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EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features 20

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mo...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Khalid, Beenish Hassan, Ali Munir, Ehsan Ullah Niazi, Imran Khan Dept. of Computer and Software Engineering Islamabad Pakistan Comsats University Islamabad Dept. of Computer Science Pakistan College of Chiropractic Centre of Chiropractic Research Auckland New Zealand

ISBN: (纸本)9798350331110

Electroencephalogram (EEG) signals, inherently non-stationary and non-linear, present significant challenges in their processing and interpretation. This paper presents a hybrid mode selection approach using two advanced decomposition methods: Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze these signals, targeting their application in the classification of upper limb complex movements for enhanced prosthetic limb control and rehabilitation therapy assessment. Using optimized statistical features extracted from selected modes, Intrinsic mode functions (IMFs) via EMD and modes via VMD, we seek to better distinguish neural activities in pre-movement EEG signal. Our methodology involves the following two strategies: straightforward extraction of statistical features from modes yielded by EMD and VMD;a genetic algorithm (GA) feature selection technique to select the most optimal set from these statistical features. These derived features train machine learning (ML) classifiers to differentiate limb movements. The results, derived from proprietary dataset from Aalborg University, Denmark, comprising five distinct upper limb movements, demonstrate the effectiveness of our hybrid approach. The usage of EMD and VMD significantly enhanced the discriminatory power of the extracted features, leading to improved classification performance. Furthermore, our hybrid approach yielded classification accuracies of 93.1 % and 95.6% with EMD and VMD respectively when the K-NN classifier was deployed with a 10-fold cross-validation. K-NN classifier outperformed traditional ML classifiers in terms of computational time, highlighting its potential as lightweight yet robust algorithm for classification of complex movements. The primary goal is to present and validate a hybrid mode (IMFs/modes) selection approach through EMD and VMD to analyze EEG signals associated with upper limb complex movements. © 2023 IEEE.

关键词： Empirical mode decomposition

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SWIN and Vision Transformer-Driven Crack Detection in the Al Qattara Oasis, UAE: Towards Sustainable Infrastructure Management 11

SWIN and Vision Transformer-Driven Crack Detection in the Al...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Ali, Luqman Ambali Parambil, Medha Mohan Swavaf, Muhammed Alnajjar, Fady Aljassmi, Hamad De Man, Adriaan De Emirates Centre for Mobility Research United Arab Emirates University Dept. Computer Science and Software Engineering Al Ain United Arab Emirates United Arab Emirates University Dept. Computer Science and Software Engineering Al Ain United Arab Emirates Emirates Centre for Mobility Research Dept. Civil and Enviromental Engieering Engieering Al Ain United Arab Emirates United Arab Emirates University Dept. Tourism and Heritage Al Ain United Arab Emirates

ISBN: (纸本)9798350367300

Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pressing need to preserve and maintain built heritage by employing modern technological solutions for infrastructure upkeep. Specifically, it focuses on crack detection, a critical aspect of ensuring the structural integrity of heritage buildings. Utilizing data collected from various structures across the UAE, obtained through handheld cameras, the effectiveness of Vision Transformers (ViTs) and Swin Transformers in identifying cracks within Al Qattara is assessed. The research thoroughly evaluates models, with particular attention to different input patch sizes. Through systematic experimentation and analysis over 100 epochs, ViT models with a patch size 16 exhibit significant promise in crack detection. Notably, the ViT-16 model achieves good performance metrics, including training and validation accuracies of 85% and (82%) respectively, and correctly classifies 791 out of 957 cracks and 772 out of 903 non-cracks. In contrast, Swin Transformers show lower validation accuracies (70-74%) and higher misclassification rates. The outcomes underscore the potential of ViT models in enhancing infrastructure maintenance efforts within heritage sites like Qattara Oasis. By combining advanced technology with a profound respect for historical preservation, this study aims to contribute to the sustainable conservation and protection of cultural heritage, ensuring that future generations can continue to appreciate the enduring legacy of sites such as Qattara Oasis. © 2024 IEEE.

关键词： Historic preservation

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An Introduction to Real-Time Object-Z

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Formal Aspects of Computing 2022年第2期13卷 128–141页

作者： Graeme Smith Ian Hayes Software Verification Research Centre University of Queensland Brisbane Australia AU School of Computer Science and Electrical Engineering University of Queensland Brisbane Australia AU

This paper presents Real-Time Object-Z: an integration of the object-oriented, state-based specification language Object-Z with the timed trace notation of the timed refinement calculus. This integration provides a method of formally specifying and refining systems involving continuous variables and real-time constraints. The basis of the integration is a mapping of the existing Object-Z history semantics to timed traces.

