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SSRN 2025年

作者： Mahali, Muhammad Izzuddin Avian, Cries Putro, Nur Achmad Sulistyo Prakosa, Setya Widyawan Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and timely intervention. Traditional gait analysis focuses on lower limb movement to differentiate between moderate and advanced PD stages;however, integrating upper and lower limb dynamics offers a more comprehensive approach to distinguishing early-stage PD from *** study introduces a novel spatio-temporal model that integrates CNN, Transformer, and BiLSTM layers to classify PD based on the Hoehn and Yahr (H&Y) score. The architecture captures both spatial and temporal dependencies in gait patterns, with a CNN serving as the primary spatial feature extractor, utilizing a temporal-focused kernel, and augmented by a Transformer to enhance temporal feature learning. Additionally, a BiLSTM module is incorporated to effectively capture complex spatio-temporal gait dynamics across all modules. For data augmentation, sliding windows of 500, 800, and 1200 samples were employed, and the model's performance was rigorously assessed through 3-fold and 5-fold cross-subject *** included accuracy, F1-score, recall, precision, and a voting-based confidence score, while t-SNE visualization provided insights into spatio-temporal feature differentiation. The model demonstrated high performance, achieving optimal accuracy of 0.90 and an average confidence score of 0.97 with a 1200-sample window in 5-fold validation. These findings highlight the potential of a spatio-temporal approach incorporating upper and lower limb characteristics in gait analysis as a robust method for PD multi-class classification, supporting early and effective PD diagnosis from raw IMU signals. © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Impact of Underlying Insulators on the Crystallinity and Antisite Defect Formation in PVD-MoS2 Films

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IEEE Journal of the Electron Devices Society 2024年

作者： Matsunaga, Naoki Imai, Shinya Shirokura, Takanori Mochizuki, Yasuhide Kuwahata, Kazuaki Tsutsui, Kazuo Kakushima, Kuniyuki Wakabayashi, Hitoshi Tokyo Institute of Technology School of Engineering Tokyo Japan Tokyo Institute of Technology Institute of Innovative Research Japan Tokyo Institute of Technology Department of Materials Science and Engineering School of Materials and Chemical Technology Japan Tokyo Institute of Technology Tokyo Tech Academy for Convergence of Materials and Informatics Japan

The effect of different underlying insulators on molybdenum disulfide (MoS2) films deposited using sputtering, which is a physical vapor deposition (PVD) method, was studied. The study reveals that the underlying insulator strongly affects the properties of the deposited PVD-MoS2 films. First, the results suggested the formation of Mo antisites during sputtering deposition, which weaken the Mo-S bonds in the MoS2 film and enhances its carrier density. Second, the crystallinity of the MoS2 films was evaluated on the basis of Raman intensity and fast Fourier transform (FFT) analysises of cross-sectional transmission electron microscopy (TEM) images. Among the samples investigated, MoS2 deposited onto atomic layer deposition (ALD)-SiO2 exhibited the highest crystallinity. These findings highlight the critical role of the underlying insulator in optimizing PVD-MoS2 films for future applications in three-dimensionally stacked field-effect transistors. © 2013 IEEE.

关键词： Molybdenum disulfide

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A Novel Scheme for Data Security in UAV Communication Networks for Preventing Data Breach

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Gaoyuan Si, Ruisong Ma, Congfang Ji, Baofeng Wang, Weiguang Mu, Yu Li, Yiwei Mumtaz, Shahid the School of Information Engineering Henan University of Science and Technology Luoyang471023 China the Science and Technology Innovation Center of Intelligent system Longmen Laboratory Luoyang471000 China the Institute of Physics Henan Academy of Sciences Zhengzhou451451 China the School of mechanical and electrical engineering Guangdong University of Science and Technology Dongguan523083 China the Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland the Department of Engineering Nottingham Trent University United Kingdom

