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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Xiong, Ruihong Jin, Lihui Xu, Xuankai Li, Wenzhen Du, Shihao Wang, Yiwei Chen, Fang Wu, Tao School of Information Science and Technology ShanghaiTech University Shanghai China State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic Shanghai China

ISBN: (纸本)9798331508890

This paper presents a novel near-zero stiffness MEMS accelerometer, based on a dual nonlinear curved beams anti-spring mechanism. The mechanism consists of two nonlinear curved beams connected in parallel to the proof mass of the accelerometer. During deflection, these beams are compressed, introducing an axial compression force within each beam, thus achieving a significant stiffness reduction. By utilizing electrostatic force to bias the accelerometer into a low-stiffness position, the proposed design achieves a stiffness reduction of up to 88.29%. Additionally, a high-precision experimental system was built with the accelerometer chip and a low noise interface ASIC. Experimental results demonstrate that the noise floor of the accelerometer is 500 ng/√Hz (at 1-10 Hz), and it has successfully detected seismic wave signal during earthquake exploration. © 2025 IEEE.

关键词： Accelerometers

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AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Gangul, Ranita Kolekar, Sagar Chintakunta, Advaitha Naidu Mungara, Sudeep Bangalore India Institute of Technology Department of Data Science Stevens HobokenNJ07030 United States Delaware State University Computer Science & Engineering Bangalore India Department of Civil Engineering Pune India University of North Carolina at Charlotte Department of Computer Science CharlotteNC28223 United States Masters in Information Systems Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9798331508685

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity. © 2025 IEEE.

关键词： Risk assessment

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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3.5 Ghz Thermo-Acoustic Phase Modulator Based on Z-Cut LiNbO3 Thin Film 38

3.5 Ghz Thermo-Acoustic Phase Modulator Based on Z-Cut LiNbO...

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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Xu, Xuankai Liu, Yushuai Xiong, Ruihong Wu, Tao School of Information Science and Technology ShanghaiTech University Shanghai China State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic Shanghai China

ISBN: (纸本)9798331508890

We demonstrate the first thermo-acoustic (TA) phase modulator utilizing a suspended Z-cut LiNbO thin film, operating in the 3rd order A1 mode at frequencies up to 3.55 GHz. The device exhibits an impressive phase response of 97.3°/mW. It achieves a maximum phase shift of 435° with modulation heating voltages below 1 V, within a compact footprint of 0.1 mm2. Compared to state-of-the-art electro-acoustic (EA) modulators, this design requires two orders of magnitude less voltage for the same modulation length. The suspended structure, offering excellent thermal isolation and low thermal capacitance, provides a 37 times improvement in phase response over TA modulation on surface acoustic wave (SAW) platforms, underscoring its potential for next-generation acoustic signal processing applications. © 2025 IEEE.

关键词： Acoustic signal processing

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Sensing-Assisted Channel Estimation in ISAC systems 14th

Sensing-Assisted Channel Estimation in ISAC Systems

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14th EAI International Conference on Wireless and Satellite systems, WiSATS 2024

作者： Yin, Rui Shi, Yexin Qi, Wei Chen, Xianfu Wu, Celimuge Ji, Yusheng School of Information and Electrical Engineering Hangzhou City University Hangzhou310018 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Shenzhen CyberAray Network Technology Company Ltd Shenzhen China Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9783031862021

In future Sixth-Generation (6G) mobile communication, satellite communication is a crucial means of extending mobile networks worldwide. However, in satellite communication systems, the transmission capacity between ground stations and users has become a bottleneck for satellite communication systems. For this issue, Integrated Sensing and Communication (ISAC) technology and Artificial Intelligence (AI), which has been widely studied, has the potential to offer excellent solutions. This paper explores a Uformer-based sensing-assisted communication system that enhances channel estimation accuracy through the design of a Uformer-based channel estimation enhancement algorithm. By formulating a global optimization problem, we seek to improve resource efficiency through finding the optimal resource allocation schemes. Initially, the system utilizes radar data to obtain reconstructed channels and employs communication data for channel estimation. The proposed neural network model then fuses these data into a new and more accurate channel state information. Finally, we present a resource allocation algorithm and validate the performance of all proposed algorithms through simulations. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Resource allocation

