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On the Feasibility of RIS-enabled Cooperative Driving

On the Feasibility of RIS-enabled Cooperative Driving

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IEEE Vehicular Networking Conference (VNC)

作者： Michele Segata Paolo Casari Marios Lestas Dimitrios Tyrovolas Taqwa Saeed George Karagiannidis Christos Liaskos Department of Information Engineering and Computer Science University of Trento Italy Dept. of Electrical Engineering Frederick University Cyprus Dept. of Electrical Engineering Aristotle University of Thessaloniki Greece School of Information Technology Halmstad University Sweden Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon Dept. of Computer Science and Engineering University of Ioannina & FORTH Greece

Future cooperative autonomous vehicles will require high-performance communication means to support functions such as cooperative maneuvering and cooperative perception. The high-bandwidth requirements of these functions can be met through mmWave communications, whose utilization is often hindered by the harsh propagation conditions of typical vehicular environments. A solution to this problem is the use of reconfigurable intelligent surfaces (RISs), which enable the reflection of signals in a configurable direction, and have recently gained attention in the vehicular domain. In this paper, we provide an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communications. Specifically, we utilize CoopeRIS, a simulation framework for RISs integrated into the Plexe/Veins/SUMO ecosystem that we develop as further contribution and will release to the public.

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A Framework for Human Motion Estimation using IMUs in Human-Robot Interaction

A Framework for Human Motion Estimation using IMUs in Human-...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Gizem Ateş Martin Fodstad Stølen Erik Kyrkjebø Dept. of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway

ISBN: (纸本)9781728119496

The field of human-robot interaction (HRI) covers a wide range of interactions between humans and robots in various settings. In most cases, both the human and the robot should know the status of each other and interchange data accordingly. Particularly, informing the robot about the human poses and gestures is critical for safe and reliable interaction. We present an open-source inertial measurement unit (IMU)-based human motion estimation framework designed for HRI applications which can be used with multiple robotic systems. The presented framework is developed as a Robot Operating System (ROS) package and takes on a bridge role between the human and the robot by utilizing the estimated human motions to produce a desired robot goal pose. Although there are different human motion tracking systems that are used in gaming, film making, industrial and medical applications today, they are either quite costly, tedious to set up or system dependent. Also, those systems have some limitations to be used in industrial environments such as suffering from occlusion and obstacles between the tracker system and the human, sensitive to light changes etc., and require long calibration steps before each use. The availability of a versatile human motion estimation framework that can be easily used in different HRI scenarios is handy for industrial applications. Some examples of usages of the proposed framework both in simulation and real-world applications are demonstrated in this paper. We aim our package to be a useful boosting tool to develop IMU-based human motion estimated HRI applications and auxiliary templates to develop more complex HRI scenarios for research and development purposes.

关键词： Measurement units Tracking Service robots Motion estimation Operating systems Human-robot interaction Medical services

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Federated Learning for the Metaverse: A Short Survey

Federated Learning for the Metaverse: A Short Survey

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Gokul Yenduri Dasaradharami Reddy K Gautam Srivastava Supriya Y Ramalingam M Thippa Reddy Gadekallu Feras M. Awaysheh Vellore Instituite of Technology Vellore Tamil Nadu India Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Delta Research Center Institute of Computer Science Tartu University Tartu Estonia

The Metaverse, a 3-dimensional virtual realm mirroring real-world objects, promises transformative experiences. Its potential is tempered by data collection, confidentiality, and privacy concerns. In tandem with AI-enabled technologies, edge computing can provide a practical solution to overcoming many of these challenges. This paper argues that combining AI-enabled technologies with edge computing-specifically via federated learning (FL) can address these challenges. FL, as a privacy-centric distributed machine learning (ML) approach, enables knowledge sharing among Metaverse clients without compromising user data. However, while FL posits potential resolutions for the Metaverse, a discernible lacuna remains in the comprehensive study of its overarching consequences. The literature misses an extensive study of adopting these new deployment architectures, giving it significant research impact. This paper bridges this knowledge gap, exploring the symbiosis between the Metaverse and FL. We first introduce the foundational concepts of both domains and detail enabling technologies such as digital twins, the Internet of Things, brain-computer interfaces, blockchain, and extended reality. Next, we delve into practical applications of FL within the metaverse, spanning sectors like healthcare, education, e-commerce, gaming, and the military. Finally, the paper highlights the key challenges and future directions for integrating FL within the metaverse ecosystem.

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Efficient Models for Detecting Monkeypox using Skin Lesion Images

Efficient Models for Detecting Monkeypox using Skin Lesion I...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Neelam Sunda Divya Sharma Sujith Battu Rewanth Nayak Banoth Rahul Monika Department of Computer Science Kanoria PG Mahila Mahavidyalaya Jaipur India Department of Computer Science Kanoria PG Mahila Mahavidhyalaya jaipur India Dept. of Software Engineering Delhi Technological University New Delhi India Department of Applied Physics Delhi Technological University New Delhi India Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi

Monkeypox is predicted to be the next pandemic and is spreading over African countries like water under a mat. The study basically investigates three deep learning imaging models for the identification of monkeypox from skin lesion photos from the MSID dataset. The motto is to define a computer-based technique to identify monkeypox from just its image, making it cheaper and faster with accurate results. The study achieved the highest accuracy of 99.37% as compared to previous works related to Resnet 50. The limits and complexity of the procedures in real-world situations are also clearly addressed.