The data security is crucial for unmanned aerial vehicles (UAVs) to ensure the integrity of data, prevent fraud, and authentication process. In order to achieve unconditional security for data in UAVs communication networks, this research study proposes a simple feedback method for wiretap channel-I (WTC-I) to establish a communication with the receiving link. The threshold effect (decides the error correction capability of the system) in the proposed research uses soft-decision decoding for low-density parity-check (LDPC) codes for secured communication channels over additive white Gaussian noise (BI-AWGN) channel as well as Binary Symmetric Channel (BSC). There is no channel state information (CSI) required for the legitimate receiver to build the WTC-I which eventually minimizes the implementation complexity. The empirical results and theory analysis indicate that the suggested approach is robust and has the characteristics of simple implementation with low energy consumption. The results also show minimal loss of safe capacity, which is crucial for secured transmissions in battery-operated UAV communication networks. The proposed study helps to achieve secured transmission of data without affecting the performance of the system with optimal consumption of power and lower maintenance complexity. © 2025 IEEE. All rights reserved,

关键词： Gaussian noise (electronic)

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Noise Reduction in Industry Based on Virtual Instrumentation

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Computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Lattice Parameters of B2 and B19′ Phases in Ti–Ni Alloys Near Transformation Temperatures: Effects of Ni Concentration and Temperature

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Shape Memory and Superelasticity 2025年 1-14页

作者： Hikosaka, Gen Shinohara, Yuri Matsumura, Ryutaro Nishida, Minoru Inamura, Tomonari School of Materials and Chemical Technology Institute of Science Tokyo Yokohama Japan Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Japan Materials and Structures Laboratory Institute of Integrated Research Institute of Science Tokyo Yokohama Japan Laboratory for Future Interdisciplinary Research of Science and Technology Institute of Integrated Research Institute of Science Tokyo Yokohama Japan Department of Advanced Materials Science and Engineering Faculty of Engineering Sciences Kyushu University Kasuga Japan

The Ni concentration and temperature dependence of the lattice parameters of the B2 and B19′ phases in binary Ti–Ni alloys was systematically clarified near the martensitic transformation temperatures, using samples with finely varied Ni concentrations ranging from 50.00 to 51.00 at.%. The differences between the present data and previously reported values were quantitatively explained and reinterpreted in terms of uncertainties in Ni concentration, measurement conditions, thermal history, and extrapolation methods. Furthermore, based on the lattice parameters obtained in this study, the effect of Ni concentration on the suppression of transformation-induced dislocations and the stabilization of thermal cycling behavior was discussed in terms of geometrical incompatibility. From the perspectives of composition increment, temperature steps, consistency in thermal history, and transparency of extrapolation procedure, the present dataset is considered to be among the most accurate, precise, and consistent lattice parameter datasets currently available. This dataset provides a highly reliable basis for evaluating the variation in lattice parameters among literature and serves as a foundation for future investigations into phase transformation behavior in Ti–Ni alloys.

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Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India

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VirusDisease 2023年第4期34卷 514-525页

作者： Suresh, Kuralayanapalya Puttahonnappa Barman, Nagendra Nath Bari, Tarushree Jagadish, Dikshitha Sushma, Bylaiah Darshan, H.V. Patil, Sharanagouda S. Bora, Mousumi Deka, Abhijit Spatial Epidemiology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Microbiology College of Veterinary Science Assam Agricultural University Guwahati India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Matthikere Karnataka Bengaluru India Virology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Pathology College of Veterinary Science Assam Agricultural University Guwahati India

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R₀) was calculated after the predicted results were superimposed onto the risk maps. The R₀ value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models’ performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen’s Kappa, True Skill Statistics, Area Under ROC Curve, ACCURACY, ERROR RATE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks. © 2023, The Author(s), under exclusive licence to Indian V

