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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Chi, Chun-Nan Hsu, Jia-Lien Hung, Chi-Huang Hsu, Chia-Hsun Phuong, To Nguyen Tan Juan, Wei-Chih Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Department of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Dept. of Information Technology Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of International High School Kang Chiao International School New Taipei City Taiwan Dept. Electronical Engineering Lee-Ming Institute of Technology New Taipei City Taiwan Department of Information Management Hwa Hsia University of Technology New Taipei City Taiwan

ISBN: (纸本)9798331521165

This study introduces "InstaSight," a system enabling visually impaired individuals to engage with social media focused on visual content. InstaSight integrates a Single Board computer, head-mounted webcam, and Bluetooth headset for seamless voice interactions. By utilizing the APIs of social media, speech recognition, and Large Language Models (LLMs), users can post photos and videos with voice-to-text captions and listen to comments converted from text or images. This system fosters social engagement, helping blind users connect and share experiences, improving their psychological well-being. © 2025 IEEE.

关键词： Social networking (online)

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Fq-Sat: A Fuzzy Q-Learning-Based Mpquic Scheduler for Data Transmission Optimization

SSRN

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SSRN 2023年

作者： Nguyen, Thanh Trung Vu, Minh Hai Dinh, Thi Ha Ly Nguyen, Thanh Hung Le Nguyen, Phi Nguyen, Kien School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam Faculty of Computer Science Phenikaa University Hanoi Viet Nam Institute for Advanced Academic Research Chiba University Japan Graduate School of Engineering Chiba University Japan

In the 5G and beyond era, multipath transport protocols, including MPQUIC, are necessary in various use cases. In MPQUIC, one of the most critical issues is efficiently scheduling the upcoming transmission packets on several paths considering path dynamicity. To this end, this paper introduces FQ-SAT - a novel Fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization, including download time, in heterogeneous wireless networks. Different from previous works, FQ-SAT combines Q-learning and Fuzzy logic in an MPQUIC scheduler to determine optimal transmission on heterogeneous paths. FQ-SAT leverages the self-learning ability of reinforcement learning (i.e., in a Q-learning model) to deal with heterogeneity. Moreover, FQ-SAT facilitates Fuzzy logic to dynamically adjust the proposed Q-Learning model's hyper-parameters along with the networks' rapid changes. We evaluate FQ-SAT extensively in various scenarios in both simulated and actual networks. The results show that FQ-SAT reduces the download time by 2.1%-13.5% in simulation and by 2.2%-5.1% in actual network, compared to state-of-the-art MPQUIC schedulers. © 2023, The Authors. All rights reserved.

关键词： Fuzzy logic

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Yanfi Yanfi Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Food industry Companies Classification algorithms

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

Loss Distillation via Gradient Matching for Point Cloud Comp...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Fangzhou Lin Haotian Liu Haoying Zhou Songlin Hou Kazunori D Yamada Gregory S. Fischer Yanhua Li Haichong K. Zhang Ziming Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Graduate School of Information Sciences Tohoku University Sendai Japan Dell Technologies Hopkinton MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/IROS2024-LossDistillationWeightedCD.

关键词： Point cloud compression Training Three-dimensional displays Laser radar Benchmark testing Iterative algorithms Tuning Optimization Intelligent robots Convergence

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SBS_FD: Fault Diagnosis of Harmonic Reducers Based on Symmetrized Dot Pattern, Bag of Visual Word, and Support Vector Machine Jointed Method

SBS_FD: Fault Diagnosis of Harmonic Reducers Based on Symmet...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Xinming Han Zhuo Long Xiaoguang Ma Jie Jia Wenjie Chen Wenjie Lin School of Computer Science and Engineering Northeastern University Shenyang China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China Foshan Graduate School Northeastern University Foshan China College of Information and Engineering Northeastern University Shenyang China Lancheng Lab Midea Group Foshan China Guangdong Jiya Precision Machinery Technology Co. Ltd Foshan China

In order to realize fault diagnosis of harmonic reducers in real industrial scenarios, a fault diagnosis method based on image visual information, i.e., SBS_FD is proposed. Firstly, Symmetrized Dot Pattern images are obtained based on vibration signals, and Scale Invariant Feature Transform method is used to extract image information, wherein the mappings between fault signals and visual information are achieved. Then the bag of visual word and space pyramid matching method are used to process the visual information. Finally, the support vector machine is used for fault diagnosis. Comprehensive experimental results show that the diagnostic accuracy of four fault states can be up to 98% at all speeds, using proposed SBS_FD method, illustrating superior performance over other machine learning methods.

