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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jedidah Mwaura Shunsuke Araki Muhammad Bisri Musthafa Samsul Huda Yasuyuki Nogami Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Environmental Life Natural Science and Technology Okayama University Okayama Japan Green Innovation Center Okayama University Okayama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Currently, there are many ongoing attempts to incorporate transfer learning into Intrusion Detection systems. Transfer learning leverages on the knowledge of previous models thus reducing training time, improving accuracy and making it possible to train new models with limited data. However, it is difficult to incorporate such a technique into IoT devices with limited computational power. In this paper, we propose a Transfer Learning TinyML-based IDS framework with feature selection to be implemented on a microcontroller for detecting attacks on IoT devices. To implement the proposal, we leveraged knowledge achieved from our previous DDoS attack detection model to create a new transfer learning framework that could detect various IoT attacks such as malware attacks and zero-day attacks. To test the transfer learning framework, we trained it using two separate sets of datasets each containing different attacks. After each training, we evaluated the original model, converted it into a format compatible with a microcontroller and evaluated it further. The TL TinyML-based framework was evaluated based on its accuracy, model sizes and average inference times. As the results confirmed, the framework performed well and can successfully be implemented on a microcontroller without depleting its resources.

关键词： Training Accuracy Microcontrollers Computational modeling Transfer learning Intrusion detection Feature extraction Malware Data models Internet of Things

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Open Wireless Digital Twin: End-to-End 5G Mobility Emulation in O-RAN Framework

arXiv

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arXiv 2025年

作者： Iye, Tetsuya Sakamoto, Masaya Takaya, Shohei Sato, Eisaku Susukida, Yuki Nagaoka, Yu Maruta, Kazuki Nakazato, Jin Kozo Keikaku Engineering Inc. Tokyo Japan Department of Electronics and Computer Systems Graduate School of Engineering Takushoku University Tokyo Japan Department of Electrical Engineering Graduate School of Engineering Tokyo University of Science Tokyo Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

This paper presents an end-to-end wireless digital twin platform constructed using open-source software and open data to enhance the evaluation of mobile communication systems. The proposed open wireless digital twin (OWDT) integrates OpenAirInterface (OAI) for 5G NR protocol stack emulation and NVIDIA Sionna RT for high-resolution ray tracing based radio propagation modeling. This integration enables realistic emulation of vehicular mobility scenarios, leveraging real-world geographic and building data to bridge the gap between theoretical simulations and real-world deployments. The platform utilizes ORAN’s Near-RT RIC via OAI FlexRIC to dynamically monitor key performance indicators (KPIs) such as RSRP, MCS, BLER, and throughput in real time. Through extensive evaluation in urban environments, this study demonstrates the validity of the emulation framework, revealing its capability to replicate real-world communication dynamics with high fidelity. The results underscore the potential of OWDT in accelerating wireless system development, reducing experimental costs, and optimizing network configurations. Copyright © 2025, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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A Study on the Effectiveness of Interactive systems Using Electromyography

A Study on the Effectiveness of Interactive Systems Using El...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Hanawa, Shinnosuke Ho, Yihsin Kobayashi, Iciro Sato-Shimokawara, Eri Shibata, Hiroki Obo, Takenori Takushoku University Graduate School of Engineering 815-1 TatemachiHachioji Tokyo Japan Takushoku University Faculty of Engineering 815-1 TatemachiHachioji Tokyo Japan Tokyo Metropolitan University Department of Computer Science 6-6 AsahigaokaHino Tokyo Japan Tokyo Metropolitan University Department of Mechanical Systems Engineering 6-6 AsahigaokaHino Tokyo Japan

ISBN: (纸本)9798331531614

The paper presented an intuitive control system using electromyography (EMG) data that is obtained from the Myo gesture control armband. The aim of this study is to enable users to control multiple devices with a single EMG device in an intuitive way. The presented system shows the ability of EMG-based gestures to control Phillips Hue that allow user to control light bulb by simple hand movements. Moreover, the authors also developed new functionalities, which is according to users' preferences, to register gestures. These functions aim to improve the usability and enhance the naturalness of operations. Additionally, unique gestures, which less common in everyday life, is defined to reduce misrecognition when switching between different devices. Furthermore, to achieve reliable and accurate recognition of multiple gestures, the Support Vector Machine (SVM) models is considered to be a machine learning method for training processed EMG data. The experiment results demonstrate significant improvements in user experience and practical applicability in various interactive scenarios. © 2025 IEEE.

关键词： Support vector machines

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

arXiv

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arXiv 2025年

作者： Roh, Daeyoung Han, Donghee Kim, Daehee Han, Keejun Yi, Mun Graduate School of Data Science KAIST Korea Republic of School of Computer Engineering Hansung University Korea Republic of Department of Industrial and Systems Engineering KAIST Korea Republic of

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***. © 2025, CC BY.

关键词： Contrastive Learning

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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IoT-based Efficient Waste Management on University Campus : Enhancing Monitoring and Optimization through Machine Learning

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Procedia computer science 2025年 258卷 4157-4168页

作者： Sumathi M. Hanumanthappa J. K.S. Santhosh Kumar S.P. Shiva Prakash Meghana J. Department of Studies in Computer Science Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka 570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka 570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)’s flawless

关键词： Artificial Intelligence Smart Parking: Smart city Machine Learning Smart Applications

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Testing Facility Location with Constrained Queue Time Problem: A Case Study in Florida, USA 24

Testing Facility Location with Constrained Queue Time Proble...

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Proceedings of the Winter Simulation Conference

作者： Almir Antonio Monteiro Junior Rafael Schneider Vanessa de Almeida Guimarães Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro BRAZIL COPPEAD Graduate School of Business Federal University of Rio de Janeiro BRAZIL

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model's output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability.

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