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Conventional/Phase Shift Dual Drive Mach-Zehnder Modulation Measured Type Based Radio over Fiber Systems

Journal of Optical Communications 2024年第s1期45卷 s123-s131页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Rahman, Ziaur Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt School of Computer Science and Software Engineering RMIT University 414-418 Swanston Street MelbourneVIC3000 Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh

This work clarified the conventional/phase shift dual Mach-Zehnder modulation measured technique for upgrading radio over fiber (ROF) systems. Four levels of pulse amplitude modulation and non-return to zero code scheme are employed in different previous studies for upgrading ROF systems. The possible transmission distance is extended to 150 km and a bit rate of 40 Gb/s for achieved acceptable max. Q factor of 13.7498 and minimum bit error rates of 1.82×10-43. The total optical received power is measured at fiber cable length for maximum distance. Also, in the same way, the signal power levels are measured after fiber optic cable and avalanche photo detector optical receiver. The proposed model has presented the enhancement percentage ratio of 13.654% over the previous models in signal quality enhancement. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Q factor measurement

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A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-30页

作者： Al-Samarraay, Mohammed S. Magableh, Aws A. Mahmood, Rana I. Joudar, Shahad Sabbar Zahid, Idrees A. Al-Obaidi, Jameel R. Al-Saffar, Ali Z. Albahri, A. S. Albahri, O. S. Alamoodi, A. H. Abdullah, Mohd Faizal Nizam Lee Electrical and Electronic Engineering Department College of Engineering Gulf University Sanad Bahrain Faculty of Information Technology and Computer Sciences Department of Information Systems Yarmouk University Irbid Jordan Department of Software Engineering Prince Sultan University Riyadh Saudi Arabia Department of Biomedical Engineering College of Engineering Al-Nahrain University Jadriya Baghdad Iraq University of Technology Baghdad Iraq Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Molecular and Medical Biotechnology College of Biotechnology Al-Nahrain University Baghdad Iraq Technical Engineering College Imam Ja’afar Al-Sadiq Baghdad Iraq University of Information Technology and Communications (UOITC) Baghdad Iraq Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Victorian Institute of Technology Melbourne Australia GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Science and Technology Mishref Kuwait Department of Mathematics Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a simulated dataset of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.

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Prediction of reinforced concrete structures behavior using finite element method

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Procedia Structural Integrity 2022年 36卷 177-181页

作者： O. Kononchuк V. Iasnii N. Lutsyk Ternopil Ivan Puluj National Technical University Faculty of Engineering of Machines Structures and Technologies 56 Ruska St. Ternopil 46011 Ukraine Ternopil Ivan Puluj National Technical University Faculty Of Computer Information Systems And Software Engineering 56 Ruska St. Ternopil 46011 Ukraine

In this paper the results of finite element modelling of bended reinforced concrete elements before and after reinforcing by carbon-fibre plastics under static loading are presented. Numerical results were performed taking into account non-linear behaviour of rebar and deformation of concrete with further cracking. A comparative analysis between obtained experimental results and calculated ones was performed and their well-enough matches were shown. Prediction of reinforced concrete structures behavior using finite element method was performed.

关键词： crack finite element method carbon fiber reinforced polymer structural elements strengthening stress rate

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Automatic Video Segmentation Based on Information Centroid and Optimized SaliencyCut

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Journal of computer Science & Technology 2020年第3期35卷 564-575页

作者： Hui-Si Wu Meng-Shu Liu Lu-Lu Yin Ping Li Zhen-Kun Wen Hon-Cheng Wong College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical *** the existing methods,the image information of another dimension is provided by the IC to enhance the video segmentation ***,our IC is implemented based on the information-level balance principle in the image,and denoted as the information pivot by aggregating all the image information to a *** effectively enhance the saliency value of the target object and suppress the background area,we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the *** saliency maps for all frames in the video are calculated based on the detected *** applying IC smoothing to enhance the optimized saliency detection,we can further correct the unsatisfied saliency maps,where sharp variations of colors or motions may exist in complex ***,we obtain the segmentation results based on IC-based saliency maps and optimized *** method is evaluated on the DAVIS dataset,consisting of different kinds of challenging *** with the state-of-the-art methods are also conducted to evaluate our *** visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.

关键词： automatic video segmentation information centroid saliency detection optimized SaliencyCut

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Facial Recognition Utilizing the CLM Model and Supervised Machine Learning: A Comparative Analysis

Facial Recognition Utilizing the CLM Model and Supervised Ma...

