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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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Cyber-Physical Steganography in Robotic Motion Control

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Lin, Yijie Echizen, Isao The Information and Society Research Division National Institute of Informatics Japan The Department of Information Engineering and Computer Science Feng Chia University Taiwan The Graduate School of Information Science and Technology University of Tokyo Japan

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies. © 2025, CC BY.

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Neural Error Covariance Estimation for Precise LiDAR Localization

arXiv

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arXiv 2025年

作者： Dolatabadi, Minoo Ayar, Fardin Javanmardi, Ehsan Tsukada, Manabu Javanmardi, Mahdi Department of Computer Engineering Amirkabir University of Technology Tehran Iran Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

Autonomous vehicles have gained significant attention due to technological advancements and their potential to transform transportation. A critical challenge in this domain is precise localization, particularly in LiDAR-based map matching, which is prone to errors due to degeneracy in the data. Most sensor fusion techniques, such as the Kalman filter, rely on accurate error covariance estimates for each sensor to improve localization accuracy. However, obtaining reliable covariance values for map matching remains a complex task. To address this challenge, we propose a neural network-based framework for predicting localization error covariance in LiDAR map matching. To achieve this, we introduce a novel dataset generation method specifically designed for error covariance estimation. In our evaluation using a Kalman filter, we achieved a 2 cm improvement in localization accuracy, a significant enhancement in this domain. © 2025, CC BY.

关键词： Kalman filters

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Where Do You Go? Pedestrian Trajectory Prediction using Scene Features

arXiv

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arXiv 2025年

作者： Rezaei, Mohammad Ali Ayar, Fardin Javanmardi, Ehsan Tsukada, Manabu Javanmardi, Mahdi Department of Computer Engineering Amirkabir University of Technology Tehran Iran Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

Accurate prediction of pedestrian trajectories is crucial for enhancing the safety of autonomous vehicles and reducing traffic fatalities involving pedestrians. While numerous studies have focused on modeling interactions among pedestrians to forecast their movements, the influence of environmental factors and scene-object placements has been comparatively underexplored. In this paper, we present a novel trajectory prediction model that integrates both pedestrian interactions and environmental context to improve prediction accuracy. Our approach captures spatial and temporal interactions among pedestrians within a sparse graph framework. To account for pedestrian-scene interactions, we employ advanced image enhancement and semantic segmentation techniques to extract detailed scene features. These scene and interaction features are then fused through a cross-attention mechanism, enabling the model to prioritize relevant environmental factors that influence pedestrian movements. Finally, a temporal convolutional network processes the fused features to predict future pedestrian trajectories. Experimental results demonstrate that our method significantly outperforms existing state-of-the-art approaches, achieving ADE and FDE values of 0.252 and 0.372 meters, respectively, underscoring the importance of incorporating both social interactions and environmental context in pedestrian trajectory prediction. © 2025, CC BY.

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A Verification Framework Based on EDA Algorithms for E-book Design by Visual programming Language

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IEEE Software 2025年

作者： Koong, Chorng-Shiuh Hsieh, Tung-Li Wu, Chao-Chin Guo, Cheng-Yan National Taichung University of Education Department of Computer Science and Information Engineering Taichung City403514 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung City807618 Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Changhua50007 Taiwan National Taiwan University Graduate Institute of Electronics Engineering The Graduate School of Advanced Technology Taipei106319 Taiwan

Creating an interactive electronic book (e-book) requires substantial time, skill, and effort. Our study reveals that authoring tools must eliminate users' need for textual programming. We propose a novel interactive e-book design method that uses multimedia reusable components (MRCs) and an e-book description language (EBDL) to implement e-book authoring tools like a circuit design concept. Interactive ebooks can be designed intuitively using MRCs and EBDL without text-based programming, which differs from static syntax analysis of text-based programming languages. The designs using MRCs and EBDL can be verified and tested using the Boolean satisfiability of the electronic design automation algorithm. Notably, verification and debugging are challenging in current visual programming languages. Therefore, our research provides e-book designers reduce the technical requirements of e-book design. The findings can also ensure visual programming languages' consistent behavior and design while detecting design errors. © 1984-2012 IEEE.