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Correction to: An advanced reinforcement learning control method for quadruped robots in typical urban terrains (International Journal of Machine Learning and Cybernetics, (2024), 10.1007/s13042-024-02478-9)

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International Journal of Machine Learning and Cybernetics 2025年 1-2页

作者： Yan, Chi Wang, Ning Gao, Hongbo Wang, Xinmiao Tang, Chao Zhou, Lin Li, Yuehua Wang, Yue School of Information Science and Technology University of Science and Technology of China No. 96 Jinzhai Road Anhui Hefei230026 China School of Information and Security Chongqing College of Mobile Communication No. 36 Dengying Avenue Qijiang District Sichuan Chongqing401520 China Institute of Advanced Technology University of Science and Technology of China No. 5089 Wangjiang West Road Anhui Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Nanyang639798 Singapore Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Zhejiang Hangzhou311121 China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

In this article, the affiliation details for Authors "Ning Wang2, Hongbo Gao1,3,4, Yuehua Li5, Yue Wang6" were interchanged and it is corrected now. Incorrect affiliation: 2Institute of Advanced technology, University of science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 3School of Electrical and Electronic engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 4Zhejiang lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 5College of Control science and engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China 6School of information and Security, Chongqing College of Mobile Communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China Correct affiliation: 2School of information and Security, Chongqing College of Mobile Communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China 3Institute of Advanced technology, University of science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 4School of Electrical and Electronic engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 5Zhejiang lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 6College of Control science and engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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Enhancing student performance prediction: the role of class imbalance handling in machine learning models

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Discover Computing 2025年第1期28卷 1-34页

作者： Althaqafi, Turki Saleem, Farrukh AL-Ghamdi, Abdullah AL-Malaise Computer Science Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah 22246 Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University Leeds LS6 3QR United Kingdom Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

The Learning Management System (LMS) is an essential tool for educational institutions that facilitates content delivery, assessments, lecture delivery, and collaboration to enhance the learning experience. This study explores the role of LMS in creating an effective learning environment to improve students’ academic performance. To achieve the main objective of this study, we utilized a dataset [xAPI-Edu-Data] comprising multiple factors, such as academic, psychological, and cognitive engagement. Various machine learning techniques are employed to assess the impact of engagement activities on students’ performance. Initially, a class imbalance issue identified in the dataset and addressed using SMOTE technique. In addition, other resampling strategies applied to compare the effectiveness of proposed work. The model performance evaluated and compared using different evaluation metrics before and after data enrichment. In addition, hyperparameter optimization is conducted using a grid search approach to enhance models’ accuracy. The performance of individual models such as support vector machine (0.81), logistic regression (0.80), and decision tree (0.75) enhanced using the enriched dataset. The integration of multiple base learners into an ensemble model, with random forest as the stacking learner, achieved a weighted precision of 0.83, improving from 0.60 with the original dataset. The implementation of the stacking approach with enriched dataset has identified a better result and improved accuracy by 23%. The key contribution of this study includes identifying the effectiveness of data enrichment in improving prediction accuracy. Moreover, the research highlights the role of student engagement and behavior in measuring academic performance. The proposed model can identify the factors behind low performance, allowing further actions to be taken. Based on the prediction, the educators can work on the associated factors that could be low engagement, participation, or

关键词： Academic performance Learning management system Machine learning Predictive modeling Student engagement

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Finite-Time Multisynchronization of Coupled Discontinuous Cohen-Grossberg Neural Networks and Its Application in Associative Memory

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IEEE Transactions on Circuits and systems I: Regular Papers 2025年

作者： Liu, Yang Wang, Zhen Huang, Xia Shen, Hao Shandong University of Science and Technology College of Mathematics and Systems Science Qingdao266590 China Anhui University of Technology School of Electrical and Information Engineering Ma'anshan243002 China

