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10th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2021

作者： Kamel, eng. M. Massoud, Yasmin M. El Ghany, Mohamed A. Salem, Mohammed A.-M. German University in Cairo Media Eng. Tech. Cairo Egypt German University in Cairo Elec. Eng. Dept. Cairo Egypt Int. Elec. Sys. Lab Tu Darmstadt Germany Computer & Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781665440769

Epilepsy is a brain disorder in which abnormal brain activity occurs, causing seizures. Epilepsy causes sudden seizures which affects patients medical and social life. Prior investigations for seizure treatment utilized seizure prediction to offer a solution for epileptic seizures. The past two decades witnessed several advances in classification of elec.roencephalogram (EEG) to assess in seizure detection. Advancements in this field include in-vivo devices that measure brain neural activity, however this damages brain tissues and has an irreversible effect. In this paper, the major problem of seizure classification for drug-resistant epileptic patients using two different algorithms is presented. Two cascaded classification models, SVM and deep ANN, are presented and implemented. The models were created in need of both feature extraction and classification of both pre-ictal and inter-ictal EEG signals. In addition, high precision and accuracy was a main thought in requirement to show that these classifiers are effective compared to other trials in such path. The aim was fairly reached with an accuracy of 97% for the first classification method and 96.7% with cross validation, whereas the second classification model resulted in a accuracy of 92% and 92.3% with cross validation. © 2021 IEEE.

关键词： elec.roencephalography

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IoT-Enabled Low-Cost Fog Computing System with Online Machine Learning for Accurate and Low-Latency Heart Monitoring in Rural Healthcare Settings

arXiv

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arXiv 2023年

作者： Maneshti, Hamidreza Dadashi, Morteza Rostami, Kamyar Dept. Computer Eng. & Information Tech Engineering Kermanshah Iran Dep. Computer Science Montreal Canada

Healthcare services in rural areas face numerous challeng.s due to the high cost of treatment and a lack of appropriate services. The application of Internet of Things (IoT) technology has shown potential in mitigating these issues. This article discusses the potential of Internet of Things (IoT) and fog computing to reduce healthcare costs and improve patient outcomes. The use of these technologies in cardiovascular health informatics is explored, along with the economic thought process of hospital decision-makers and end-of-life practices in intensive care units. Remote monitoring using IoT devices is highlighted as a promising way to detect health issues before they become serious, leading to earlier interventions and improved health outcomes. The use of fog computing in healthcare is also discussed, with a focus on its ability to provide real-time data processing, analysis, and decision-making capabilities. The article presents a novel architecture for Device-as-a-Service, utilizing both fog and cloud computing to improve the efficiency and accuracy of ECG device processing, and concludes that it has the potential to reduce costs by up to 80% in the Iranian market. The adoption of fog computing in healthcare is acknowledged to present significant challeng.s, such as security and privacy concerns, but its potential benefits make it a promising technology for the future of healthcare services. © 2023, CC BY.

关键词： elec.rocardiograms

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Reputation-Based Trust Assessment of Transacting Service Components

SSRN

引用

SSRN 2024年

作者： Tsiounis, Konstantinos Kontogiannis, Kostas Western University Computer Science Dept LondonON Canada York University Dept. of Electrical Eng. & Comp. Science TorontoON Canada

Complex service-oriented systems rely heavily on dynamically provisioned components which most often eng.ge in ad-hoc interactions in a distributed manner and across many different servers, providers, jurisdictions and diverse platforms. In such systems, a key issue that emerges is how a service client component can trust the services offered by a service provider component. Here, we consider trust as a meta-requirement that is, the level of belief a service client has that a service provider will indeed provide the service, or data, at a quality level that meets its expectations. In this paper, we propose a technique that allows for evaluating and assigning trust to various service providers by considering first how service providers fulfill their clients’ quality expectations, and second by taking into account the reputation other service clients have, when these act as recommenders for the aforementioned service providers. The approach forms a type of a "social network'' of service providers who offer services, service clients who request the use of services, and service clients who act as recommenders. In this work, service clients and service providers are considered software applications and may include micro-services, software agents, smart contracts or any other distributed inter-networked resource. © 2024, The Authors. All rights reserved.

关键词： Software agents

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Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification

Uncertainty Propagation in Stochastic Systems via Mixture Mo...

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IEEE Conference on Decision and Control

作者： Eduardo Figueiredo Andrea Patane Morteza Lahijanian Luca Laurenti Delft Center for Systems and Control TU Delft School of Computer Science and Statistics Trinity College Dublin Dept. of Aerospace Eng. Sciences and Computer Science CU Boulder

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work, we focus on discrete-time non-linear stochastic dynamical systems. We present a novel approach to approximate the distribution of the system over a given finite time horizon with a mixture of distributions. The key novelty of our approach is that it not only provides tractable approximations for the distribution of a nonlinear stochastic system but also comes with formal guarantees of correctness. In particular, we consider the Total Variation (TV) distance to quantify the distance between two distributions and derive an upper bound on the TV between the distribution of the original system and the approximating mixture distribution derived from our framework. We show that in various cases of interest, including in the case of Gaussian noise, the resulting bound can be efficiently computed in closed form. This allows us to quantify the correctness of the approximation and to optimize the parameters of the resulting mixture distribution to minimize such distance. The effectiveness of our approach is illustrated on several benchmarks from the control community.

