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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Goktug C. Ozmen Christopher J. Nichols Lan Lan Emily Moise Christopher Sugino Alper Erturk Omer T. Inan School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA BioEngineering Graduate Program Georgia Institute of Technology Atlanta GA USA Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA Coulter Department of Biomedical Engineering School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Non-invasive vibration measurements from the knee offer a convenient and affordable alternative to benchtop or biomechanics lab joint health monitoring systems. Recently, joint acoustic emissions (JAEs) measured from the knee were shown to be an indicator of knee health. However, the origin of JAEs is still not fully understood, which limits its acceptance and use by clinical experts. In this proof-of-concept study, rather than relying on the movements of the knee and corresponding frictional rubbing of internal surfaces to produce vibrations, we propose using an active vibration sensing approach with a known vibration source interrogating the knee. We aim to elucidate the linkage between knee vibration characteristics and structural changes in the joint following injuries. We measured tibial vibration responses of two participants using a laser vibrometer system to quantify the frequency band where the most repeatable tibial vibration measurement can be taken. Subsequently, a custom-designed wearable system measured mid-activity tibial vibration characteristics from four participants (five healthy knees and three knees with prior acute injury) during unloaded knee flexion-extensions. An active sensing knee health score was defined as the ratio of the changes in low- to high-frequency response during flexion-extension. Since changes in the boundary of tibia would alter low-frequency response more than high frequency response, we found that increased knee laxity with acute injuries resulted in an increased active sensing knee health score. Our findings demonstrate the potential of active vibration sensing as an interpretable, computationally inexpensive alternative to JAEs for wearable knee health assessment.

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm 1

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Jirayu Petchhan Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science Dept. of Electrical Engineering National Taiwan University of Science & Technology Taiwan R. O. C. Department of Computer Science and Information Engineering National Taipei University of Technology Taiwan R. O. C.

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle.

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Based-on computer Vision Applications for Bus Stop Passenger Detection System

Based-on Computer Vision Applications for Bus Stop Passenger...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Yu-Cheng Chang Hua-Wen Tsai Chao-Yi Huang Zong-Rong Wu Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Taiwan

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the "Bus Stop Passenger Detection System". Taking the object detection of "Wheelchairs" into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient.

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Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments

arXiv

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arXiv 2024年

作者： Piao, Yinhua Lee, Sangseon Lu, Yijingxiu Kim, Sun Department of Computer Science and Engineering Seoul National University Korea Republic of Institute of Computer Technology Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more semantically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical semantic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the input graph to generate proxy predictions on local environments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Particularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively. © 2024, CC BY.

关键词： Semantics

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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A Heuristic Approach using Block Chain to Fight Novel COVID-19 during an Election 6

A Heuristic Approach using Block Chain to Fight Novel COVID-...

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6th IEEE International Conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021

作者： Nath, Ira Biswas, Pratnadeep Ganguly, Kankana Singh, Dharmpal Islam, Sardar M.N. Quang Cuong, Ton Pal, Souvik JIS College of Engineering Computer Science Engineering Kalyani India Victoria University ISILC and Decision Sciences and Modeling Program Australia Vietnam National University Hanoi Viet Nam Global Institute of Management and Technology Computer Science Engineering Krisnanagar India

ISBN: (纸本)9781665417846

India is a highly densely populated country. India is facing a very serious challenge due to the novel COVID-I9 outbreak. In this situation, the economic state of the people is very unstable. Blockchain technology proves helpful to subdue the situation. A voting system with ballot papers is quite tuff now. It is very costly for the government. The pandemic may also spread very quickly. The system of developing ledger serves the idea of developments of a new e-voting system that is economical, easy to use, and highly secure. This paper proposes ways and solutions to design an e-voting system using blockchain technology. The paper's primary goal is to develop a highly secure e-voting technique using which people can cast their valuable votes from their home, working place - anywhere from the world. This e-voting technique is least costly, highly secure, and able to prevent further spreading of any infectious disease in the near future. In this paper, an e-voting system using blockchain has been designed to overcome all these difficulties mentioned above during traditional voting using ballot. The significance of the proposed work is to design of a highly safe and least costly e- voting system using blockchain. Using this system the people can cast their vote easily, securely and without wasting any time. Casting vote will be now just from few clicks away and from any place with a stable internet connection. © 2021 IEEE.

关键词： COVID-19

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Using Real-Time Integrated computer Vision and Deep Learning for Advanced Factory Safety

Using Real-Time Integrated Computer Vision and Deep Learning...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Yu-Cheng Chang Chao-Yi Huang Fang-Yuan Yang Wei Chien Zong-Rong Wu School Art and Design Sanming University Fujian China Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as “normal operations” and “unexpected incidents” to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety.

关键词： computer vision Prevention and mitigation Employment Real-time systems Production facilities Safety Internet of Things

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Performance Analysis of Economic Model Predictive Control on a Solar-Powered Direct Contact Membrane Distillation System

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IFAC-PapersOnLine 2022年第33期55卷 13-19页

作者： Hichem Bessafa Ghania Khodja Chakir Messaoud Taous-Meriem Laleg-Kirati Electrical and Computer Engineering Program. Computer Electrical and Mathematical Science and Engineering (CEMSE) division. King Abdullah University of Science and Technology (KAUST) Ecole Polytechnique of Algiers Algeria National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

This paper proposes an economic model predictive control (EMPC) design for a Direct Contact Membrane Distillation powered by a solar collector system which aims at enhancing its economical performances. A differential algebraic equations-based model is used for the design of the EMPC control. Moreover, a nonlinear observer is developed for the estimation of the unmeasured state. A neural network is proposed to predict the unknown solar irradiance for future horizon where a solar model provides temperature predictions. The proposed control design has been validated in simulation using data provided by a partial differential equation-based model mimicking the real plant.

关键词： Direct contact membrane distillation Economic model predictive control differential algebraic equations nonlinear observer Long-short term memory neural network

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NIR-Responsive Methotrexate-Modified Iron Selenide Nanorods for Synergistic Magnetic Hyperthermic, Photothermal, and Chemodynamic Therapy

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ACS Applied Materials and Interfaces 2024年第20期16卷 25622-25636页

作者： Thirumurugan, Senthilkumar Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Dhawan, Udesh Liu, Wai-Ching Wang, An-Ni Liu, Xinke Hsiao, Michael Tseng, Ching-Li Chung, Ren-Jei No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong Hong Kong New Territories999077 Hong Kong Scrona AG Grubenstrasse 9 Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei10608 Taiwan

Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) were designed for use in magnetic hyperthermic, photothermal, and chemodynamic therapy (MHT/PTT/CDT) for breast cancer. To illustrate their efficacy, FeSe2 NRs were modified with the chemotherapeutic agent methotrexate (MTX). MTX-modified FeSe2 (FeSe2-MTX) exhibited excellent controlled drug release properties. Fe2+ released from FeSe2 NRs induced the release of •OH from H2O2 via a Fenton/Fenton-like reaction, enhancing the efficacy of CDT. Under alternating magnetic field (AMF) stimulation and 808 nm laser irradiation, FeSe2-MTX exerted potent hyperthermic and photothermal effects by suppressing tumor growth in a breast cancer nude mouse model. In addition, FeSe2 NRs can be used for magnetic resonance imaging in vivo by incorporating their superparamagnetic characteristics into a single nanomaterial. Overall, we presented a novel technique for the precise delivery of functional nanosystems to tumors that can enhance the efficacy of breast cancer treatment. © 2024 The Authors. Published by American Chemical Society

关键词： Magnetic resonance imaging

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