关键词： Visual languages

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Evaluating Segmentation Accuracy with Diverse Prompt Strategies in Medsam

Evaluating Segmentation Accuracy with Diverse Prompt Strateg...

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IEEE International Symposium on Biomedical Imaging

作者： Muhammad Nouman Ghada Khoriba Essam A. Rashed Graduate School of Information Science University of Hyogo Kobe Japan School of Information Technology and Computer Science Nile University Giza Egypt Advanced Medical Engineering Research Institute University of Hyogo Himeji Japan

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Segment Anything Model (SAM) shows robustness in prompt-based segmentation of natural images. However, adapting SAM to the medical domain as a foundation model, MedSAM, presents unique challenges arising from clinical settings. This study evaluates MedSAM's performance across five medical imaging modalities (MRI, CT, ultrasound, endoscopy, and fundus IR) using four different prompting strategies: standard bounding boxes (SBB), rotated bounding boxes (RBB), contour points (CP), and simulated human annotations (SHA). MedSAM's performance is assessed under precise and perturbed conditions for each prompt type to evaluate its segmentation capabilities comprehensively. We empirically evaluate MedSAM's abilities and adaptability by experimenting with zero-shot learning and fine-tuning approaches. The results indicate that performance varies markedly with prompt type, accuracy, and imaging modality. The study concludes that MedSAM is sensitive to prompt precision and anatomical complexity. Fine-tuning improves performance, but further refinements are necessary to make it robust in various clinical settings. This work highlights the need for prompt engineering and provides practical insights to enhance segmentation accuracy in SAM-like foundation models.

关键词： Image segmentation Ethics Accuracy Ultrasonic imaging Foundation models Magnetic resonance imaging Robustness Prompt engineering Standards Biomedical imaging

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Bridging Gender Disparities in Mental Health: A Bilingual Large Language Model for Multilingual Therapeutic Chatbots

Bridging Gender Disparities in Mental Health: A Bilingual La...

引用

Learning and Technology Conference (L&T)

作者： Ahmed Tamer Elboardy Ziad Mohamed Ghada Khoriba Tamer Arafa Essam A. Rashed School of Information Technology and Computer Science Nile University Giza Egypt Graduate School of Information Science University of Hyogo Kobe Japan Advanced Medical Engineering Research Institute University of Hyogo Himeji Japan

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Significant gender disparities and a general lack of support systems compound the state of mental health in developing countries. Women, in particular, face unique challenges due to societal norms and expectations that often restrict their access to mental health services. Additionally, the cultural stigma associated with mental health issues affects both men and women but can be particularly debilitating for women who may be less likely to seek help or disclose their struggles. This study explores the application of Jais-13B, a pre-existing bilingual Large Language Model (LLM) proficient in English and Arabic, tailored to address mental health inquiries. We employed a rich dataset encompassing both languages, essential for fine-tuning and enhancing Jais-13B’s responsiveness and empathetic engagement in mental health contexts. The optimization focused on improving the model’s ability to deliver accurate and contextually aware responses, which is crucial for effective mental health support. Performance evaluation using the cross-entropy loss function demonstrated that Jais-13B achieved an accuracy of 70.58%, indicating its capability to produce relevant and precise answers to mental health questions. These results showcase Jais-13B’s potential to enhance digital mental health interventions significantly, providing accessible care solutions across diverse linguistic landscapes. This study reaffirms the effectiveness of bilingual LLMs in mental health applications and provides insights into their practical implementation, setting a foundational benchmark for future research in multilingual therapeutic chatbots.

关键词： Performance evaluation Ethics Adaptation models Sentiment analysis Accuracy Large language models Mental health Chatbots Multilingual Context modeling

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Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mollah, Muhammad Baqer Wang, Honggang Karim, Mohammad Ataul Fang, Hua University of Massachusetts Dartmouth Department of Electrical and Computer Engineering MA02747 United States Yeshiva University Katz School of Science and Health Department of Graduate Computer Science and Engineering NY10016 United States University of Massachusetts Dartmouth Department of Computer and Information Science MA02747 United States

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications. © 2015 IEEE.

关键词： Beamforming

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