关键词： Measurement Uncertainty TV Upper bound Stochastic systems Stochastic processes Mixture models Machine learning Probability distribution Dynamical systems

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CART-II: Development of Collision Avoidance Robotic Tether with Soft Sensing Capabilities for Underwater Nuclear Inspection Vehicles

CART-II: Development of Collision Avoidance Robotic Tether w...

引用

International Conference on Autonomous Robots and Agents, ICARA

作者： Ammar K. Al Mhdawi Nick Wright Simon Benson Maryam Haroutunian Dept. of Computer Science and Eng. Edge Hill University United Kingdom Dept. of EE Eng. Newcastle University United Kingdom Dept. of Marine Eng. Newcastle University United Kingdom

In nuclear inspection environments, a tether cable is used to transfer power and data between the underwater robotic system and the surface control unit. During underwater nuclear inspection, the tether cable can become entangled and loop with the environment such as nuclear waste boxes and objects. The risk of colliding with underwater objects is increased by the presence of more inspection robots underwater, especially if they are equipped with manipulator arms. As a result of the loops and knots around the cable, the inspection process may be affected and the ROV may not be able to perform its job. The present article is an extended development of the previous Collision Avoidance Robotic Tether (CART-I) model [1]. The CART-I system consists of micro thrusters that are attached to the base unit by a tether cable. The micro thrust unit is capable of generating a small amount of thrust that can move the tether away from obstacles in the water, particularly in restricted spaces. The use of light detection technologies such as IR or LiDAR for obstacle detection is not effective underwater due to the complex motion dynamics of the tether underwater and the size of obstacles, which makes it impossible to provide definite identification of the objects within a given time period. In order to provide the surroundings of the micro thrust units with obstacle detection capability, we have developed an autonomous force soft sensor. Additionally, the soft moulded sealed encase was developed for effective force detection underwater, and was experimentally tested in a water tank to validate our proposed design. Simulation and experimental results of the sensor is provided. The overall goal of the CART-II is to provide a smart autonomous vision of obstacle avoidance using soft force sensing capabilities. This paper presents the full kinematic model and the simulation with finite element analysis of the CART-II system with the hardware and physical implementation of the soft sensor

关键词： Underwater cables Radioactive pollution Soft sensors Force Inspection Robot sensing systems Manipulators

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Tensile Testing Based on the Polarization-maintaining Fiber Powered by an SM Optical Fibre 4

Tensile Testing Based on the Polarization-maintaining Fiber ...

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4th International Conference on elec.rical, computer, Communications and Mechatronics eng.neering, ICECCME 2024

作者： Kyselak, Martin Vavra, Jiri Vylezich, Zdenek Grenar, David Slavicek, Karel University of Defense Faculty of Military Technology Department of Electrical Engineering Brno Czech Republic University of Technology Faculty of Electrical Eng. and Communication Dept. of Telecommunications Brno Czech Republic Institute of Computer Science Masaryk University IT Infrastructure Division Brno Czech Republic

ISBN: (纸本)9798350391183

Optical fibre sensors are currently used to detect many non-elec.rical phenomena. The latest sensors analyze the polarization properties of light inside an optical fiber. Unlike interferometric methods, this method uses the interference of two planes of polarization at the output of the optical fibre. The sensors are divided into linear polarizer sensors or multi-fibre interference sensors based on different ways of converting the polarization state into a change in the intensity of the optical radiation. These sensors are particularly suitable for low frequency vibration measurements due to the transformation of beats into the longitudinal deformation of the optical fibre. The aim of the presented measurements was to demonstrate that these polarization vibration sensors have excellent sensitivity and are thus suitable for vibration detection. This paper demonstrates that polarization sensors have excellent sensitivity, are convenient and inexpensive to install, and, due to their non-elec.ric nature, are suitable for hazardous environments. In addition, it is possible to employ these sensors as intrusion detection and prevention systems and remotely power them at a distance of tens of kilometers. The motivation for this research was the lack of information and equipment for vibration testing of optical fibres and the associated polarization changes. © 2024 IEEE.