关键词： Assam Classical swine fever Incidence Machine learning Risk modelling Swine

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds

arXiv

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arXiv 2024年

作者： Fu, Liqun Tong, Jingwen Lin, Tongtong Zhang, Jun The School of Informatics Xiamen University Xiamen361005 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple challenges. This paper studies quality of experience (QoE)oriented resource allocation in MEC while considering user diversity, limited resources, and the complex relationship between allocated resources and user experience. We introduce a closed-loop online resource allocation (CORA) framework to tackle this problem. It learns the objective function of resource allocation from the historical dataset and updates the learned model using the online testing results. Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem. Thereafter, we put forth a data-driven optimal online queue resource allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm for homogenous and heterogeneous user cases, respectively. Moreover, we provide a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive experiments to evaluate the proposed algorithms using the synthesis and YouTube datasets. Numerical results validate the theoretical analysis and demonstrate that the user complaint rate is reduced by up to 100% and 18% in the synthesis and YouTube datasets, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Label Estimation of Data Using the Modified Fisher Criterion

Label Estimation of Data Using the Modified Fisher Criterion

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Motoki, Ryuhei Jin'no, Kenya Tokyo City University Informatics Graduate School of Integrative Science and Engineering Tokyo Japan Tokyo City University Faculty of Information Technology Department of Intelligent System Tokyo Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In supervised learning, the amount of work required to assign labels to data becomes enormous as the input data increases. To reduce this work, we propose a method for label estimation using only a small amount of labeled data. The SVM separation hyperplane is moved by iterating label assignment and classification, and the results are evaluated using a new cluster evaluation criterion. This criterion is based on the Fisher criterion used for linearly separable data and applied to nonlinear data. Some semi-supervised learning methods, which are related to the proposed method, are also explained. We show that the proposed method is effective for linearly inseparable data through experiments. In addition, we compared the performance of the proposed method with some semi-supervised learning methods. The contributions of this study are the classification of data in situations where labeled data is scarce and the development of new criteria for evaluating non-linear classification results. © 2022 IEEE.

关键词： Support vector machines

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Mandarin Speech Reconstruction from Tongue Motion Ultrasound Images based on Generative Adversarial Networks 46

Mandarin Speech Reconstruction from Tongue Motion Ultrasound...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Fengji Shen, Fei Ma, Ding Zhang, Shaochuan Zhou, Jie Wang, Li Fan, Fan Liu, Tao Chen, Xiaohong Toda, Tomoki Niu, Haijun Beihang University Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beijing China Nagoya University Graduate School of Informatics Nagoya Japan Capital Medical University Department of Otolaryngology Head and Neck Surgery Beijing Tongren Hospital Beijing China Nagoya University Information Technology Center Nagoya Japan

ISBN: (纸本)9798350371499

Speech impairment resulting from laryngectomy causes severe physiological and psychological distress to laryngectomee. In clinical practice, the upper vocal tract articulatory organs function normally in most laryngectomee. The potential to reconstruct speech by leveraging articulatory information is of significant importance, offering a meaningful contribution to the effective rehabilitation of speech in these patients. To begin, we created a Mandarin corpus, capturing simultaneous dynamic tongue motion ultrasound images and speech waveform during experiment. Then we utilized an autoencoder to extract deep representation from ultrasound images. Building on this, a speech waveform generation model was established using generative adversarial networks, and both objective and subjective evaluations were conducted to access the quality of the reconstructed speech. The results reveal that the phoneme accuracy of the reconstructed speech reaches 72.43%, with accuracy of Mandarin tones being 76.10%. Observing the mel-spectrogram and fundamental frequency contour, the reconstructed speech shows a high degree of similarity to original speech. Additionally, subjective speech perceptions of the reconstructed speech affirm its acceptability (mean opinion score > 6). The method presented in this paper enables to reconstruct tonal Mandarin speech from dynamic tongue motion ultrasound images. However, future research should focus on specific conditions of laryngectomee, improving and optimizing model performance, expanding training datasets, and enhancing the quality of reconstructed speech. © 2024 IEEE.

关键词： Ultrasonic imaging

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