Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok krt. 2. BudapestH-1117 Hungary

This paper presents the estimation of stability and control derivatives of an unmanned aircraft. The aerodynamics are described using regressors composed of velocity, angular rates, flow angles and control surface deflections. The flight data is generated from numerical simulation of postulated equations of motion describing the aerodynamics model. Least squares based on the equation error method is used to estimate the parameters representing the different force and moment aerodynamic coefficients. Statistical analysis is done on the estimates to determine the accuracy and adequacy of the estimates to describe the aerodynamic model. A dynamic simulation based on the identified aerodynamic model is used to improve the parameter estimates through regression of the errors between the flight data and the model response. The aircraft under consideration is a scaled Yak-54 fixed wing unmanned aerial vehicle. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Fixed wings

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Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

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China Communications 2024年第12期21卷 218-229页

作者： Xu Wenjing Wang Wei Li Zuguang Wu Qihui Wang Xianbin College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing 211106China Department of Electrical and Computer Engineering Western UniversityCanada

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless ***,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be *** this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different *** minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each *** apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal ***,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the *** results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

关键词： blockchain collaborative edge computing resource optimization task allocation

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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