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作者： Zhang, Mingyue Zhou, Junlong Cong, Peijin Zhang, Gongxuan Zhuo, Cheng Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100190 China Zhejiang University Department of Information Science and Electronic Engineering Hangzhou310027 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Internet of Vehicles (IoV) has become an indispensable data sensing and processing platform in Internet of Things (IoT) for intelligent transportation. The mounted cameras on the vehicles along with the fixed roadside cameras are utilized to provide pictorial services for IoV users and law enforcement agencies. For such forensic services, ensuring the security and privacy of vehicles while guaranteeing the efficiency of data transmission among vehicles is important. In this paper, we propose a lightweight incentive authentication scheme (LIAS) for forensic services in IoV. LIAS is developed on a three-tier architecture containing cloud layer, fog layer, and user layer. LIAS uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to realize a secure anonymous authentication efficiently. We conduct correctness and security analysis, as well as performance analysis and evaluation to validate the high security and efficiency of LIAS. Experimental results reveal that, the communication and computation overheads as well as the message delay and packet loss of LIAS are much lower than those of state-of-the-art techniques. Note to Practitioners - This paper is motivated by the security and privacy issues of forensic services in IoV for intelligent transportation. Our goal is to improve the security and privacy of vehicles while guaranteeing the lightweight and incentive of data transmission among the vehicles. Fog-assisted IoV is introduced to fully utilize the capacities of near-user edge devices as well as the connections between fog nodes and devices. However, it still faces the difficulties in ensuring vehicles' security and privacy. Moreover, vehicles' information dissemination could be easily monitored because of the unavoidable defect of wireless communication. Thereby, it is essential to guarantee the security and privacy of vehicles while enhancing the efficiency of vehicles' data transmission during the forensic service.

关键词： Authentication

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Multi-agent Reinforcement Learning Based Resource Allocation in End-Edge-Cloud Enabled Industrial Internet of Things

Multi-agent Reinforcement Learning Based Resource Allocation...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yanmei Chen Xiaohuan Li Jin Ye Xun Wang Qian Chen School of Information and Communication Guilin University of Electronic Technology Guilin China School of Computer Electronics and Information Guangxi University Nanning China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin China

The dynamic changes of task requirement and the time-varying distribution of resources in Industrial Internet of Things (IIoT) make a challenge for traditional static resource allocation methods to flexibly adapt to these changes. These can lead to increase latency and energy consumption, which result in low efficiency of resource allocation. In this paper, a resource allocation method based on multi-agent reinforcement learning (MARL) in end-edge-cloud enabled IIoT is proposed. The method builds an end-edge-cloud collaboration resource management model in IIoT scenarios, and constructs the optimization problem of minimizing task latency and energy consumption. Then, the optimization problem is further transformed into a multi-agent Markov decision process (MDP). Furthermore, the multi-agent deep deterministic policy gradient (MADDPG) is adopted to solve the formulated MDP problem. Finally, simulation results demonstrate that the proposed algorithm can significantly reduce the task latency cost by 27% and energy consumption cost by 15% compared with that of the baseline methods.

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Secure Beamforming for Continuous Aperture Array (CAPA) Systems

arXiv

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wang, Zhaolin Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering UCD College of Engineering and Architecture University College DublinD04 V1W8 Ireland School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on Zero-Forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a waterfilling approach to design an optimal power allocation scheme that maximizes the WSSR. In high SNR regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Akkaraekthalin, Prayoot Somwong, Tanee Chudpooti, Nonchanutt Hirun, Nuttawut Meesomklin, Suwaluck King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronic and Telecommunications Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok College of Industrial Technology Department of Electronics Engineering Technology Bangkok10800 Thailand

ISBN: (纸本)9798331543952

关键词： greenhouse system IOT smart agriculture Smart farming water convolvulus

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Adaptable Invisibility Management Using Kirigami-Inspired Transformable Metamaterials

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Research 2021年第1期2021卷 1034-1044页

作者： He-Xiu Xu Mingzhao Wang Guangwei Hu Shaojie Wang Yanzhao Wang Chaohui Wang Yixuan Zeng Jiafang Li Shuang Zhang Wei Huang Air and Missile Defense College Air Force Engineering UniversityXi’an 710051China Institute of Flexible Electronics Northwestern Polytechnical UniversityXi’an 710072China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore 117583 Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)Beijing 100081China School of Physics Beijing Institute of TechnologyBeijing 100081China Department of Physics University of Hong KongHong KongChina Department of Electronic&Electrical Engineering University of Hong KongHong KongChina

Many real-world applications,including adaptive radar scanning and smart stealth,require reconfigurable multifunctional devices to simultaneously manipulate multiple degrees of freedom of electromagnetic(EM)waves in an on-demand ***,kirigami technique,affording versatile and unconventional structural transformation,has been introduced to endow metamaterials with the capability of controlling EM waves in a reconfigurable ***,we report for a kirigami-inspired sparse meta-architecture,with structural density of 1.5%in terms of the occupation space,for adaptive invisibility based on independent operations of frequency,bandwidth,and *** on the general principle of dipolar management via structural reconstruction of kirigami-inspired meta-architectures,we demonstrate reconfigurable invisibility management with abundant EM functions and a wide tuning range using three enantiomers(A,B,and C)of different geometries characterized by the folding angleβ.Our strategy circumvents issues of limited abilities,narrow tuning range,extreme condition,and high cost raised by available reconfigurable metamaterials,providing a new avenue toward multifunctional smart devices.

