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IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies (CHASE)

作者： Soroush Zare Ye Sun Department of Mechanical and Aerospace Engineering Department of Electrical and Computer Engineering University of Virginia Charlottesville VA

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

Neuromuscular disorders encompass pathological conditions affecting either the musculoskeletal system, the ner-vous system, or their interface. The majority of neuromuscular disorders lack complete curative treatments and thus necessi-tate efforts focused on improving rehabilitation and quality of life. Assistive technologies with brain-computer interface (BCI) control provide a promising solution to main patients' daily activities. Central to these technologies is the classification of electroencephalography (EEG) motor imagery signals, which is paramount for the operational success of BCIs. The endeavor to enhance the precision of these classifications has led to the exploration of various computational techniques, with convo-lutional neural networks (CNNs) being at the forefront due to their proficiency in handling spatial hierarchies in data. Despite the advancements, the quest for improved accuracy continues, especially in scenarios characterized by raw EEG data inputs, as opposed to the transformed data inputs (e.g., using Fast Fourier Transform (FFT)) commonly reported in existing literature. This study introduces an integrated CNN model augmented with a transformer encoder block, aiming to harness the inherent spatial-temporal features of EEG signals more effectively. Preliminary evaluations suggest an encouraging comparable performance with the studies reported in the field, thereby underscoring its potential utility in enhancing motor imazery classification for BCI applications.

关键词： Pathology Musculoskeletal system Neuromuscular Fast Fourier transforms Assistive technologies Motors Transformers

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Cyber Twin Technology for AI-Driven Real-Time Software Security in IoT Ecosystems 4

Cyber Twin Technology for AI-Driven Real-Time Software Secur...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Dhasmana, Gulshan Ravi, K.S. Nayana Yadav, M. Desai, Ishan Shankar, Rajmani Oracle Cloud Engineering Data Center NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Aj Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks, which are increasingly targeted by sophisticated cyber-attacks. To address these issues, new methodologies that combine real-time monitoring and adaptive security mechanisms are needed. Cyber Twin technology, an innovative extension of digital twin technology, presents a promising solution by creating AI-driven digital replicas of IoT devices and software systems for continuous security monitoring and management. This paper introduces a Cyber Twin Technology Framework for AI-driven real-time software security in IoT ecosystems. The framework employs advanced AI models, including Convolutional Neural Networks (CNNs) for anomaly detection and Generative Adversarial Networks (GANs) for synthetic data generation to simulate potential attack scenarios. A dynamic reinforcement learning module is integrated to optimize threat response strategies based on evolving threat patterns. By creating real-time digital replicas of IoT components, the Cyber Twin framework continuously monitors device behaviors, identifies anomalies, and autonomously initiates mitigation actions. The system is evaluated in a simulated IoT environment with over 500 interconnected devices. Experimental results demonstrate that the Cyber Twin framework achieved a 99.2% detection accuracy in identifying cyber threats, with a false positive rate of 1.3%. The dynamic response module reduced incident response time by 35% compared to traditional methods, enhancing the system's ability to neutralize potential threats in real-time. The use of GAN-based synthetic data also enabled proactive defense strategies, reducing attack success rates by 40% during testing. The Cyber Twin Technology Framework provides a robust solution for real-time software security in complex IoT ecosystems. By leveraging A

关键词： Generative adversarial networks

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Recyclability and Self-Healing of Dynamic Cross-Linked Polyimide with mechanical/electrical Damage

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Energy & Environmental Materials 2023年第1期6卷 401-407页

作者： Baoquan Wan Ming-Sheng Zheng Xing Yang Xiaodi Dong Yuchao Li Yiu-Wing Mai George Chen Jun-Wei Zha School of Chemistry and Biological Engineering University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing 100083China Shunde Graduate School of University of Science and Technology Beijing Foshan 528300China School of Materials Science and Engineering Liaocheng UniversityLiaocheng 252059China Centre for Advanced Materials Technology(CAMT) School of AerospaceMechanical and Mechatronic EngineeringThe University of SydneySydney New South Wales 2006Australia Department of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK

