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International SoC Design Conference, ISOCC

作者： En-Chi Yang Suz-Ting Wang Kusn-Lin Liu Wen-Ho Juang Ming-Hwa Sheu How-Chiun Wu Shin-Chi Lai Dept. Electronics Engineering / Dept. Doctor’s Program of Smart Industry Technology Research and Design National Yunlin University of Science and Technology / National Formosa University Douliu Taiwan / Huwei Taiwan Dept. Doctor’s Program of Smart Industry Technology Research and Design National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Natural Biotechnology Nanhua University Chiayi Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

This paper presents a design for a fast measurement electrochemical impedance spectroscopy (EIS) system, which differs from the traditional single-frequency point calculation approach. The proposed system is capable of simultaneously measuring 23 frequency points within the frequency range of 1Hz-10KHz. Additionally, it has an impedance measurement range of 100$\omega$ - 1M$\omega$. The experimental results demonstrate that the average MRE and PAE are both below 0.4% and 1°, with conditions of R=9940 $\omega$ and C=1.02uF.

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Ideation of Engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students

Ideation of Engineering Solutions for Sustainable Developmen...

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IEEE Region 10 International Conference TENCON

作者： Kodchakorn Krutphong Natt Leelawat Jing Tang Eri Ota Rie Murakami Suzuki Yuki Taoka Proadpran Punyabukkana Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University and Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University and the Learning Innovation Center Chulalongkorn University Thailand

A collaborative course called Global Awareness for technology Implementation was conducted by the Faculty of Engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation.

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High-Order Cardiomyopathy Human Heart Model and Mesh Generation

High-Order Cardiomyopathy Human Heart Model and Mesh Generat...

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2021 Computing in Cardiology, CinC 2021

作者： Mohammadi, Fariba Shontz, Suzanne M Linte, Cristian A Department of Mechanical Engineering University of Kansas LawrenceKS United States Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Chester F Carlson Center for Imaging Science Rochester Institute of Technology RochesterNY United States Department of Biomedical Engineering Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9781665479165

Faithful, accurate, and successful cardiac biomechanics and electrophysiological simulations require patient-specific geometric models of the heart. Since the cardiac geometry consists of highly-curved boundaries, the use of high-order meshes with curved elements would ensure that the various curves and features present in the cardiac geometry are well-captured and preserved in the corresponding mesh. Most other existing mesh generation techniques require computer-aided design files to represent the geometric boundary, which are often not available for biomedical applications. Unlike such methods, our technique takes a high-order surface mesh, generated from patient medical images, as input and generates a high-order volume mesh directly from the curved surface mesh. In this paper, we use our direct high-order curvilinear tetrahedral mesh generation method [1] to generate several second-order cardiac meshes. Our meshes include the left ventricle myocardia of a healthy heart and hearts with dilated and hypertrophic cardiomyopathy. We show that our high-order cardiac meshes do not contain inverted elements and are of sufficiently high quality for use in cardiac finite element simulations. © 2021 Creative Commons.

关键词： Heart

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LOCI: A Mobile QA System with Multimodal Motivation Scheme for Local Intent Questions in Dynamic Social Networks 91

LOCI: A Mobile QA System with Multimodal Motivation Scheme f...

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91st IEEE Vehicular technology Conference, VTC Spring 2020

作者： Ali, Imad Chang, Ronald Y. Hsu, Cheng-Hsin Lee, Chi-Han Institute of Information Science Academia Sinica Taiwan International Graduate Program Social Networks and Human-Centered Computing Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9781728152073

Recent research has shown that people are frequently searching for information with local intent. The existing systems fail to answer this class of questions as they are not mainly designed to handle it. In this paper, we propose LOCI, a mobile question answer system with a multimodal motivation scheme for local intent questions where users provide timely and accurate answers in a dynamic social network. LOCI mines users' shared and contextual information to identify the most relevant users for each question. The proposed multimodal motivation scheme exploits users' social ties and monetary rewards to raise their basic motivation levels. LOCI assigns questions to the best answerers via our three proposed algorithms: (i) LOCI-RT, which immediately assigns newly arrived questions/users to available users/questions, (ii) LOCI-LO, which uses short-time batches for locally optimal questions-users assignment, and (iii) LOCI-AD, which assigns short- and long-deadline questions immediately and via batching, respectively. We conduct: (i) a survey to support our assumptions regarding the proposed multimodal motivation scheme, and (ii) trace-driven experiments to evaluate the performance of the three proposed LOCI's algorithms. © 2020 IEEE.

关键词： Motivation

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Data Analysis and Visualization Technique for Exploring of Factor Associated with the Incidence of Complication in Pregnancy and Newborn 17

Data Analysis and Visualization Technique for Exploring of F...

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17th International Conference on Electrical Engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON 2020

作者： Prappre, Tagoon Vasupongayya, Sangsuree Liabsuetrakul, Tippawan Prince of Songkla University Management of Information Technology Program Hat Yai Thailand Prince of Songkla University Department of Computer Engineering Hat Yai Thailand Prince of Songkla University Department of Medicine Hat Yai Thailand

ISBN: (纸本)9781728164861

To assist the scientists in understanding the large amount of health data, this project aims to develop a prototype for visualizing the data related to the factors associated with the incidence of complication in pregnancy and newborn. The data for this work is from the improving health system response through epidemiological surveillance in improving maternal and newborn health and survival: field testing in Thailand project. The prototype converts the statistical significance level obtained from the data analysis to 2D graphics using the iconographic data presentation technique. And, the hierarchical data representation technique is used by the prototype to represent the relationship between the statistical significance level and the factors and the complication in pregnancy and newborn. The prototype evaluation results by the statistical expert show that the data presentation in graphic formats enables the researcher to better understand the results and to better interpret the results. The data representation technique in this work can be applied to other area of researches. © 2020 IEEE.