关键词： Tensile testing

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BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations

BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neur...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Kaiqiao Han Yi Yang Zijie Huang Xuan Kan Ying Guo Yang Yang Lifang He Liang Zhan Yizhou Sun Wei Wang Carl Yang Dept. of Computer Science University of California LA Los Angeles USA Dept. of Computer Science Duke University Durham USA Dept. of Computer Science Emory University Atlanta USA Dept. of Biostat. & Bioinf. Emory University Atlanta USA Dept. of Computer Science Zhejiang University Hangzhou China Dept. of Computer Sci. & Eng. Lehigh University Bethlehem USA Dept. of ECE and BioE University of Pittsburgh Pittsburgh USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. However, widely used brain signals such as Blood Oxygen Level Dependent (BOLD) time series generated from functional Magnetic Resonance Imaging (fMRI) often manifest three challeng.s: (1) missing values, (2) irregular samples, and (3) sampling misalignment, due to instrumental limitations, impacting down-stream brain network analysis and clinical outcome predictions. In this work, we propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals using Ordinary Differential Equations (ODE). By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point, mitigating the aforementioned three data challeng.s of brain signals altogether. Comprehensive experimental results on real-world neuroimaging datasets demonstrate the superior performance of BrainODE and its capability to address the three data challeng.s.

关键词： Neuroimaging Phenotypes Oxygen Time series analysis Network analyzers Ordinary differential equations Functional magnetic resonance imaging Brain modeling Mathematical models Signal analysis

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Influence of Quantum Computing on IoT Using Modern Algorithms 4

Influence of Quantum Computing on IoT Using Modern Algorithm...

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4th International Conference on Advanced science and eng.neering, ICOASE 2022

作者： Ageed, Zainab Salih Zeebaree, Subhi R. M. Saeed, Rezgar Hasan Nawroz University Computer Science Dept. Duhok Iraq Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq Duhok Private Technical Institute Information Technology Dept. Duhok Iraq

ISBN: (纸本)9781665472227

Even while the Internet of Things (IoT) already affects our day-to-day activities, its future relevance and potential for transformation remain untapped. Security issues with present communications technology must be solved before secure end-to-end connection between services can be achieved. Internet of Things (IoT) is here to stay. In the next years, it will become an integral part of our everyday life. There is a good chance that sensor-based networks can recognize it as a direct service provider in our surroundings. Even if it's just in the form of a value-added service delivered through cellular networks, it's going to be helpful nevertheless. It is, nevertheless, subject to a wide range of security threats. The current level of security is insufficient for IoT applications in the future. A secure cryptosystem is needed for the Internet of Things. quantum-based security has seen a surge of attention recently. Additional quantum key distribution systems and network services are now supported by this solution. As a result of the quantum computing's unrivaled security level, it has become more popular in recent years. Because every measurement must affect the state of the quantum bit being sent, quantum physics dictates that this must be the case. Regardless of whether the sender or recipient is aware of the change, it is clear. It is thus no longer possible to listen passively. Polarized photons may encode a string of bits using protocols like BB84. Secure cryptographic keys may be generated over an unsafe channel utilizing various key distillation procedures. © 2022 IEEE.

关键词： Internet of things

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elec.roencephalographic Biomarkers for Major Depressive Disorder and Alcohol Use Disorder

Electroencephalographic Biomarkers for Major Depressive Diso...

引用

International Conference on elec.rical, elec.ronics and System eng.neering (ICEESE)

作者： Muhammad Nur Aiman Haja Maidin Norashikin Yahya Mohamad Naufal Mohamad Saad Danish M. Khan Nidal S. Kamel Electrical & Electronics Eng. Dept. Universiti Teknologi PETRONAS Perak Malaysia Dept. of Computing & Information Systems Sunway University Selangor Malaysia College of Eng. & Computer Science VinUniversity Hanoi Vietnam

ISBN: (数字)9798350367447

ISBN: (纸本)9798350367454

Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD) are common neurological disorders that significantly impact physical health, family life, and work. Accurate diagnosis is essential for early intervention and treatment. However, conventional DSM-based diagnosis is subjective and susceptible to errors due to patient dishonesty or memory lapses. This study introduces a more reliable and objective diagnostic approach using effective connectivity (EC) from EEG recordings of 30 healthy controls, 30 MDD, and 30 AUD patients. EC, calculated via partial directed coherence, was used as input for an SVM model, achieving a peak classification accuracy of 95.3%, with perfect accuracy in the beta and gamma rhythms. These findings underscore EC’s potential as biomarkers for more accurate diagnosis and early intervention in neurological disorders.

关键词： Support vector machines Neurological diseases Accuracy Coherence Biomarkers Depression Systems eng.neering and theory elec.roencephalography Recording Reliability

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Research on Steganography Course with Large Language Model ChatGPT Assisted Learning 24

Research on Steganography Course with Large Language Model C...

引用

3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Hwang, Min-Shiang Fatima, Kanza Chan, Chi-Shiang Wu, Chia-Chun Dept. of C.S. and Info. Eng. Asia University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798400712692

In this study, by integrating practice-oriented teaching methods and combining large language models to assist learning and teaching situations, it is proposed that the Steganography course is practical-oriented and integrates ChatGPT with the Steganography language model teaching and teaching established by the applicant. In addition to focusing In addition to cultivating students with Steganography concepts and practical operational abilities, we hope to cultivate students' abilities of active observation, independent research, and critical thinking so that students can meet the technology industry's professional skills and requirements for Steganography studies. This research explores the pre-test and post-test, of course, students' use of large language models to learn to have higher learning attitudes and achievements towards Steganography theory and practice. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Steganography

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