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arXiv 2022年

作者： Xia, Huiyun Mao, Yijie Clerckx, Bruno Zhou, Xiaokang Han, Shuai Li, Cheng School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’s Canada

As investigations on physical layer security evolve from point-to-point systems to multi-user scenarios, multi-user interference (MUI) is introduced and becomes an unavoidable issue. Different from treating MUI totally as noise in conventional secure communications, in this paper, we propose a rate-splitting multiple access (RSMA)-based secure beamforming design, where user messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop the private streams of other users. We formulate a weighted sum-rate (WSR) maximization problem subject to the secrecy rate requirements of all users. To tackle the non-convexity of the formulated problem, a successive convex approximation (SCA)based approach is adopted to convert the original non-convex and intractable problem into a low-complexity suboptimal iterative algorithm. Numerical results demonstrate that the proposed secure beamforming scheme outperforms the conventional multiuser linear precoding (MULP) technique in terms of the WSR performance while ensuring user secrecy rate requirements. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

Human Observation-Inspired Trajectory Prediction for Autonom...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haicheng Liao Shangqian Liu Yongkang Li Zhenning Li Chengyue Wang Yunjian Li Shengbo Eben Li Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of Macau Macau SAR China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Departments of Civil and Environmental Engineering and Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of Macau Macau SAR China Faculty of Innovation Engineering Macau University of Science and Technology Macau SAR China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel “adaptive visual sector” mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a “dynamic traffic graph” using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs.

关键词： Adaptation models Visualization Time series analysis Predictive models Cognition Trajectory Convolutional neural networks

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Super Twisting Sliding Mode Control Strategy for a Three-Level T-Type Dual MPPT Inverter

Super Twisting Sliding Mode Control Strategy for a Three-Lev...

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Compatibility in Power electronics (CPE)

作者： Farzaneh Bagheri Naki Guler Marco Rivera Hasan Komurcugil Department of Electrical and Electronic Engineering Antalya Bilim University Antalya Turkey Electrical and Electronic Engineering Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Power Electronics Machines and Control (PEMC) Research Institute University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Académica Universidad de Talca Curicó Chile Department of Computer Engineering Eastern Mediterranean University (EMU) Famagusta Turkey

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

Nowadays Residential and small commercial power systems are taking advantage of the dual maximum power point tracking (MPPT) inverters, optimizing energy harvesting from two individual solar panel arrays to increase performance and energy output. Three-level T-type inverters are also famous due to their lower total harmonic distortion, high efficacy, and lower voltage stress on the switch which are a good option to be integrated into the dual MPPT solar plants. So, this article incorporates the dual MPPT solar plants with a T-Type grid-connected inverter which uses a double super twisting sliding mode control (STSMC) to control the injected currents to the grid in d and q reference frames. The STSMC mitigates chattering while showing robustness under model variations and dynamic response. The efficiency of the proposed control method under different irradiation values is verified through simulation results.

关键词： Maximum power point trackers Total harmonic distortion Switches Inverters Robustness Real-time systems Solar panels Time factors Voltage control Sliding mode control

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CLeBPI: Contrastive Learning for Bug Priority Inference

arXiv

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arXiv 2022年

作者： Wang, Wenyao Wu, Chenhao He, Jie Faculty of Innovation Engineering Macau University of Science and Technology China College of Computer Science and Electronic Engineering Hunan University Hunan Changsha China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Guangxi Wuzhou China

Automated bug priority inference can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. Currently, there are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports;2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. In this paper, we propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for bug priority inference in a supervised way. To verify the effectiveness of CLeBPI, we choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its effectiveness. Copyright © 2022, The Authors. All rights reserved.

关键词： computer software maintenance

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Machine Learning based Beamwidth Adaptation for mmWave Vehicular Communications

Machine Learning based Beamwidth Adaptation for mmWave Vehic...

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IEEE International Conference on Telecommunications and Malaysia Communications (ICT-MICC)

作者： Setinder Manic Chuan Heng Foh Abdulkadir Kose Haeyoung Lee Chee Yen Leow Periklis Chatzimisios Klaus Moessner Chakkaphong Suthaputchakun 5GIC & 6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK Department of Computer Engineering Abdullah Gul University Kayseri Turkey School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK Wireless Communication Centre Universiti Teknologi Malaysia Johor Malaysia Department of Information and Electronic Systems Engineering International Hellenic University Thessaloniki Greece Faculty of Electronics and Information Technology Technical University Chemnitz Germany Electrical and Computer Engineering Department Bangkok University Khlong Nueng Thailand

The incorporation of mmWave technology in vehicular networks has unlocked a realm of possibilities, propelling the advancement of autonomous vehicles, enhancing interconnectedness, and facilitating communication for intelligent transportation systems (ITS). Despite these strides in connectivity, challenges such as high path-loss have arisen, impacting existing beam management procedures. This work aims to address this issue by improving beam management techniques, specifically focusing on enhancing the service time between vehicles and base stations through adaptive mmWave beamwidth adjustments, accomplished using a Contextual Multi-Armed Bandit Algorithm. By leveraging various conditions to train the ML agent of the Contextual Multi-Armed Bandit Algorithm, it seeks to learn about vehicle mobility profiles and optimize the usage of different antenna beamwidth settings to maximize seamless connection time. The extensive simulation results showcase the effectiveness of an adaptive beamwidth for mobility profiles, extending the connection time a vehicle experiences with a base station when compared to the existing strategies.