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ACO With Reinforcement Learning Applied to Rescues Operations on Urban Forests

ACO With Reinforcement Learning Applied to Rescues Operation...

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Congress on Evolutionary Computation

作者： Cláudio Alves Israel Mendonça Vanessa de Almeida Guimarèes Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan COPPEAD Graduate Business School Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm.

关键词： Ant colony optimization Forests Smart cities Helicopters Decision making Reinforcement learning Evolutionary computation

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Ergometer-Based Lower Limb Rehabilitation Robot for Post-Stroke Hemiplegic Patients with Voluntary Movement Induction using Muscle Synergy

Ergometer-Based Lower Limb Rehabilitation Robot for Post-Str...

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Conference on Human System Interactions, HSI

作者： Daisuke Chugo Yuya Miyazaki Honoka Kubo Satoshi Muramatsu Sho Yokota Jin-Hua She Keio Ishiguro Hiroshi Hashimoto Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Engineering Kwansei Gakuin University Sanda Hyogo Japan School of Information Science and Technology Tokai University Hiratsuka Kanagawa Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Health Sciences Tokyo University of Technology Kamata Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

This paper proposes a lower limb rehabilitation robot for normal walking in post-stroke hemiplegic patients. Ergometers are commonly used for lower limb rehabilitation, but their use has depended on the experience of physiotherapists. Therefore, this paper proposes a new rehabilitation robot based on an ergometer that leads the patient to voluntary muscle activity and learns how to use the muscles required for normal walking. The paper first compares how pedestrians walk between a normal person and a hemiplegic patient using muscle synergy analysis to identify the lower limb usage required for normal gait. The paper then proposes a method to approach the use of the lower limb required for normal gait by changing the load depending on the position of the pedals when a hemiplegic patient uses the ergometer. This will enable stroke hemiplegic patients to train for normal gait by pedaling the ergometer. The above proposed method has been implemented in our prototype robot and the effectiveness of our proposed method has been confirmed in experiments with post-stroke hemiplegic patients as subjects.

关键词： Legged locomotion Training Pedestrians Prototypes Muscles Stroke (medical condition) Assistive robots

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New Level-Set-Based Shape Recovery Method and its application to sparse-view shape tomography 4

New Level-Set-Based Shape Recovery Method and its applicatio...

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4th International Conference on Digital Medicine and Image Processing, DMIP 2021

作者： Ali, Haytham A. Kudo, Hiroyuki Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tennoudai 1-1-1 Tsukuba305-8573 Japan Department of Mathematics Faculty of Science Sohag University Sohag82524 Egypt

ISBN: (纸本)9781450386487

The recovery of shapes from a few numbers of their projections is very important in Computed tomography. In this paper, we propose a novel scheme based on a collocation set of Gaussian functions to represent any object by using a limited number of projections. This approach provides a continuous representation of both the implicit function and its zero level set. We show that the appropriate choice of a basis function to represent the parametric level-set leads to an optimization problem with a modest number of parameters, which exceeds many difficulties with traditional level set methods, such as regularization, re-initialization, and use of signed distance function. For the purposes of this paper, we used a dictionary of Gaussian function to provide flexibility in the representation of shapes with few terms as a basis function located at lattice points to parameterize the level set function. We propose a convex program to recover the dictionary coefficients successfully so it works stably with only four projections by overcoming the issue of local-minimum of the cost function. Finally, the performance of the proposed approach in three examples of inverse problems shows that our method compares favorably to Sparse Shape Composition (SSC), Total Variation, and Dual Problem. © 2021 ACM.

关键词： Convex optimization

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Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT

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China Communications 2021年第6期18卷 39-52页

作者： Wugedele Bao Celimuge Wu Siri Guleng Jiefang Zhang Kok-Lim Alvin Yau Yusheng Ji School of computer science and information engineering Hohhot Minzu CollegeHohhot 010051China Graduate School of Informatics and Engineering The University of Electro-Communications1-5-1ChofugaokaChofu-shiTokyo182-8585 Japan Institute of Intelligent Media Technology Communication University of ZhejiangZhejiang 310018China School of Science and Technology Sunway University5Jalan UniversitiBandar Sunway47500 Petaling JayaSelangorMalaysia Information Systems Architecture Research Division National Institute of Informatics2-1-2HitotsubashiChiyoda-kuTokyo 101-8430 Japan

In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular *** learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user ***,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still *** this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular *** proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with *** clients also work as forwarder nodes in information sharing among network *** client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking *** use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

关键词： vehicular IoT federated learning client selection networking scheme

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