关键词： tuning visibility smart

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

arXiv

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

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A Novel Method for Wind Power Plants’ Repowering Based on Multi-Criteria Decision-Making

SSRN

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SSRN 2023年

作者： Gil García, Isabel C. Fernández-Guillamón, Ana García-Cascales, M. Socorro Molina-García, Ángel c/ Coruña km 38500 Collado Villalba Madrid28400 Spain Dept. of Applied Mechanics and Projects Engineering - E.T.S. de Ingenieros Industriales & Renewable Energy Research Institute Universidad de Castilla-La Mancha C/ De la Investigación S/N Albacete02006 Spain Department of Electronics Computer Architecture and Project Engineering Department of Automatics Electrical Engineering and Electronic Technology Universidad Politécnica de Cartagena Campus Muralla del Mar Murcia Cartagena30202 Spain

Due to the climate change, over the last decades there have been changes in the rhythm of nature. However, there is an increasing social consciousness regarding decarbonization, and governments include new policies and economic support measures for renewables. The participation of the onshore and offshore wind sectors play a central role in this scenario, expecting that more than 6000 GW will be globally installed in 2050. However, the useful life of many wind power plants is coming to their end, and thus, some countries will face in the coming years the decision of repowering, decommissioning, installing new turbines, or a combination of them. In general, such areas with the greatest wind potential are already occupied by relatively old wind turbines. Subsequently, a complex group of factors influences on the best solution decision-making: climatic, technological, environmental, social or economic. This paper proposes an optimized methodology based on multi-criteria decision-making to estimate the most appropriate solution for extending the useful-life of wind power plants. The methodology is divided into three stages: (i) data, (ii) alternatives and (iii) optimization. It was assessed with a Spanish onshore wind power plant connected to the grid. From the initial data, 11 alternatives were designed based on 10 selection factors. The results provide an optimal solution, combining both repowering and installation of new wind turbines. It would allow us to reduce the LCOE of the new wind plants installed in the European Union by more than 50%. © 2023, The Authors. All rights reserved.

关键词： Wind turbines

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A novel deep learning model based on YOLOv5 optimal method for coal gangue image recognition

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Scientific reports 2025年第1期15卷 18630页

作者： Tongkai Gu Haiyan Zhao Yasheng Chang Sitong Yan Feihan Cao Wei Liu School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Xi'an 710055 China. School of Optical and Electronic Information & Suzhou Key Laboratory of Biophotonics & International Joint Metacenter for Advanced Photonics and Electronics Suzhou City University Suzhou 215104 China. School of Architecture & Design Kunshan Dengyun College of Science and Technology Suzhou 215300 China. School of Optical and Electronic Information & Suzhou Key Laboratory of Biophotonics & International Joint Metacenter for Advanced Photonics and Electronics Suzhou City University Suzhou 215104 China. cocys@***. School of Computer Science and Technology Soochow University Suzhou 215006 China. School of Optoelectronic Information Engineering Soochow University Suzhou 215006 China.

Coal gangue recognition presents significant challenges in the mining industry due to its inefficient and costly traditional treatment methods. The advent of deep learning techniques has introduced novel solutions for automating and online coal gangue processing. Despite the potential of deep learning models, challenges such as overfitting and the need for extensive labeled datasets hinder their effectiveness. You Only Look Once version 5 (YOLOv5), with its rapid inference speed and high accuracy, offers a suitable solution for real-time coal gangue detection. This research investigates the application of YOLOv5 for coal gangue image recognition, involving data preprocessing, model training, and optimization. Experimental results demonstrate that incorporating the multiple channel attention mechanism and lightweight content-aware re-assembly of features up-sampling operator significantly improves model confidence and recognition performance.

关键词： Coal gangue detection Detection accuracy Overfitting Preprocessing YOLOv5

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Cacao-X: Contour Assisted Convolutional Neural Networks with Image Explainability and Inference on the Edge

SSRN

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SSRN 2022年

作者： Daher, Ali W. Muselli, Marco Caviglia, Daniele D. Ferrari, Enrico Verda, Damiano Chible, Hussein Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

Image classification and interpretability is a basic procedure in modern AI systems as well as being employed in the fields of security, instrumentation, and image classification. Explainable Artificial Intelligence also plays a crucial role in providing machine learning techniques with a white-box output that allows the practitioner to follow or interpret the rules. Therefore, we present CACAO-X which is a framework for ML forecasts that utilizes an algorithm which converts images into time-series and employs CNN, LSTM, and MLP on the original images and converted data in a mixed input approach achieving accurate forecasts on multiple applications. Furthermore, Shapley values are employed on the proposed ML models to extract the local explainable features of such images. These features are again used with the same conversion algorithm to generate time-series and application specific landmarks using the Logic Learning Machine (LLM) to determine the explainable rules. In addition to being generated automatically, explicative labelling is adopted in the dataset which give insight to the operator to which landmarks, positions and angles contribute to the classification of each record. The system is robust as it achieves benchmark results in the gender classification application while remaining competitive in all others where it is used as a reference model for global image explainability. Additionally, the ML inference for every application is performed according to the edge-computing paradigm, by deploying the models on a limited resources Raspberry Pi platform through SSH access. Moreover, a novel callback function has been developed which is able to stop the training of the CACAO-NET model at a stable state based on the level of oscillation around the most recent mean accuracy. © 2022, The Authors. All rights reserved.

关键词： Image classification

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