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21st International Conference on Advances in ICT for Emerging Regions, ICter 2021

作者： Sirinayake, Spss Dasanayake, Dkak Rodrigo, Tmlu Perera, Wuy Hansika Mahaadikara, Mdjt Kahandawala, Surath Sri Lanka Institute of Information Technology Faculty of Computing Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665466837

Humans start their day by looking in the mirror at least once before leaving their homes every morning. In addition, they waste some considerable time of their busy workload in front of the mirror. To make this time more productive and useful, there ought to be a system that can be readily conducted, user-friendly, and smart according to the constant progress on the Internet of Things. The intelligent mirror is a new addition to the smart device family, which is a straightforward concept. There will be a screen placed behind a two-way mirror, and this Intelligent Mirror turns our room or bathroom mirror into a personal assistant with artificial intelligence. The purpose is to develop a smart mirror that can automate working humans' busy daily routines and manage their tasks when they spend their time in front of a mirror. To make the most of this moment, users can securely access all the relevant details of the day by looking in the mirror simultaneously. The intelligent mirror, which a single voice command can activate, will significantly help disabled persons and the general. Raspberry Pi has been used to build the proposed intelligent mirror, linked to the digital world via the Internet. The mirror can communicate with the user through voice commands and reply appropriately. The monitoring of emotions and health measuring function will provide a distinctive experience to the users. The mirror will reflect important elements such as weather, date & time, covid-19 situation reports, local news, To-do list, water reminder, home workouts, and meal plans. The mirror can also handle specialized functions such as automating and controlling home IoT devices. © 2021 IEEE.

关键词： Internet of things

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Application of Computer Vision Techniques to Identify Spray Primary Breakup Structures in High-Speed Flow

Application of Computer Vision Techniques to Identify Spray ...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Pathan, Refat Khan Lim, Wei Lun Lau, Sian Lun Ho, Chiung Ching Bravo, Luis Koneru, Rahul Babu Khare, Prashant School of Engineering and Technology Sunway University Dept. of Computing and Information Systems Bandar Sunway Malaysia School of Engineering and Technology Sunway University Dept. of Engineering Bandar Sunway Malaysia Sunway University Sunway DataXSight Research Cluster Bandar Sunway Malaysia Devcom Army Research Laboratory Army Research Directorate MD21005 United States University of Cincinnati Department of Aerospace Engineering CincinnatiOH45221-0070 United States Engineering University of Maryland Department of Aerospace College ParkMD20742 United States

ISBN: (纸本)9798350302684

Accurate analysis of the spray formation process is of utmost importance because it governs the fuel injector performance in air-breathing engines. Historically, optical diagnostic measurements in this region have been limited, hindering progress in understanding the underpinning physics. Due to the rapid growth in computer power, first principle simulations of the primary breakup process are now available, providing access to the full spatiotemporal fields. This data is essential to investigate the physical instability modes. However, it typically relies on subjective criteria to identify the emerging structures in the spray formation process. Since computer vision tools are known to help automate such classification tasks, this work focuses on developing a framework that can be applied to help identify critical regions during the breakup process. The emerging spray structures identified and tracked include the well-known lobes, ligaments, and droplets that arise during the fuel injection process. Analysis shows that our approach yields promising results providing helpful statistics of the fundamental breakup dynamics for engineering analysis. © 2023 IEEE.

关键词： Optical data processing

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Variations of total electron content during quiet and disturbed geomagnetic conditions over Malaysia 6

Variations of total electron content during quiet and distur...

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6th IEEE International Symposium on Telecommunication Technologies, ISTT 2022

作者： Mostofa, Sadia Abdullah, Mardina Islam, Mohammad Tariqul Bahari, Siti Aminah Ahmmed, Mohammad Badal University Kebangsaan Malaysia Space Science Center Institute of Climate Change Dept. of Electrical Electronic and Systems Engineering Selangor Bangi43000 Malaysia University Malaysia Pahang Faculty of Computing Pekan26620 Malaysia

ISBN: (数字)9781665489423

ISBN: (纸本)9781665489423

Ionosphere behavior is characterized by geographical and temporal changes. It was possible to determine the mobility of the ionosphere by observing how the total electron content (TEC) changed over time. The study on TEC variation was conducted in 2008 and 2009 over Malaysia, where latitude ranged from 1° N to 8° E and longitude ranged from 95° to 120° E, with each year reflecting the same degree of solar activity. The hourly mean vertical TEC (VTEC) is used to monitor the difference of the TEC under various geomagnetic factors such as disturbance storm time (Dst), planetary 3-hour range index (Kp), and solar flux F10.7 variations. This paper considers four cases of geomagnetic disturbances were considered 9 March, 27 March, 4 September 2008, and 22 July 2009. A percentage deviation was calculated to further analyze the geomagnetic disturbances in the equatorial region on quiet days. Results showed the TEC during geomagnetic disturbances was lower than the quiet day, with the percentage deviations between -20% and 10%. An increment of TEC was shown after approximately 24 hours, which is in the storm recovery phase. Also found were that GPS-TEC reaches its highest around post noon and its lowest in the small hours of the morning. © 2022 IEEE.

