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

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong 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. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

Cloud-Driven Autonomous Drones for Dynamic Emergency Medical Response Using Convolutional Neural networks

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Cloud-Driven Autonomous Drones for Dynamic Emergency Medical...

Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： P. Karthikeyani D. David Neels Ponkumar S. Hemalatha M. Tamilselvam M. Muthulekshmi C. Dhanesh Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai India Department of Civil Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mechanical Engineering S.A. Engineering College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

This research presents an innovative method for improving emergency medical response by using autonomous drones powered by Convolutional Neural networks (CNNs) and controlled by cloud computing. The proposed system incorporates advanced technology to speed up help delivery in emergency conditions requiring rapid medical intervention. Using CNNs, drones can detect and recognize objects in real-time, vital for quickly diagnosing life-threatening medical emergencies. Drones can make quick decisions and navigate themselves using the vast computer capacity made available by cloud computing. The system uses dynamic routing algorithms to maximize drone deployment and respond quickly to changing emergency dynamics. The experimental results demonstrate the framework's effectiveness and scalability, highlighting its capacity to transform emergency medical care. The system's seamless integration image processing shows an enormous step forward in enhancing emergency response capabilities. Drones may now effectively provide lifesaving help by navigating complicated situations autonomously, made possible by the combination of advanced technology. It presents a viable route for improving outcomes in critical conditions by employing AI-driven drones for dynamic emergency medical intervention.

关键词： Measurement Cloud computing Navigation Heuristic algorithms Medical services Routing Real-time systems Convolutional neural networks Medical diagnostic imaging Drones

Machine Learning Designed identification on cervical cancers in patient

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Machine Learning Designed identification on cervical cancers...

Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： M. Sangeetha G. Sasikala K. Anitha S D Prabu Ragavendiran K R Senthil Kumar M. Deivakani Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Mathematics SRM Valliammai Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha Engineering College Thandalam Tamil Nadu India Department of Computer Science and Engineering RVS Technical campus - Coimbatore Coimbatore Tamil Nadu India Department of Mechanical Engineering R. M K. Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

In this paper, we develop an automatic diagnosis model that aims to screen and detect the presence of cervical cancer in women patients. The diagnosis model consists of a series of stages that involves pre-processing, feature extraction and classification of cancer using bees swarm optimization (BSO). The BSO helps to classify the instances from the extracted features in an effective way that does not fall into premature convergence. The fully grown solution provides effective classification of images from the pre-defined medical datasets. The simulation is conducted on python in a high-end computing system to test the efficacy of BSO in classifying the cervical cancer. The validation shows an increasing precision of classifying the instances than other state-of-art methods.

关键词： Computational modeling Machine learning Feature extraction Knowledge discovery Concurrent engineering Particle swarm optimization Cervical cancer

Boosting Time-series Domain Adaptation via A Time-Frequency Consensus Framework

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IEEE Transactions on Artificial Intelligence

IEEE Transactions on Artificial Intelligence 2025年

作者： Yang, Wenmian Ragab, Mohamed Wu, Min Pan, Sinno Jialin Lin, Guosheng Jia, Weijia Chen, Zhenghua Institute of Technology Innovation Institute United Arab Emirates A∗STAR Singapore Department of Computer Science and Engineering Hong Kong Hong Kong Nanyang Technological University School of Computer Science and Engineering Singapore Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China BNUHKBU United International College Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China

Unsupervised Domain Adaptation (UDA) has proven to be effective in addressing the domain shift problem in computer vision. However, compared with visual applications, UDA for time series brings forth additional challenges. Potential domain shifts may have varying impacts on both time and frequency features, rendering conventional UDA methods less effective in this context. To address these challenges, we propose a Time-Frequency Consensus Domain Adaption (TFCDA) framework to enhance UDA methods for time-series data. TFCDA designs a frequency encoder, a trainable Time-Frequency Mapping (TFM), and a consensus loss, building upon conventional UDA methods to boost their performance. The TFM is trained on source domain data to learn the inherent time-frequency feature mapping, while the novel consensus loss ensures consistent feature transfer during UDA in the target domain, effectively reducing domain shifts in both time and frequency, and thus boosting overall performance. Experimental evaluations on four publicly available time-series datasets demonstrate TFCDA's effectiveness in enhancing existing UDA methods for time-series data, highlighting its potential for real-world applications. © 2025 IEEE.

