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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

IEEE communications Surveys and Tutorials 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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International Conference on Electrical Power and Energy systems (ICEPES)

作者： Franciskus Antonius Alijoyo Deepak A. Vidhate Chamandeep Kaur V Nivedan V. Kanpur Rani A. Balakumar School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar Maharashtra India Department of Computer Science Jazan University Jazan Saudi Arabia Department of Electronics and Communication System Sri Ramakrishna College of Arts & Science Coimbatore India Hindustan Institute of Technology and Science Chennai India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future.

关键词： Cloud computing Scalability Transforms Real-time systems Smart grids Internet of Things Servers

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Synthesis and Simulation Study of Titanium Dioxide-Based Nanomaterial for Electron Carrier Selective Contact Solar Cell

SSRN

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SSRN 2024年

作者： Yi, Junsin Rahman, Rafi Ur Aslam, Junaid Jony, Jaljalalul Abedin Aida, Maha Nur Mengmeng, Chu Hussain, Shahzada Qamar Park, Sangheon Khan, Alamgeer Khokhar, Muhammad Quddamah Yousuf, Hasnain Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Physics COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of

This research focused on synthesizing titanium dioxide (TiO2) nanomaterials using the sol-gel method to improve Silicon Heterojunction (SHJ) solar cells. Iso-Propanol Alcohol and Titanium Tetra Isopropoxides were used in the synthesis process. TiO2's wide bandgap and low work function make it a suitable replacement for these cells' n-doped amorphous silicon layer. Atomic Force Microscopy (AFM) measured the roughness and 3D profile, while UV-V spectrophotometry assessed absorption. The nanomaterials' structure and surface morphology were analyzed using Scanning Electron Microscopy (SEM) and Transmission Electron Microscope (TEM). Simulations using the AFORS-HET simulator showed that the refractive index of TiO2 and a-Si:H(n), TiO2 with a work function of 4.6 eV enhanced the solar cells' performance by 22.57%, achieving an open-circuit voltage (Voc) of 716.8 mV, a short-circuit current density (Jsc) of 37.71 mA/cm2, a fill factor (FF) of 83.49. Future work will focus on refining TiO2's nanostructure to further advance its photovoltaic applications. © 2024, The Authors. All rights reserved.

关键词： Titanium dioxide

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. © ... 详细信息

关键词： Light modulators

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2023年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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Multi-objective ant lion optimizer for stochastic robotic disassembly line balancing problem subject to resource constraints 2

Multi-objective ant lion optimizer for stochastic robotic di...

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2nd International Conference on computer Vision and Data Mining, ICVDM 2021

作者： Dong, ChengShun Liu, Peisheng Guo, Xi Wang Qi, Liang Qin, Shujin Xu, Gongdan College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China Department of Computer Intelligent Sci. and Tech. Shandong University of Science and Technology Qingdao266590 China College of Economics and Management Shangqiu Normal University Shangqiu476000 China Institute of System Engineering Macao University of Science and Technology 999078 China

With the development of manufacturing and society, a large amount of end-of-life products are generated but with reuse value. Therefore, it is of great significance to dismantle and reuse them. A robotic linear disassembly line plays an important role and promotes the disassembly efficiency in a disassembly process. In this work, we establish a mathematical model of a linear robotic disassembly line balancing problem, which requires the maximization of profit and minimization of energy consumption. Then, an improved ant lion optimizer is proposed to optimize the objectives of the multi-objective robotic disassembly line balancing problem. The experimental results show that the multi-objective ant lion optimizer has good performance, and the diversity and convergence are better than its peers. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Robotics

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Downstream-agnostic Adversarial Examples

arXiv

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arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Security Data Sharing Model for Cloud Network 6th

A Security Data Sharing Model for Cloud Network

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Zhao, Kunyuan Zhang, Qikun Wu, Yimeng Zhu, Liang Zhu, Hongfei Wang, Ruifang School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Zhengzhou Technical College Zhengzhou450000 China College of Computer and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Department of System Technology Zhengzhou University of Light Industry Zhengzhou450000 China

ISBN: (纸本)9789811675010

With the continuous development and wide application of cloud computing technology, its security threats are also increasing, especially in data security sharing and user privacy security protection. Therefore, it is very necessary to establish a security and reliable data sharing model. Combined with blockchain technology, this paper proposes a security data sharing model for cloud network. Blockchain has the characteristics of non-forgery and traceability. By storing the user identity information into the blockchain, not only the authentication efficiency of the user identity can be improved, but also the security of the data sharing system can be improved. At the same time, bilinear mapping and attribute signature technology are used to realize mutual authentication among different entities in domains, secure data sharing and secure interoperability among domains are protected. In addition, the sharing model has the characteristics of low network delay, supporting massive data access and elastic infrastructure. After proof and analysis, the scheme has better security and performance than other references. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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A Novel Ensemble Deep Learning Framework for Breast Cancer Prediction

A Novel Ensemble Deep Learning Framework for Breast Cancer P...

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Innovative Computing, Intelligent communication and Smart Electrical systems (ICSES), International Conference on

作者： Pundru Chandra Shaker Reddy S Nithyapriya Nipun Sharma S Maheswari B Jayaram Tejaswini Katale School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Artificial Intelligence and Data Science Bannari Amman Institute of Technology Anna University Chennai Tamil Nadu India Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Mathematics Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Computer Science and Business System RMK Engineering College Kavaraipettai Tamil Nadu India Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India

Because of its high incidence and high mortality rate, breast cancer requires reliable means of early detection and diagnosis to improve survival rates for its patients. With advantages including increased accuracy and efficiency, deep learning (DL) and machine learning (ML) methods are quickly becoming indispensable in the fight against breast cancer. However, there is a need for additional study because existing approaches have scalability and performance constraints. Utilizing a pre-trained ResNet50V2 and ensemble-based ML approaches, we offer a hybrid approach to reliable breast cancer diagnosis in this study. Learning and extracting latent trends from complex breast-cancer(BC) images is made possible by DL's incorporation, with ML strategies contributing interpretability and generalizability. We performed wide experiments utilizing a openly accessible Invasive-Ductal-Carcinoma(IDC) dataset based on breast histopathology images of varying sample sizes. The resilience and great performance of our hybrid model are supported by the results of our extensive experiments. When compared to state-of-the-art models, our 95% accuracy rate, 94.5 precision, 94.2 recall, and 94.6 F1 score are all improvements. We also found that the best ML model to use with the ResNet50V2 architecture is the Light Boosting Classifier (LGB). This study's findings provide substantial contributions to the field of breast cancer diagnosis thanks to its novel approach, in-depth performance analysis, and trustworthy evaluation. In addition, it may help doctors make better decisions, provide better care, and boost results for people with breast cancer.

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