This article studies the finite-time multisynchronization of coupled multistable Cohen-Grossberg neural networks (CGNNs) with discontinuous activation functions (AFs). At first, the activation function (AF) with p discontinuous points is proposed to guarantee that n-neuron subnetwork could produce (2 p + 1)n locally stable equilibrium points (EPs) or periodic orbits (POs). Moreover, sufficient conditions are given to guarantee that dynamical multisynchronization (DMS) or static multisynchronization (SMS) of coupled CGNNs can be reached in finite time. Moreover, the settling time is estimated. Coupled CGNNs with discontinuous AFs in this paper can produce more synchronization manifolds compared with coupled CGNNs with continuous AFs, which implies that in associative memory application, each subnetwork can store more memory patterns. Lastly, an example and the application in associative memory are illustrated to testify the validity of the main results. Compared with the multistability-based associative memory, the multisynchronization-based associative memory can have superiority in resisting the noise interference. © 2025 IEEE.

关键词： Associative storage

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Dynamic Monitoring of Acne Progression and Quantitative Severity Assessment Using Optical Coherence Tomography 17

Dynamic Monitoring of Acne Progression and Quantitative Seve...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Chen, Zhida Lin, Hui Li, Yao Zhong, Cheng Li, Zhifang Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Provincial Key Lab of Photonic Technology Information Photonics Research Center College of Photonic and Electronic Engineering Fujian Normal University Fujian Fuzhou350007 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Acne vulgaris, a common dermatological condition affecting the pilosebaceous follicular unit, arises from a complex interplay of factors including hyperseborrhea, Propionibacterium acnes colonization, hyperkeratosis, and inflammation. Existing pharmacodynamic evaluation techniques do not facilitate real-time and non-invasive assessment of acne and its severity within living *** this study, the aim was to establish a murine model of acne vulgaris, elucidate its pathological processes, and quantitatively assess the severity of the condition using non-invasive imaging techniques. We employed optical coherence tomography (OCT) in conjunction with microscopy to scrutinize the complete life cycle of acne lesions. microscopy provided insights into the micro-anatomical characteristics of murine skin lesions, while OCT allowed for the assessment of skin structural changes. Our findings indicate that the epidermis undergoes hyperplasia concurrent with acne development, and the subsequent reparative phase is marked by scab *** deep learning algorithms enabled the precise quantification of epidermal and scab thickness variations. This novel approach provides a unique perspective for acne assessment, potentially guiding more precise clinical drug selection and improving therapeutic outcomes. © 2025 SPIE.

关键词： Optical coherence tomography

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Optimized deep learning models based-sentiment analysis approach for identifying healthcare rumors over social media 2nd

Optimized deep learning models based-sentiment analysis appr...

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2nd International Conference on Next-Generation Computing and information systems, ICNGCIS 2023

作者： Kumar, Pramod Vashishtha, Ela Alghayadh, Faisal Yousef Wasim Bhatt, Mohammed Bala, Suchitra Sreejitha, T.S. Computer Science and Technology Ganga Institute of Technology and Management Haryana Kablana Jhajjar India Lead Analytics Texas Health Resources TX United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts and Science College India St. Marys College Thrissur India

ISBN: (纸本)9781032738659

Research the mechanism of fake news refuting rumors in the real social network environment. Propose methods to evaluate the effect of rumor refuting and explore the factors that affect the impact of rumor refuting. Based on the current research results and assumptions, eight factors that affect the effect of rumor refuting are summed up, such as the proportion of the original fake news content. Experiments were conducted based on the rumor-refuting Weibo data. The experimental analysis showed that the proportion of the original fake news in the rumor-refuting information was negatively correlated with the rumor-refuting effect, and the explanation was positively correlated with the rumor-refuting effect suggestions for refuting rumors, including explaining why the original fake news is wrong to guiding social network fake news denying rumors. © 2025 The Author(s).

关键词： Sentiment analysis

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