关键词： Data visualization

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Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve

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IEEE Journal of Translational Engineering in Health and Medicine 2025年 13卷 286-298页

作者： Lin, Yuan-Jin Chen, Shih-Lun Mao, Yi-Cheng Chen, Tsung-Yi Peng, Cheng-Hao Tsai, Tzu-Hsiang Li, Kuo-Chen Chen, Chiung-An Tu, Wei-Chen Abu, Patricia Angela R. National Cheng Kung University Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan City701401 Taiwan Chung Yuan Christian University Department of Electronic Engineering Taoyuan City32023 Taiwan Taoyuan Chang Gang Memorial Hospital Department of Operative Dentistry Taoyuan City33305 Taiwan Feng Chia University Department of Electronic Engineering Taichung city40724 Taiwan Chung Yuan Christian University Taoyuan City32023 Taiwan Chung Yuan Christian University Department of Information Management Taoyuan City32023 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243303 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan city701401 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

Objective: Extraction of the third molar of the mandible is one of the most common oral surgical procedures. Preoperative monitoring and assessment are crucial to mitigate neurological risks. Identifying whether the third molar in the mandible compresses the inferior alveolar nerve still relies on dental professionals, a task that is repetitive and time-consuming. Thus, the primary objective is to utilize dental panoramic radiography for image processing and classify whether the third molar compresses the inferior alveolar nerve, aiming to reduce the demand for CT images in symptom diagnosis and mitigate the risks associated with high-dose radiation. technology or Method: This study proposes an innovative dental panoramic radiography segmentation technique to locate the third molar position. Subsequently, an innovative edge masking enhancement method is used to extract features of the inferior alveolar nerve and the third molar. Moreover, a transformer-based image detection model to consider whether the third molar compresses the inferior alveolar nerve. Results: The third molar position localization method achieved an accuracy rate of 97.92%, compared to recent research at least improved by 3.6% accuracy. Subsequently, innovative edge masking and image enhancement methods improve classification accuracy by 4.3%, when supplemented with computed tomography scan images for further evaluation, the maximum accuracy reached 98.45%, representing a 4.5% improvement compared to previous studies. Conclusions: The third molar position detection results will impact the identification of the inferior alveolar nerve compressed by the third molar. Through the innovative edge region segmentation algorithm can effectively distinguish this object, and the overall evaluation accuracy can be improved by approximately 3.8%. © 2013 IEEE.

关键词： Neurophysiology

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Unveiling the frontiers of deep learning: Innovations shaping diverse domains

arXiv

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arXiv 2023年

作者： Ahmed, Shams Forruque Alam, Md. Sakib Bin Kabir, Maliha Afrin, Shaila Rafa, Sabiha Jannat Mehjabin, Aanushka Gandomi, Amir H. School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya Selangor Darul Ehsan 47500 Malaysia Department of Mathematics & Physics North South University Dhaka1229 Bangladesh Science and Math Program Asian University for Women Chattogram4000 Bangladesh School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia Óbuda University Budapest1034 Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

Deep learning (DL) allows computer models to learn, visualize, optimize, refine, and predict data. To understand its present state, examining the most recent advancements and applications of deep learning across various domains is essential. However, prior reviews focused on DL applications in only one or two domains. The current review thoroughly investigates the use of DL in four different broad fields due to the plenty of relevant research literature in these domains. This wide range of coverage provides a comprehensive and interconnected understanding of DL’s influence and opportunities, which is lacking in other reviews. The study also discusses DL frameworks and addresses the benefits and challenges of utilizing DL in each field, which is only occasionally available in other reviews. DL frameworks like TensorFlow and PyTorch make it easy to develop innovative DL applications across diverse domains by providing model development and deployment platforms. This helps bridge theoretical progress and practical implementation. Deep learning solves complex problems and advances technology in many fields, demonstrating its revolutionary potential and adaptability. CNN-LSTM models with attention mechanisms can forecast traffic with 99% accuracy. Fungal-diseased mango leaves can be classified with 97.13% accuracy by the multi-layer CNN model. However, deep learning requires rigorous data collection to analyze and process large amounts of data because it is independent of training data. Thus, large-scale medical, research, healthcare, and environmental data compilation are challenging, reducing deep learning effectiveness. Future research should address data volume, privacy, domain complexity, and data quality issues in DL *** Codes 68T07 © 2023, CC BY.

关键词： Natural language processing systems

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Economics of Semantic Communication System: An Auction Approach

arXiv

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arXiv 2022年

作者： Liew, Zi Qin Du, Hongyang Yang, Wei Lim, Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Kim, Dong In Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method. Copyright © 2022, The Authors. All rights reserved.

关键词： Commerce

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Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk

Investigating Author Research Relatedness through Crowdsourc...

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International Conference on computer Supported Cooperative Work in Design

作者： António Correia Dennis Paulino Hugo Paredes Diogo Guimarães Daniel Schneider Benjamim Fonseca UTAD Quinta de Prados INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha Omaha NE USA Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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