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Antennas and Propagation Society International Symposium

作者： David Chatzichristodoulou Photos Vryonides Dimitra Psychogiou Symeon Nikolaou Frederick Research Center Nicosia Cyprus RF and Microwave Solutions LTD Cyprus Dept. Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus School of Engineering University College Cork Cork Ireland Tyndall National Institute Cork Ireland

This paper discusses the RF design of a compact metasurface that enables directional propagation characteristics. It allows for circularly-polarized waves to penetrate through its volume in one direction of propagation whereas it absorbs them in the reverse one. Directionality is achieved by combining two transistors with two circularly-polarized patch antennas in each unit cell of the metasurface. A compact multi-layer integration scheme is proposed and is demonstrated through electromagnetic (EM) simulations at S-band. The proposed metasurface configuration exhibits directional characteristics with the following performance metrics. BW in the forward direction: 3.4-3.7 GHz, maximum Gain in the forward direction: 29 dB, isolation in the reversed direction> 50 dB.

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Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition 21

Texture and Orientation-based Feature Extraction for Robust ...

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21st IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2023

作者： Khan, Sajid Ali Bin Abdulrahman Alawairdhi, Mohammed Al-Akhras, Mousa Foundation University Islamabad Department of Software Engineering Islamabad Pakistan Saudi Electronic University College of Computing and Informatics Saudi Arabia The University of Jordan Amman King Abdullah II School for Information Technology Computer Information Systems Department Jordan

ISBN: (纸本)9798350345889

Facial expressions are the most effective way to characterize people's motives, emotions, and feelings. Several new methods are proposed each year;however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods. © 2023 IEEE.

关键词： Local binary pattern

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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PLC and Human Machine Interface based PV Condition Monitoring Structure Comprising an Innovative Fault Detection Scheme

PLC and Human Machine Interface based PV Condition Monitorin...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Amir Nedaei Aref Eskandari Nima Pourshahbaz Sherko Salehpour Parviz Parvin Andreas Livera George Makrides Mohammadreza Aghaei Department of Electrical Engineering Amirkabir University of Technology (AUT) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology (IUST) Tehran Iran Department of Physics and Energy Engineering Amirkabir University of Technology (AUT) Tehran Iran Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus (UCY) PHAENTON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus (UCY) PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Ocean Operations and Civil Engineering Norwegian University of Science and Technology (NTNU) Ålesund Norway Department of Sustainable Systems Engineering (INATECH) University of Freiburg Freiburg Germany

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic (PV) systems operate outdoors and are inevitably exposed to various environmental stresses. These stresses may result in several faults and failures which can affect the PV proper performance in terms of reducing the output power and even in some catastrophic cases may end in fire hazards. Therefore, PV systems need to be constantly and carefully monitored to maintain their appropriate performance. In this paper, a condition monitoring (CM) scheme is proposed based on the Programmable Logic Controller (PLC). The scheme is capable to detect line-to-line (LL) and line-to-ground (LG) faults on the DC-side of the PV array and also enables the system to track the maximum power point (MPP) and provides the operator with the real-time PV information via Human-Machine Interface (HMI).

关键词： Condition monitoring Photovoltaic systems Human-machine systems Programmable logic devices Programmable logic arrays Real-time systems Hazards Logic Stress Monitoring

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A high granularity state-space method for contaminant detection and isolation in intelligent buildings 16

A high granularity state-space method for contaminant detect...

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16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020

作者： Kyriacou, Alexis Michaelides, Michalis Panayiotou, Christos Polycarpou, Marios KIOS Research and Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9781713823605

Conservation of the Indoor Air Quality is essential in modern, energy efficient, intelligent buildings. However, accidents or malicious acts that often result in various airborne contaminant releases can endanger the occupants' wellbeing. In this work, an indoor air quality monitoring methodology is presented for detecting and localizing a contaminant source in the 3D indoor environment. Specifically, a state-space model is used to describe the contaminant dispersion in a zone which is discretised into multiple cuboid cells. The airflow exchange between the cells is computed based on a 3D discretized coarse-grid CFD analysis. A contaminant detection and localization methodology that considers modelling uncertainty and measurement noise, is adapted and applied to the CFD-based state-space model for detecting the existence of a possible contaminant source and estimating its location within the zone. The performance of the approach is illustrated through simulation examples. © 2020 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020. All rights reserved.