Recyclability and self-healing are two most critical factors in developing sustainable polymers to deal with environmental pollution and resource *** this work,a dynamic cross-linked polyimide insulation film with full closed-loop recyclability is successfully prepared,which also possesses good self-healing ability after being mechanical/electrical damaged depending on the Schiff base dynamic covalent *** recycled and self-healed polyimide film still maintain its good tensile strength(r t)>60 MPa with Young’s modulus(E)>4 GPa,high thermal stability with glass transition temperature(T g)>220℃,and outstanding insulation property with breakdown strength(E 0)>358 kV mm^(-1),making it a very promising low energy consumption and high temperature resistant insulation *** strategy of using Schiff base dynamic covalent bonds for reversible repairing the structure of high T g polyimides promotes the wider application of such sustainable and recyclable material in the field of electrical power and micro-electronics.

关键词： electrical damage polyimide recyclability self-healing

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Evaluation of Deep Neural Operator Models Toward Ocean Forecasting

Evaluation of Deep Neural Operator Models Toward Ocean Forec...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Rajagopal, Ellery Babu, Anantha N. S. Ryu, Tony Haley, Patrick J. Mirabito, Chris Lermusiaux, Pierre F. J. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States Center for Computational Science and Engineering Massachusetts Institute of Technology Department of Mechanical Engineering CambridgeMA United States

ISBN: (纸本)9798218142186

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications. © 2023 The Marine Technology Society (MTS).

关键词： Flow of fluids

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Microfabrication of 3D Spheroid Tumor Models and Imaging using Spectral Domain Optical Coherence Tomography 10

Microfabrication of 3D Spheroid Tumor Models and Imaging usi...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： John, Pauline Sukumar, Pavithra Sami, Christopher Qasaimeh, Mohammad A. Zam, Azhar New York University Abu Dhabi Division of Engineering Abu Dhabi United Arab Emirates New York University Department of Mechanical and Aerospace Engineering New York United States New York University Department of Biomedical Engineering Department of Electrical and Computer Engineering New York United States

ISBN: (纸本)9798350350951

A low-cost, robust, and flexible device for generating an array of miniaturized 3D spheroid tumor models was designed and developed. The device consisting of two layers: a layer with through-hole array and a plain lamination sheet, in which both are bonded so the through-holes acting as confining barriers. The hydrophobic bottom lamination sheet led to drop formation in each through-hole, confirmed by experimental characterization using food color dye of different volumes. Further, 3D tumor spheroid models were grown within through-holes on the lamination sheet by the hanging drop approach. With preliminary experiments, spheroids were gown in a flat substrate using simple micro-pipetting and the classical hanging drop method. The formed 3D spheroid tumor models were consequently characterized by live/dead assay. To verify formation of the 3D tumor spheroid models on days 1, 4, 7, 14, and 21, label-free spectral domain based optical coherence tomography (SD-OCT) technique was employed. Results showed an increase in the height of the spheroids up to 600 μm. By utilizing SD-OCT, we were able to achieve enhanced penetration within the 3D spheroids, allowing us to visualize the structural information at various depths. With spheroids growth, the SD-OCT was performed to quantitatively analyze their sizes and necrotic cores. The length, width and diameter of the spheroids were measured for volumetric analysis, based on the cross-sectional and en face SD-OCT images. Next experiments will consider using the developed device with the SD-OCT technique for personalized medication in clinical field and drug-screening/drug-discovery in pharmaceutical industry. © 2024 IEEE.

关键词： Optical tomography

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Machine Learning and MANET-Driven 5G Technology for Smart Healthcare: Enhanced Patient Diagnosis and Treatment 4