关键词： Transfer learning

Implications of Brewer's Rule in data Warehouse Design

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Implications of Brewer's Rule in Data Warehouse Design

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Ramakrishnan Raman John Benito Jesudasan Peter Atul A Gokhale J. Manikandan E. N. Ganesh C. Srinivasan Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Distributed systems cannot achieve consistency, availability, and partition tolerance, according to Brewer's Rule (CAP theorem). Brewer's Rule affects distributed data warehouses. Brewer's Rule improves data warehouse architecture, according to this study. database research Brewer's Rule reliability, accessibility, and tolerance for partitions. Consistency—the condition that all distributed system nodes access the same data simultaneously—is examined. Distributed transactions, two-phase commit, and consensus methods let data warehouses achieve consistency. A distributed system must be available even if some nodes fail, the study says. Replication, load balancing, and failover provide high availability in data warehouses. Finally, partition tolerance—the need for a distributed system to operate after network partitions—is examined. data sharding, distributed caching, and data replication help data warehousing systems achieve partition tolerance. Brewer's Rule helps improve data warehouse architecture for use cases and business needs. The study also discusses trade-offs companies must make when improving consistency, availability, and partition tolerance. To maintain dependable and performant data warehousing solutions, organizations must balance these trade-offs. Organizations designing and implementing distributed data warehouses must consider Brewer's Rule. Organizations may construct high-performing, dependable data warehouse systems that fulfill their business needs by understanding how consistency, availability, and partition tolerance affect data warehouse architecture.

关键词：

*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

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arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing Elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： data visualization

Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

AGRICULTURAL PLANT CATALOGING AND ESTABLISHMENT OF A data FRAMEWORK FROM UAV-BASED CROP IMAGES BY computer VISION

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arXiv 2022年

作者： Günder, Maurice Ispizua Yamati, Facundo R. Kierdorf, Jana Roscher, Ribana Mahlein, Anne-Katrin Bauckhage, Christian Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Schloss Birlinghoven Sankt Augustin53757 Germany Institute for Computer Science III University of Bonn Friedrich-Hirzebruch-Allee 5 Bonn53115 Germany Holtenser Landstraße 77 37079 Germany Institute for Geodesy and Geoinformation University of Bonn Niebuhrstraße 1a Bonn53113 Germany Department of Aerospace and Geodesy Data Science in Earth Observation Technical University of Munich Lise-Meitner-Straße 9 Ottobrunn85521 Germany

UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on two real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers large-scale spatio-temporal image dataset that in turn can be applied to train further machine learning models including various data layers. The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets. © 2022, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big data

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computers, Materials & Continua 2019年第7期61卷 227-241页

作者： Ning Cao Shengfang Li Keyong Shen Sheng Bin Gengxin Sun Dongjie Zhu Xiuli Han Guangsheng Cao Abraham Campbell College of Computer Information and Engineering Nanchang Institute of TechnologyNanchangChina College of Information Engineering Sanming UniversitySanmingChina School of Data Science and Software Engineering Qingdao UniversityQingdaoChina School of Computer Science and Technology Harbin Institute of TechnologyWeihaiChina Public Teaching Department Qingdao Technical CollegeQingdaoChina School of Computer Science University College DublinDublinIreland

Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human ***-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD *** this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in *** spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD *** based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows *** propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

关键词： Origin-destination(OD)flows semantics analytics complex network big data analysis