关键词： Uncertainty analysis

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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A Novel Diagnostic Model for Early Detection of Alzheimer’s Disease Based on Clinical and Neuroimaging Features 11th

A Novel Diagnostic Model for Early Detection of Alzheimer’s...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Gad, Eyad Gamal, Aya Elattar, Mustafa Selim, Sahar School of Engineering and Applied Sciences Nile University Giza Egypt Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt

ISBN: (纸本)9783031215940

Alzheimer’s Disease (AD) is a dangerous disease that is known for its characteristics of eroding memory and destroying the brain. The classification of Alzheimer's disease is an important topic that has recently been addressed by many studies using Machine Learning (ML) and Deep Learning (DL) methods. Most research papers tackling early diagnosis of AD use these methods as a feature extractor for neuroimaging data. In our research paper, the proposed algorithm is to optimize the performance of the prediction of early diagnosis from the multimodal dataset by a multi-step framework that uses a Deep Neural Network (DNN) as an optimization technique to extract features and train these features by Random Forest (RF) classifier. The results of the proposed algorithm showed that using only demographic and clinical data results in a balanced accuracy of 88% and an area under the curve (AUC) of 94.6. Ultimately, combining clinical and neuroimaging features, prediction results improved further to a balanced accuracy of 92% and an AUC of 97%. This study successfully outperformed other studies for both clinical and the combination of clinical and neuroimaging data, proving that multimodal data is efficient in the early diagnosis of AD. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neuroimaging

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA 4

An Exploratory Investigation into Sustaining Cybersecurity P...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Abdullah Huda, Mahfuzul Brayyich, Mohammed Haroon, Noor Hanoon Khan, Abdul Azeem Verma, Rajan University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq Negara Brunei Darussalam Faculty of Islamic Technology Brunei Lovely Professional University Division of Research and Development Phagwara144411 India KKR & KSR Institute of Technology & Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services. © 2024 IEEE.

关键词： Personnel training

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Cyber Threat Intelligence in Smart Cities: Bayesian Inference and Energy Optimization in LoRa Networks for Big Data Applications

Cyber Threat Intelligence in Smart Cities: Bayesian Inferenc...

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IEEE International Conference on Big Data

作者： Aristeidis Karras Leonidas Theodorakopoulos Christos Karras Hera Antonopoulou Computer Engineering and Informatics Department University of Patras Patras Greece Department of Management Science and Technology University of Patras Patras Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In the evolving landscape of smart cities, optimizing energy consumption and enhancing cybersecurity in Internet of Things (IoT) networks are crucial. This study leverages LoRa (Long Range) technology, Bayesian Inference, and Extreme Learning Machines (ELMs) to advance cyber threat intelligence and energy efficiency in large-scale IoT deployments. The proposed algorithms address key challenges within LoRa networks by implementing a novel energy consumption model, kernel-based ELM fine-tuning, Bayesian parameter tuning, and data fusion. Through Bayesian Inference, our approach dynamically adjusts network parameters to optimize packet transmission and collision rates, ultimately reducing power consumption across smart city applications. The Kernel-ELM algorithm fine-tunes LoRa network applications by adapting kernel parameters to the unique demands of IoT environments. Additionally, our Anomaly Detection and Fusion Algorithm (ADFA) integrates data from multiple sources to detect potential cyber threats, enhancing network security. Experimental results validate these algorithms on various datasets, demonstrating improvements in both energy optimization and threat detection. Ultimately, this study provides actionable insights into the deployment of scalable, energy-efficient, and secure IoT networks for smart city infrastructure.

关键词： Energy consumption Smart cities Heuristic algorithms LoRa Inference algorithms Energy efficiency Bayes methods Cyber threat intelligence Internet of Things Optimization

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An Intelligent Condition Monitoring Scheme For Photovoltaic Systems using Programmable Logic Controller

An Intelligent Condition Monitoring Scheme For Photovoltaic ...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Environmental and other stresses are constantly affecting photovoltaic (PV) components which can end in various faults. The anomalous condition can reduce the PV output power or even result in a calamitous fire hazard. Hence, PV systems must be monitored constantly in order to ensure their proper performance. This paper proposes an intelligent PV condition monitoring scheme based on Programmable Logic Controller (PLC). This scheme monitors the PV performance and detects different faults and failures on the DC-side of the array. It also takes advantage of Human-Machine Interface (HMI) which provides the operators with useful real-time data from the array.

关键词： Condition monitoring Photovoltaic systems Human-machine systems Programmable logic devices Real-time systems Arrays Logic Monitoring Stress Logic arrays

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