Machine Learning and MANET-Driven 5G Technology for Smart He...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Bhaskaranaik, S. Sreedevi, M. Rafick, S. Ravivarman, G. Rajendiran, M. Maranan, Ramya SVU College commerce management & Computer Science Department of Computer Science Andara Pradesh Tirupati India RVS College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Poonamallee Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This article presents the design, development, implementation, and evaluation of a system for patient health monitoring by synergistically combining machine learning models, mobile ad hoc networks (MANET), and Internet of Things (IoT). In this patient health monitoring system, data are gathered from numerous sensors installed on IoT devices in real-time to support dynamic and continuous health monitoring of patients within predefined constraints. The data are sent over MANET, which has nodes that send data to cloud storage for a centralized management. The data collected from the patients are used to train various Machine Learning (ML) models to learn when the patient is likely to experience a health problem. The analysis carried out in this study reveal that the Support Vector Machine (SVM) is found to be the most accurate model for predicting patient health problems, with an accuracy of 91.23%. The Artificial Neural Network (ANN), Random Forest (RF), and Naive Bayes (NB) rank next, respectively, with accuracy of 88.7%, 86.5%, and 84.4% respectively, following SVM. Recall, accuracy, and F1 score are given to provide comparative information on the relative merits of these models. This integrated healthcare monitoring system, with its timely alert system, real-time monitoring, and powerful predictive analytics, provide patients with personalized and proactive care. It enhances healthcare practices by showing that the data-driven strategic approach and its associated technologies are transforming patient outcomes in the emerging field of healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Autonomous Agricultural Monitoring Robot for Efficient Smart Farming 23

Autonomous Agricultural Monitoring Robot for Efficient Smart...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Al-Mashhadani, Zubaidah Park, Joon-Hyuk University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States University of Central Florida Department of Mechanical and Aerospace Engineering OrlandoFL32816 United States

ISBN: (纸本)9788993215274

The demand for precise agricultural monitoring grows as the global population increases. Robotic systems have emerged as invaluable tools, particularly in smart farming, with the potential to enhance crop efficiency. This paper presents the implementation of an autonomous robotic monitoring system tailored to agricultural fields. Focused on comprehensive tomato plant monitoring, including ripeness assessment, disease detection, and environmental monitoring, the study addresses the limitations of time-consuming and costly traditional methods. The proposed robotic system offers an affordable and efficient alternative to boost productivity rates. Built on the Robot Operating System (ROS) framework, the robot's navigation system utilizes the A∗ algorithm and Dynamic Window Approach (DWA) planners. Deep learning techniques were employed (YOLOV5) for autonomous 3-level ripeness classification and detection of nine tomato diseases. The integration of Internet of Things (IoT) technology enabled wireless soil monitoring through the robot. The robot achieved high accuracy in detecting the ripeness level in real-world validation. © 2023 ICROS.

关键词： Agricultural robots

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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4th IEEE International Conference on Emerging Frontiers in electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： SLAM robotics

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XCardio-Twin: An explainable framework to aid in monitoring and analysis of cardiovascular status 3

XCardio-Twin: An explainable framework to aid in monitoring ...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Krzysiak, Rafal An, Di Chen, YangQuan University of California - Merced Dept. of Mechanical Engineering Merced United States University of California - Merced Electrical Engineering & Computer Science Merced United States

ISBN: (纸本)9798350318470

We present an explainable digital-twin framework designed for cardiovascular health monitoring by integrating feature extraction using the NeuroKit2 and Heartpy python libraries and predictive modeling through machine learning. The digital-twin extracts key physiological features from ECG data, such as heart rate variability, and utilized machine learning algorithms to predict individual cardiovascular status. To improve trustworthiness, interpretability and transparency behind the decision making process, SHAP (Shapley Additive Explanations) was paired to help describe the underlying contributions of each feature to the model's decision-making process. The performance of the machine learning model scored a 90.08 % at accurately predicting abnormal or normal ECG signal. The combination of extraction of key features from ECG signals and a trained explainable machine learning model offers transparent, personalized insights into cardiovascular health. © 2023 IEEE.

关键词： Electrocardiograms

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Organoid intelligence:Integration of organoid technology and artificial intelligence in the new era of in vitro models

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Medicine in Novel Technology and Devices 2024年第1期21卷 39-44页

作者： Huaiyu Shi Andrew Kowalczewski Danny Vu Xiyuan Liu Asif Salekin Huaxiao Yang Zhen Ma Department of Biomedical&Chemical Engineering Syracuse UniversitySyracuseNYUSA BioInspired Institute for Material and Living Systems Syracuse UniversitySyracuseNYUSA Department of Mechanical&Aerospace Engineering Syracuse UniversitySyracuseNYUSA Department of Electrical Engineering&Computer Science Syracuse UniversitySyracuseNYUSA Department of Biomedical Engineering University of North TexasDentonTXUSA

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial ***,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in *** the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various *** intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to *** synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic *** Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.

关键词： Organoids Machinelearning Deep learning Artificial intelligence Stem cells

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