关键词： Keywords: Object-Z Real-time refinement Real-time specification Timed refinement calculus

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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SWIN and Vision Transformer-Driven Crack Detection in the Al Qattara Oasis, UAE: Towards Sustainable Infrastructure Management

SWIN and Vision Transformer-Driven Crack Detection in the Al...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Luqman Ali Medha Mohan Ambali Parambil Muhammed Swavaf Fady Alnajjar Hamad Aljassmi Adriaan De Man Dept. Computer Science and Software Engineering Emirates Centre for Mobility Research United Arab Emirates University Al Ain UAE Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE Dept. Civil and Enviromental Engieering Engieering Emirates Centre for Mobility Research Al Ain UAE Dept. Tourism and Heritage United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

关键词： Training Measurement computer vision Accuracy Systematics Pressing Transformers Maintenance Cultural differences Protection

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EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mo...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Beenish Khalid Ali Hassan Ehsan Ullah Munir Imran Khan Niazi Dept. of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan Dept. of Computer Science COMSATS University Islamabad Pakistan Centre of Chiropractic Research College of Chiropractic Auckland New Zealand

Electroencephalogram (EEG) signals, inherently non-stationary and non-linear, present significant challenges in their processing and interpretation. This paper presents a hybrid mode selection approach using two advanced decomposition methods: Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze these signals, targeting their application in the classification of upper limb complex movements for enhanced prosthetic limb control and rehabilitation therapy assessment. Using optimized statistical features extracted from selected modes, Intrinsic mode functions (IMFs) via EMD and modes via VMD, we seek to better distinguish neural activities in pre-movement EEG signal. Our methodology involves the following two strategies: straightforward extraction of statistical features from modes yielded by EMD and VMD; a genetic algorithm (GA) feature selection technique to select the most optimal set from these statistical features. These derived features train machine learning (ML) classifiers to differentiate limb movements. The results, derived from proprietary dataset from Aalborg University, Denmark, comprising five distinct upper limb movements, demonstrate the effectiveness of our hybrid approach. The usage of EMD and VMD significantly enhanced the discriminatory power of the extracted features, leading to improved classification performance. Furthermore, our hybrid approach yielded classification accuracies of 93.1 % and 95.6% with EMD and VMD respectively when the K-NN classifier was deployed with a 10-fold cross-validation. K-NN classifier outperformed traditional ML classifiers in terms of computational time, highlighting its potential as lightweight yet robust algorithm for classification of complex movements. The primary goal is to present and validate a hybrid mode (IMFs/modes) selection approach through EMD and VMD to analyze EEG signals associated with upper limb complex movements.

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Perspectives on the emerging field of autonomous systems and its theoretical foundations

Perspectives on the emerging field of autonomous systems and...

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2021 IEEE International Conference on Autonomous Systems, ICAS 2021

作者： Wang, Yingxu Plataniotis, Konstantinos N. Mohammadi, Arash Marcenaro, Lucio Asif, Amir Hou, Ming Leung, Henry Gavrilova, Marina University of Calgary FIEEEs Dept. of Electrical and Software Engineering Canada University of Toronto FIEEE Dept. of Electrical and Computer Engineering ON Canada Concordia University SMIEEEs Dept. of Computer Science Montreal Canada University of Genova SMIEEE Dept. of DITEN Italy SMIEEE Toronto Research Centre DRDC Canada University of Calgary SMIEEE Dept. of Computer Science Canada

ISBN: (纸本)9781728172897

Autonomous systems are advanced intelligent systems and general AI technologies triggered by the transdisciplinary development in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics. AS are driven by the increasing demands in the modern industries of cognitive computers, deep machine learning, robotics, brain-inspired systems, self-driving cars, internet of things, and intelligent appliances. This paper presents a perspective on the framework of autonomous systems and their theoretical foundations. A wide range of application paradigms of autonomous systems are explored. © 2021 IEEE.

关键词： Intelligent systems

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EEG Feature Extraction with Fast Fourier Transform for Investigating different Brain regions in Cognitive and Reasoning Activity 5

EEG Feature Extraction with Fast Fourier Transform for Inves...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Amin, Hafeez Ullah Hafeez, Yasir Reza, Mohammed Faruque Adil, Syed Hasan Hasan, Rumaisa Abu Ali, Syed Saad Azhar University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Sains Malaysia Dept. of Neurosciences Kota Bharu Kubang Kerian Malaysia Iqra University Software Engineering Department Karachi Pakistan Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

In this study, cortical brain activity during a pattern matching task (PMT) was measured by employing electroencephalography (EEG). The EEG data were recorded from 128 scalp locations during a pattern-matching task and in rest conditions (eyes open and eyes closed). Spectral Analysis of EEG frequency bands reflected a significant (p © 2022 IEEE.

关键词： Fast Fourier transforms

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Secure the 5G and Beyond Networks with Zero Trust and Access Control Systems for Cloud Native Architectures

Secure the 5G and Beyond Networks with Zero Trust and Access...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Hisham A. Kholidy Keven Disen Andrew Karam Elhadj Benkhelifa Mohammad A. Rahman Atta-Ur Rahman Ibrahim Almazyad Ahmed F. Sayed Rakia Jaziri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Networks and Comp. Security SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ. UK Dept of Electrical and Computer Engineering Florida International University USA Dept. of Computer Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ USA Transmission Dept. Telecom Egypt Fayoum Egypt Paragraphe Research Lab. Univ. of Paris VIII France

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

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