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Information Technology (ACIT)

作者： Musab Iqtait Jafar Ababneh Amer Abu-Jassar Mohammad Al-Refai Mohammad Dmour Mohammad Arabiat Ruaa Binsaddig Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan College of of Computer Sciences and Information Amman Arab University University Amman Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Finance College of Business Administration University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Facial recognition is a common challenge in artificial intelligence. We extensively utilized this program in our daily life. Face recognition software was implemented on many phones to protect user identities and promptly recognize Facebook users in photographs. Despite the numerous methodologies proposed to date, the actual implementation of facial recognition continues to pose significant challenges. Distinguishing individuals by a fundamental method relies on various factors, including lighting, postural differences, and partial facial obstruction. This study aims to develop a facial recognition technique utilizing a constrained local model (CLM) and a machine learning algorithm. It is evaluated with KNN, SVR, and CCA. Furthermore, it has employed CLM and CCA to achieve a maximum recognition accuracy of 3.98 MAE.

关键词： Machine learning algorithms Social networking (online) Face recognition Supervised learning Lighting Machine learning Nearest neighbor methods Feature extraction software Information technology

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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Computing, Robotics and System Sciences (ICRSS), International Conference on

作者： Shahid Karim Muhammad Ibrar Anas Bilal Akeel Qadir Irfana Bibi School of Information Engineering Xi'an Eurasia University Xi'an China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Lahore Punjab Pakistan

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou _ nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection.

关键词： YOLO Training Accuracy Sensitivity Service robots Neural networks Fires Clustering algorithms Smart homes Safety

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Characterization and Control of a Superconducting Homopolar Motor with Cryogenic GaN Inverter for Electric Aircrafts

Characterization and Control of a Superconducting Homopolar ...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Felix Gliese Matthew G. S. Pearce Aaron Wadsworth Mohammad Siamaki James Storey Grant Lumsden Duleepa J. Thrimawithana Rod Badcock Joerg Roth-Stielow Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand OpenStar Technologies New Zealand Robinson Research Institute Victoria University of Wellington New Zealand Institute for Power Electronics and Electrical Drives University of Stuttgart Germany

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor’s magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights.

关键词： Resistance Power system measurements Electric potential Torque Density measurement Cryogenics Motors Control systems Inverters Mathematical models

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Groove Gap Waveguide Crossover for Butler Matrices and Beamforming in Millimeter-Wave Satellite Antenna Systems

Groove Gap Waveguide Crossover for Butler Matrices and Beamf...

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International Conference on Radioelektronika

作者： Mohammad Alibakhshikenari Peiman Parand Ali Zidour Bal S. Virdee Lida Kouhalvandi Patrick Longhi Takfarinas Saber Ernesto Limiti Electronics Engineering Department University of Rome “Tor Vergata” Rome Italy Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara Boumerdes Algeria Center for Communications Technology London Metropolitan University London UK Department of Electrical and Electronics Engineering Dogus University Istanbul Turkiye Lero Science Foundation Ireland Centre for Software School of Computer Science University of Galway Galway Ireland

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

This paper presents an innovative H-plane crossover based on groove gap-waveguide (GGW) technology for high-performance millimeter-wave (mm-wave) circuits. The design facilitates the development of key transmission components, such as Butler matrices (BMs) and beamforming feeding networks (BFNs), for multi-beam antenna systems operating in the V-band spectrum (40–50 GHz). The proposed crossover is built by cascading two identical 3-dB/90° hybrid couplers. Each coupler is designed with GGW unit-cells constructed from metallic pins spaced less than a quarter-wavelength apart. This configuration creates a wide stopband of 20–57 GHz, ensuring minimal signal interference and strong impedance matching. The coupler achieves 90° phase shift, 50 dB isolation, and low insertion loss of 0.02 dB at 45 GHz, with a fractional bandwidth of 22.22%. The crossover demonstrates excellent performance over the entire V-band, making it suitable for advanced antenna systems in satellite communications and space applications. The design reduces complexity, cost, and losses typically associated with 3D and multilayer crossover technologies, providing a compact and efficient solution for mm-wave networks.

关键词： Butler matrices Satellite antennas Millimeter wave circuits Three-dimensional displays Array signal processing Impedance matching Transmitting antennas Gap waveguide Couplers Millimeter wave communication

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Correction to: A comprehensive performance evaluation of container migration strategies

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Computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Expert Phishing Detection System

Expert Phishing Detection System

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Ayoub Alsarhan Issa Al-Aiash Dimah Al-Fraihat Mohammad Aljaidi Dena Abd Al Hakem Abu Laila Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model's performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts.

关键词： Uniform resource locators Visualization Accuracy Machine learning algorithms Phishing Predictive models Feature extraction

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