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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Jennifer Boger Alexander Wong George Shaker dept. Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Public Health Sciences University of Waterloo Waterloo Canad dept. Systems Design Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350351057

ISBN: (纸本)9798350351064

This paper presents a novel method for human gait analysis in cluttered in-home environments using mm-wave radar systems, leveraging novel signal processing and unsupervised machine learning to extract gait parameters accurately amidst obstacles, multipath, and ghosts. Our approach accurately identifies gait parameters such as stride length and walking speed while walking on a curvy path, outperforming traditional techniques. The findings highlight the potential of mm-wave radar for non-invasive, privacy-preserving gait monitoring, with significant implications for healthcare, elderly care, and ambient assisted living. This work advances human motion analysis technology and opens new avenues for in-home monitoring applications.

关键词： Legged locomotion Microwave technology Radar clutter Medical services Radar signal processing Sensors Biomedical monitoring

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Towards Motor Imagery Classification of Finger Tapping by Both Hands for Controlling a Finger-Arm Bionic Robot

Towards Motor Imagery Classification of Finger Tapping by Bo...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Seong-Hyun Yu Young-Min Go Hyeong-Yeong Park Seo-Jin Lee Minji Lee Ji-Hoon Jeong Dept. Computer Science Chungbuk National University Cheongju Republic of Korea School of Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

In motor imagery, the greatest attention was paid to decoding signals from large muscles. However, there is a missing piece in helping people with finger issues or paralysis. This study investigates the potential advancements in neurorehabilitation by exploring the discriminative capabilities of electroencephalography (EEG) based classification in finger tapping. To improve neurorehabilitation strategies, we performed a comparative analysis of finger tapping classification during motor imagery and execution tasks. Using EEG signals, we employed machine learning and deep learning techniques, including support vector machines (SVM), linear discriminant analysis (LDA) and EEGNet. Our results indicate promising differences in EEG signatures between the two paradigms, with SVM, LDA, and EEGNet demonstrating varying degrees of efficacy in classification. These findings suggest potential implications for customized neurorehabilitation interventions. The study contributes to the affirmative possibility of effective rehabilitation strategies and is of particular relevance for patients with impaired motor function.

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning

Robust Phishing URL Classification Using FastText Character ...

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Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Abdullah Al-Sakib Zishad Hossain Limon Anamul Sakib Md Nuruzzaman Pranto Md Ariful Islam Shayma Sultana Md. Redwan Ahmed Shafiur Rahman Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Management Information System International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims.

关键词： Deep learning Uniform resource locators Training Adaptation models Phishing Prevention and mitigation Encoding Tokenization Real-time systems Robots

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Learning Adaptive Safety for Multi-Agent systems

arXiv

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arXiv 2023年

作者： Berducci, Luigi Yang, Shuo Mangharam, Rahul Grosu, Radu Institute of Computer Engineering TU Wien Austria Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and a competitive multi-agent racing scenario, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy and flexibility of ASRL, and assess generalization and scalability to out-of-distribution scenarios. Code and supplementary material are public online1 © 2023, CC BY-NC-SA.

关键词： Multi agent systems

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Single-Phase Unidirectional Multilevel Rectifiers Based on Cascaded Cells

Single-Phase Unidirectional Multilevel Rectifiers Based on C...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Amanda Pereira Monteiro Cursino Brandão Jacobina Filipe Antônio Da Costa Bahia Reuben Palmer Rezende de Sousa Jean Torelli Cardoso Alan Robson Andrade Pinto Mechanical Engineering Dept. Federal University of Ceará Fortaleza Brazil Electrical Engineering Dept. Federal University of Campina Grande Campina Grande Brazil Electrical and Computer Engineering Dept. Federal University of Bahia Salvador Brazil Electronics and Systems Dept. Federal University of Pernambuco Recife Brazil

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes and investigates two new single-phase unidirectional multilevel rectifiers based on cascaded cells. Compared to conventional topology, as the number of cascaded cells increases, proposed ones employ, proportionally, a reduced controlled switch count and driver circuits, which may reduce control complexity and increase reliability. The dc-link voltage ratios are calculated for asymmetrical operation to provide the maximum number of voltage levels generated in the ac side of the rectifiers, ensuring a high power quality. Besides, the proposed configurations can synthesize a higher number of voltage levels by employing the same controlled switch count as the conventional one so that it is possible to provide a higher levels-per-switch ratio. Simulation and experimental results are presented to validate the performance and feasibility of the proposed topologies.

关键词： Reactive power Power quality Rectifiers Switches Control systems Reliability engineering Topology Power system reliability Voltage control Switching circuits

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Jiayuan Jin Xiantao Jiang Tian Song Fei. Richard Yu WeiLi Jin Liu Dept. Information Engineering Shanghai Maritime University Shanghai China dept. Electrical and Electronics Engineering Tokushima University Tokushima Japan dept. Systems and Computer Engineering Carleton University Ottawa Canada

A new Quadtree with nested multi-type tree(QTMT) partitioning method is adopted in the latest video coding standard Versatile Video Coding(VVC) to achieve effective encoding. Compared with Quadtree(QT) in HEVC, QTMT can provide enhanced encoding performance. However, this improved coding performance is obtained at the cost of additional computational load from recursive and nested searches for the best CU structure. A context similarity-enabled CU partitioning algorithm is proposed to achieve a balance between coding efficiency and compression quality in VVC. According to the difference between frames, the method of frame difference algorithm is used to calculate the possibility that the current block can follow the same subblock division mode of the previous frame, so as to skip the unnecessary division selection process. The experimental results show that the proposed method can save the encoding time up to 18.22% with about 0.91% BD-rate degradation on average compared with VVC software reference VTM-9.3

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SCC: Efficient Error Correction Codes for MLC PCM

SCC: Efficient Error Correction Codes for MLC PCM

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International SoC Design Conference, ISOCC

作者： Yujin Lim Dongwhee Kim Jungrae Kim Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor and Display Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon Korea

Phase Change Memory (PCM) has presented as a potential replacement of DRAM due to its better scalability. However, MLC PCM encounters reliability problems such as resistance drift error and cell wear-out. To improve reliability, implementing strong Error Correction Codes (ECC) is required but this incurs substantial overhead to memory system. Existing ECC schemes have trade-offs between error correction capability and overhead. This paper proposes Single Cell Correction (SCC) codes, an efficient ECC scheme which can correct single cell errors in MLC PCM with small overhead.

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