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

IEEE Communications Surveys and Tutorials 2025年第3期27卷 1656-1686页

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 Institute for Data Science and Computing University of Miami Department of Electrical and Computer Engineering 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 Shenzhen Research Institute of Big Data Future Network of Intelligence Institute The Chinese University of Hon g Kong School of Science and Engineering Shenzhen518172 China

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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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Optimizing Convolutional Neural Networks for Plant Disease Detection on ARM-M Microcontrollers

Optimizing Convolutional Neural Networks for Plant Disease D...

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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Hermawan Nugroho Chew Jing Xan Tay Fei Siang Sivaraman Eswaran Department of Electrical and Electronic Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

Rice production in Malaysia is facing significant challenges due to plant diseases and environmental hazards, leading to a decline in the rice self-sufficiency ratio. To address these issues, this study explores the deployment of AI-driven plant disease detection models on ARM-M based microcontrollers, specifically the STM32H747I-DISCO Discovery kit. Utilizing Convolutional Neural Networks (CNNs) for image recognition, we aim to achieve real-time disease monitoring without relying on continuous internet connectivity. Our research evaluates five state-of-the-art models-FD-MobileNet, MobileNet V1, MobileNet V2, ResNet V1, and SqueezeNet V1-focusing on their suitability for resource-constrained environments. The results indicate that FD-MobileN et is the most resource-efficient model with the lowest number of Multiply-Accumulate operations (MACCs) and the shortest inference time, achieving an accuracy rate of 98.44%. However, SqueezeNet, despite having the highest accuracy at 99.22%, faces deployment challenges due to its large model size on the STM32H747I-DISCO board. This work highlights the potential of embedded AI applications in enhancing agricultural resilience and efficiency.

关键词： Plant diseases Accuracy Microcontrollers Computational modeling Crops Real-time systems Agriculture Convolutional neural networks Testing Synthetic data

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Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

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computers, Materials & Continua 2021年第11期69卷 1631-1652页

作者： Muhammad Waleed Pasha Mir Yasir Umair Alina Mirza Faizan Rao Abdul Wakeel Safia Akram Fazli Subhan Wazir Zada Khan Department of Electrical Engineering National University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering National University of Modern Languages(NUML)Islamabad44000Pakistan Faculty of Computing Capital University of Science and TechnologyIslamabad44000Pakistan

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been *** those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor *** and online are the two phases of the fingerprinting *** researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a *** learning is used for the model training in the offline phase while the locations are estimated in the online *** researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an *** learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor *** training sets are a key for obtaining better results in machine learning ***,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing *** proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the *** proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

关键词： Indoor positioning system fingerprinting received signal strength indicator mean position error support vector machine

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Confidential computing as an Attempt to Secure Service Provider's Confidential Client Data in a Multi-Tenant Cloud Environment 44

Confidential Computing as an Attempt to Secure Service Provi...

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Novkovic, Bojan Bozic, Anita Golub, Marin Gros, Stjepan Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Department of Electronics Croatia

ISBN: (纸本)9789532331011

Cloud-oriented infrastructure posed itself as a predominant deployment paradigm in the recent decade due to its ease of provisioning and relatively low cost. However, entrusting a third party with sensitive data in a multi-tenant environment brings about increased data breach risks. The aim of this paper is to give an insight into challenges and threats encountered in mitigating data breach and repudiation risks for service providers utilizing cloud-based environments. Through constructing and studying a possible cloud-based service, we form a corresponding threat model with a special focus on risks originating from internal actors. We explore confidential computing as a possible solution for data confidentiality and mitigation of internal data breach risks for Software as a Service (SaaS) providers. © 2021 Croatian Society MIPRO.

关键词： Costs

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Real-Time Disease Classification in Coffee Plants with Coral Developer Board Mini

Real-Time Disease Classification in Coffee Plants with Coral...

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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Mohammad Hamzah Warsi Hermawan Nugroho Haider Abbas AlMurib Tay Fei Siang Sivaraman Eswaran Department of Electrical and Electronic Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

Coffee significantly contributes to the socioeconomic development of numerous countries. However, coffee production faces several challenges, including the detrimental effects of coffee leaf diseases, which impact crop yield and quality. Traditional methods for diagnosing coffee leaf diseases are time-consuming and prone to human error. This paper presents an automated system for coffee leaf disease classification with edge computing using the Coral Developer Board Mini, featuring Google's Edge TPU. The system leverages advanced convolutional neural network (CNN) models, including MobileNetV2, VGG-16, VGG19, and DenseNet121, to achieve high accuracy in disease identification. The MobileNetV2 model achieved the highest training accuracy of 98.9%. Inference time comparisons revealed that the Coral Developer Board Mini, with an inference time of 10 milliseconds, outperformed a traditional GPU-based workstation, which had an inference time of 40 milliseconds. Results highlight the efficiency and suitability of the Coral Developer Board Mini for real-time, on-device machine learning applications in agriculture.

关键词： Training Accuracy Computational modeling Scalability Real-time systems Workstations Convolutional neural networks Socioeconomics Diseases Edge computing

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An Experimentally Validated Model of the Propeller Force Accounting for Cross Influences on Multi-Rotor Aerial Systems

An Experimentally Validated Model of the Propeller Force Acc...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Barbara Bazzana Ralph Brantjes Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this paper, we propose a model for the thrust coefficient of propellers that can take into account cross-influence between adjacent propellers. The aerodynamic interaction between propellers in multirotor aerial vehicles reduces the thrust they can produce. The influence between propellers depends on their relative positioning and orientation, which are taken into account by the proposed model. It is validated on measurements collected by a force sensor mounted on a propeller for different configurations of the adjacent propellers in a support structure. In this work, we focus on configurations with small relative orientations. Results show that the proposed model outperforms the traditional constant model in terms of thrust prediction on the data we collected, and it performs better than other models with fewer parameters, being the only one with less than 10% maximum percentage error.

关键词： Training Adaptation models Torque Propellers Atmospheric modeling Force Velocity control

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A control Theoretic Study on Omnidirectional MAVs with Minimum Number of Actuators and No Internal Forces at Any Orientation

A Control Theoretic Study on Omnidirectional MAVs with Minim...

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IEEE Conference on Decision and control

作者： Ahmed Ali Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a new multirotor aerial vehicle class of designs composed of a multi-body structure in which a main body is connected by passive joints to links equipped with propellers. We have investigated some instances of such class, some of which are shown to achieve omnidirectionality while having a minimum number of inputs equal to the main body Degrees of Freedom DoF’s, only uni-directional positive thrust propellers, and no internal forces generated at steady state. After dynamics are derived following the Euler-Lagrange approach, an I/O dynamic feedback linearization strategy is then used to show the controllability of any desired pose with stable zero dynamics. We finally verify the developed controller with closed-loop simulations.

关键词： Three-dimensional displays Propellers Dynamics controllability Regulation Steady-state Feedback linearization control theory Closed loop systems Vehicle dynamics

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Robust Length of Stay Prediction Model for Indoor Patients

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computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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