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks

Diving into Brain Complexity: Exploring Functional and Effec...

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International Conference on Emerging Technologies, ICET

作者： Abu Bakar Siddique Hassan Bin Khalid Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches.

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A Machine-Learning Based IoT Smart Home System to Detect and Reduce Urban Insecurity in Uganda: A Case of Kampala Metropolitan Area

A Machine-Learning Based IoT Smart Home System to Detect and...

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International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD)

作者： Edward Miiro Ismael Kato Department of Computer Science & Engineering Faculty of Computing & Informatics Makerere University Business School Kampala Uganda Dept. of Applied Computing & Information Technology Faculty of Computing & Informatics Makerere University Business School Kampala Uganda

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.

关键词： Temperature sensors Urban areas Prototypes Smart homes Machine learning Software Pins

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Ship Data Clustering for Improved Fuel Efficiency Optimization Decision Support Systems

Ship Data Clustering for Improved Fuel Efficiency Optimizati...

引用

OCEANS

作者： Caitlin Piercey Matthew Hamilton Brian Veitch Joshua Barnes Xianta Jiang Dept. of Computer Science Memorial University of Newfoundland St. John's Canada Dept. of Engineering and Applied Sciences Memorial University of Newfoundland St. John's Canada Ocean Coastal and River Engineering National Research Council Canada St. John's Canada

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

In this paper, we propose a hybrid clustering algorithm based on hierarchical clustering in combination with string clustering, similar to that used in clustering genetic sequences, to extract clusters of operational activities from ship data. After extracting clusters (n=9), the distribution and summary statistics of each cluster are compared to Canadian Coast Guard provided operational definitions to obtain context from the clusters. The clustered data is used as the input to a fuel flow prediction model, which will in turn form the basis of an on-board decision support system for marine vessels with the goal of reducing fuel consumption. Finally, we compare the results of fuel flow predictions with models trained on a) different clusters and b) the full dataset, to determine the usefulness of these clusters in improving the performance of a fuel-optimization decision support system for on-board marine vessel use.

关键词： Decision support systems Oceans Clustering algorithms Predictive models Genetics Data models Fuels Data mining Marine vehicles Optimization

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Design and Implement Driveway Door Access Control by Using VLC and RFID Technology

Design and Implement Driveway Door Access Control by Using V...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Huang Hung Yong-Yi Fanjiang Cheng-Yen Li Sheng-Wei Chiu Tzu-Chieh Hung Meng-Huan Chiang Dept. of Information Technology Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of Computer Science and Information Engineering Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Dept. of Vehicle Engineering Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of International High School Kang Chiao International School New Taipei City Taiwan

In recent years, the application of radiofrequency identification technology in smart access control systems has become a trend. Users only need to take out the RFID electronic key and approach the sensor of the gate to open the gate through signal induction. For housewives who always have a lot of bags and bags in their hands, there is no need to free up one hand to find the key to open the door and avoid being in a hurry. However, it is relatively inconvenient for drivers. Instead, it is much more convenient to use wireless controllers or license plate recognition. This study proposes a system that combines RFID with optical communication technology and vehicle headlights to open the garage door with a light code.

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Low-cost Smart Glasses for People with Visual Impairments

Low-cost Smart Glasses for People with Visual Impairments

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Nayana B R Yogesh Raju N R Abhav S Velidi Dept. of Computer Science Engineering M S Ramaiah Univesrity of Applied Sciences Bangalore Karnataka India Dept. of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bangalore Karnataka India Dept. of Electronics and Commuincation Engineering National Institute of Technology Suratkal Karnataka India

An efficient visual aid is essential for visually impaired candidate. At the same instance it has to be simple, robust and cost effective. However, regardless of being expensive, it is challenging to incorporate high end artificial intelligence into a compact device to serve the purpose. This paper presents a prototype of low-cost smart glasses for visually impaired individuals. Also, a brief survey on the cost and features of the goggles available in the market are included. In this work a prototype is developed integrating Raspberry Pi module, camera, sensors and a goggles. The captured images are processed using object recognition models and its corresponding text is read aloud using google text to translator. Subsequently, a comparative analysis of object recognition model is explored and discussed. Essential experimental results are incorporated along with cost involved for the prototype development.

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Enhancing Customer Experience, and Brand Awareness through Familiar Gameplay in Advergames

Enhancing Customer Experience, and Brand Awareness through F...

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computer science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Navid Khezrian Forouzan Farzinnejad Nilufar Baghaei dept. Electrical Engineering and Computer Science Coburg University of Applied Sciences Coburg Germany dept. Information Technology and Electrical Engineering University of Queensland Brisbane Australia

The marketing field has been looking for more creative and effective ways to attract customers. Advergames have recently attracted a lot of attention in increasing brand awareness and attracting wider groups of audiences. Research studies have indicated that customers have a more positive attitude toward a brand and stronger purchase intentions when playing advergames with brand interactivity rather than without brand interactivity. Despite the increased use of advergames as a brand promotion strategy by a range of well-known brands, there is still a limited understanding of several issues related to the effective use of such games. In this paper, we conducted one study (n= 242) held in one exhibition to examine familiar gameplay in advergames and its effectiveness in attracting and engaging more people. Our results showed that the presence of familiar gameplay in advergames is an essential feature in attracting more people, enhancing their user experience, and increasing brand awareness.

关键词： computer science Games Data engineering User experience

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