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Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

arXiv

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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Unconventional spin polarization at Argon ion milled SrTiO3 Interfaces.

arXiv

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arXiv 2023年

作者： Kumar, Amrendra Shashank, Utkarsh Maharana, Suman Kumar Mohan, John Rex Gupta, Surbhi Asada, Hironori Fukuma, Yasuhiro Medwal, Rohit Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka820-8502 Japan Department of Physics Motilal Nehru National Institute of Technology Barrister Mullah Colony Uttar PradeshTeliarganj Prayagraj211004 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

Interfacial two-dimensional electron gas (2DEG) formed at the perovskite-type oxide, such as SrTiO3, has attracted significant attention due to its properties of ferromagnetism, superconductivity, and its potential application in oxide-based low-power consumption electronics. Recent studies have investigated spin-to-charge conversion at the STO interface with different materials, which could affect the efficiency of this 2DEG interface. In this report, we presented a Ar+ ion milling method to create a 2DEG at STO directly by inducing oxygen vacancies. To quantify the spin-to-charge conversion of this interface, we measured the angular-dependent spin torque ferromagnetic resonance (ST-FMR) spectra, revealing an unconventional spin polarization at the interface of of Argon ion milled STO and NiFe . Furthermore, a micromagnetic simulation for angular dependent spin torque ferromagnetic resonance (ST-FMR) has been performed, confirming the large unconventional spin type spin polarization at the interface. Copyright © 2023, The Authors. All rights reserved.

关键词： Ferromagnetism

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Automatic Plant Disease Classification and Agriculture Monitoring Robot

SSRN

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SSRN 2024年

作者： Raza, Ali Saeed, Zubair Johar, Ahmed Husnain Asgher, Umer Ayaz, Yasar Nazir, Salman Li, Yunwang Yang, Kehu Socha, Vladimir Department of Computer Engineering University of Engineering and Technology Taxila47080 Pakistan H-12 Islamabad44000 Pakistan H-12 Islamabad44000 Pakistan Horska 2040/3 Prague12800 Czech Republic H-12 Islamabad44000 Pakistan Faculty of Technology Natural Sciences and Maritime Sciences University of South-Eastern Norway Horten Norway Nord University Business School Nord University BodÃ Norway School of Mechanical Electronic and Information Engineering China University of Mining and Technology-Beijing Beijing100083 China Key Laboratory of Intelligent Mining and Robotics Ministry of Emergency Management China Department of Information and Communication Technology in Medicine Faculty of Biomedical Engineering Czech Technical University in Prague Studnickova 7/2028 Prague128 00 Czech Republic

Agriculture is a critical sector for economic development in countries like Pakistan, where the Gross Domestic Product (GDP) is largely dependent on it. However, traditional methods used by farmers to detect plant diseases, such as visual inspection, are inaccurate and lead to low productivity. In this study, we present an autonomous Unmanned Ground Vehicle (UGV) that can detect and classify diseases in plants, as well as monitor environmental factors such as humidity, air pressure, light intensity, and rain using Internet of Things (IoT) techniques on the ThingSpeak platform. Our robot system is equipped with sensors and can move through the potato field to send sensory data in real-time to a Graphical User Interface (GUI). In addition, we remotely monitor the field using a cellphone camera. By combining robotics, IoT, and deep learning, we achieved an accuracy of 98.88\% in the detection and classification of potato leaf diseases, surpassing the accuracy reported in previous literature. Our system provides a means to improve agricultural productivity in Pakistan by enabling accurate and timely disease detection and monitoring of environmental factors. © 2024, The Authors. All rights reserved.

关键词： Agricultural robots

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How do Final Year Projects of engineering Students Relate to Myer-Briggs Type Indicator Assessment?

How do Final Year Projects of Engineering Students Relate to...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Fesal Toosy Arslan Arif Ali Ahmad Christothea Herodotou Stefan Poslad Khurram Hamid Faculty of Engineering University of Central Lahore Punjab Pakistan Institute of Educational Technology Open University Milton Keynes United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Faculty of Management Sciences University of Central Lahore Punjab Pakistan

This work-in-progress paper describes the analysis of an MBTI based personality study conducted on twenty-one final year project groups of electrical engineering undergraduate students at a private university in Pakistan. The potential influence of each of the 16 possible MBTI profiles of members of each group was studied in relation to the final performance and success of the project. The results showed that the presence of certain personality types in each group, in particular ENTJ and ESTP along with combinations of different types had an influence on the overall outcome and deliverables achieved in the project.

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Corrigendum to “Meta-heuristic optimization based cost efficient demand-side management for sustainable smart communities” [Energy Build. (2024) 113599]

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Energy and Buildings 2024年 309卷

作者： Bhagya Nathali Silva Murad Khan Ruchire Eranga Wijesinghe Udaya Wijenayake Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Science Kuwait College of Science and Technology Kuwait Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Sri Lanka

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