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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Wu, Wei-Chieh Chiang, Te-Mei Tsai, Zhi-Ren Asia University Program in Artificial Intelligence College of Information and Electrical Engineering Taichung City Taiwan Asia University Hospital Department of Radiation Oncology Taichung City Taiwan Asia University Department of Computer Science & Information Engineering Taiwan

ISBN: (纸本)9798350363043

Kidney cancer is one of the most common cancers worldwide. The major types of malignant renal tumors are renal cell carcinoma and urothelial carcinoma. Although the majority of renal tumors are malignant, up to 20% are benign, most commonly renal cyst and angiomyolipomas. Ultrasound is the most accessible imaging tool in medical practice, but it highly depends on operator skill, which may lead to high false-negative rate in diagnosis. The purpose of this study was to develop a predictive model for the automated classification of renal tumors on ultrasound images using deep neural network. A total of 880 kidney ultrasound images were used for training and testing. Transfer learning was used to the ten Convolution neural network models. The kidney ultrasound images were classified as benign or malignant tumors. The classification performance of the model was evaluated by sensitivity and specificity. The research results show that VGG18 yielded the best performance, with a sensitivity of 79%, and a specificity of 86%. © 2024 IEEE.

关键词： Ultrasonic imaging

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Using textile capacitive sensors to train synchronized movement of hands

Using textile capacitive sensors to train synchronized movem...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Chen, Shih-Hung Wu, Ze-We Liao, Kuo-Cheng Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Program Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the 'Sunrise and Sunset' game. This game primarily consists of two pieces of regular textile fabric enveloping conductive silver fabric. A microcontroller was employed to extract the sensed capacitive values, and a game named 'Sunrise and Sunset' is designed to complement the slow raising and lowering of both hands. The development of this gaming platform has the potential to offer a novel method of emotional management, particularly in high-stress living environments. It can serve as an effective relaxation tool, aiding individuals in emotional balance, anxiety reduction, and stress alleviation. Simultaneously, this platform can contribute to the promotion of mental well-being, providing an engaging and beneficial means for people to manage their emotions and moods. © 2024 IEEE.

关键词： capacitive sensing emotional stability game relaxation tool silver fabric

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Enhancing Language Learning with Real-Time Sign Language Recognition and Feedback

Enhancing Language Learning with Real-Time Sign Language Rec...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Clapp A. Daniel Nasr M. Liam Yasmina Habchi Saurav Basnet Program of Electrical and Computer Engineering Wentworth Institute of Technology Boston MA

ISBN: (数字)9798331531003

ISBN: (纸本)9798331531010

Despite the significance of Sign Language education, access to resources with immediate feedback remains a challenge. This study aims to assess the effectiveness of an online learning tool offering real-time video feedback, accessible via basic laptops. Leveraging the capabilities of Google’s Media Pipe software and the project’s robust logic, the prototype aims to provide learners with immediate and accurate feedback, addressing the challenge of access to effective resources for American Sign Language (ASL) education. Focusing on the ASL alphabet, computer-vision based learning tools for static and dynamic signs will be analyzed to explore the tool’s capabilities and limitations.

关键词： Hands Sign language Portable computers Prototypes Streaming media Media Real-time systems Software Logic Python

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The Effect of WebRTC Jitter Buffer on Cloud Streaming Game QoE

The Effect of WebRTC Jitter Buffer on Cloud Streaming Game Q...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Naoyasu Kaneko Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This paper considers a cloud streaming game using WebRTC. Since the cloud streaming game consists of a server and a client, the network quality between them affects the game. Among the network quality degradation factors, we focus on delay jitter. We reduce the effect of the jitter by buffering. We conduct a subjective experiment of a single-player action game and evaluate application-level QoS and QoE. We investigate the impact of buffering on the video and audio quality of the user playing the game.

关键词： Degradation Games Quality of service Jitter Streaming media Cloud gaming Delays Quality of experience Servers WebRTC

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The Effect of Vehicles Speed on the Performance of VANET Protocol 1

The Effect of Vehicles Speed on the Performance of VANET Pro...

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

作者： Qasim, Osama A. Mahdi, Dania Mohammed Noori, Mohammed S. Ali, Rabei Raad Sari, Christy Atika Rachmawanto, Eko Hari Northern Technical University Department of Computer Engineering Technology Mosul Iraq Uniersiti Tenaga Nasional Department of Electrical and Electronics Engineering Selangor Malaysia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

At present, ad hoc networks are getting a lot of attention, because they have many features that differ from the rest of the networks and because they are technically advanced. In routing protocols, the Vehicular Ad-hoc network (VANET) differ in performance because each protocol has different variables, such as variable density, speed, and traffic scenarios. In this work, we proposed topology-based routing namely Ad hoc On Demand Distance Vector (AODV) protocol. The AODV protocol has been simulated in case of the movement of vehicles at a fixed velocity at 60km/h, and in case of randomly changing the vehicle's locations in the network. There are three parameters used to investigate the performance of the proposed AODV protocol which are packet delivery ratio, and overheads. The Simulation results show a significant decrease from 0.55 to 0.82 in the packet delivery ratio. On the other hand, the performance of the AODV protocol demonstrates effective communication for the VANETs. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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A Machine Learning Technique for Detection of Diabetes Mellitus 5

A Machine Learning Technique for Detection of Diabetes Melli...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Awoniran, O.M. Oyelami, M.O. Ikono, R.N. Famutimi, R.F. Famutimi, T.I. Bowen University Computer Science Program Iwo Nigeria Obafemi Awolowo University Department of Computer Science and Engineering Ile-Ife Nigeria

ISBN: (纸本)9781509064229

The need for early detection of diabetes mellitus has led to the development of various intelligent systems using machine learning and artificial intelligence for the recognition of the presence of the disease. However, most of the techniques have yielded a comparatively lower accuracy. This research applied data science techniques to a dataset of diabetes mellitus to improve the accuracy of the prediction of the disease. This was achieved by pre-processing the data with dummy categories and applying principal components analysis for reduced dimensionality. Support vector machine, random forest classifier, and deep neural networks were then used to train the system. Support vector machine, random forest classifier, and deep neural networks yielded accuracies of 0.76, 0.77, and 0.89 respectively. Correspondingly, deep neural networks yielded the highest accuracy. The study concluded that better pre-processing will improve the accuracy of machine learning algorithms in the prediction of diabetes mellitus. © 2022 IEEE.

关键词： Deep neural networks

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Temperature-Dependent Hydrogen Modulations of Ultra-Scaled a-IGZO Thin Film Transistor Under Gate Bias Stress

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2024年 5卷 9-16页

作者： Aslam, Muhammad Chang, Shu-Wei Chuang, Min-Hui Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan701401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results. © 2020 IEEE.

关键词： Oxide semiconductors

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Prior-Guided Parallel Residual Bi-Fusion Network in USV Obstacle Detection

Prior-Guided Parallel Residual Bi-Fusion Network in USV Obst...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Chih-Chung Yang, Sophia Hou, Xiu-Yu Jhang, Yu-An Institute of Data Science National Cheng Kung University Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Taiwan

ISBN: (纸本)9798350324174

In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F-avg score of 0.514. © 2023 IEEE.

关键词： Unmanned surface vehicles

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