关键词： Ionosphere

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Proactive Network Traffic Prediction using Generative Adversarial Network

Proactive Network Traffic Prediction using Generative Advers...

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International Conference on Information Networking

作者： Gyurin Byun Van-Vi Vo Syed M. Raza Duc-Tai Le Huigyu Yang Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of Superintelligence Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Proactive traffic management in 5G networks is crucial for optimizing network efficiency, ensuring quality of service, adapting to dynamic conditions, and enhancing the user experience. In this paper, we propose a Generative Adversarial Network (GAN) architecture that leverages spatiotemporal features in network traffic data to predict future traffic. Our approach incorporates a Convolutional Long Short-Term Memory (ConvLSTM) model within the generator of the GAN, which collaborates with the discriminator, utilizing a Convolutional Neural Network (CNN) model, to provide essential feedback for training the generator. This integration ensures that our model not only predicts future traffic with improved accuracy but also adapts to dynamic network conditions. Based on experimental results using network traces, our model significantly outperforms the baseline, reducing prediction error by 12% while forecasting network traffic for the next 1 minute. These findings represent a significant advancement in proactive network management, particularly in addressing the challenges posed by real-time streaming and other latency-sensitive applications in 5G networks.

关键词： Training Adaptation models 5G mobile communication Telecommunication traffic Predictive models Generative adversarial networks Generators

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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A Comparative Study of Hough Transform and PCA for Bolt Orientation Detection

A Comparative Study of Hough Transform and PCA for Bolt Orie...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Antonio Gambale Sonya Coleman Dermot Kerr Philip Vance Emmett Kerr Cornelia Fermuller Yiannis Aloimonos School of Computing Engineering & Intelligent Systems Ulster University Londonderry Northern Ireland Dept. of Electronic & Mechanical Engineering Atlantic Technological University Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland USA

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In the fields of manufacturing and robotics, accurately determining the orientation of manufacturing components, such as bolts, is a critical yet challenging problem due to the limitations of existing detection methods. This study introduces a novel methodology for addressing this issue, leveraging traditional computer vision techniques, by proposing a streamlined approach that exploits the inherent geometric properties of bolts for orientation detection. Two methods are presented to ascertain the initial axis angle of the bolt: the Progressive Probabilistic Hough Transform (PPHT) and Principal Component Analysis (PCA). These methods are used in conjunction with a novel tip direction detection approach. The results of the study demonstrate consistent accuracy in angle determination, with PPHT and PCA both achieving angular deviations below ±0.5° in the simple dataset, and PCA showing enhanced robustness in the dataset degraded by shadows, with a maximum error under ±1.5°. This research not only reaffirms the viability of fundamental computer vision techniques in modern robotic applications but also sets a precedent for simple, generalisable, and reliable orientation detection solutions. These methods effectively bridge the gap between highly specialised machine learning systems, which often require tailored, complex models and extensive training data, and more universally applicable, straightforward approaches.

关键词： Computer vision Training data Transforms Machine learning Fasteners Probabilistic logic Robustness Manufacturing Informatics Principal component analysis

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Transient stability improvement of Croatian power system using FACTS

Transient stability improvement of Croatian power system usi...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Anica Šešok Ivica Pavić Dept. of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (纸本)9798350399677

In the last twenty years, the share of renewable energy sources (RES) in total electricity production has increased significantly, which has led to certain problems in power system operation. In systems with a significant share of RES, connected to the transmission network via power electronics devices, the inertial response of the system is reduced. Due to the reduced inertia of the power system, synchronous generators are more sensitive to their disturbed states and as a result, can easily lose stability. Loss of stability of one generator can lead to failure of other system elements, which may ultimately result in partial or even complete breakdown of the power system. Recently, a large increase in production from renewable sources has been recorded in the Croatian power system, therefore this paper investigates their impact on the system's transient stability and the possibilities of its improvement using Flexible AC Transmission systems (FACTS).

关键词： Renewable energy sources Flexible AC transmission systems Asia Production Power system stability Stability analysis Synchronous generators

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Artificial Intelligence in Medicine: A review of challenges in implementation and disparity

Artificial Intelligence in Medicine: A review of challenges ...

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2021 IEEE International Conference on Health, Instrumentation and Measurement, and Natural Sciences, InHeNce 2021

作者： Rathinam, Alwin Kumar Lee, Yunli Chek Ling, David Ngo Singh, Rajesh Selvaratnam, Lakshmi Pamidi, Narendra Bandar Sunway Malaysia Sunway University School of Engineering and Technology Dept. of Computing and Information Systems Bandar Sunway Malaysia Putrajaya Malaysia

ISBN: (纸本)9781665441810

Artificial Intelligence (AI) has garnered wide interest both in the field of computer science and in the eyes of the general public. AI research has proven to be exciting and has huge potential to augment and improve many fields. The question remains though, whether AI can be implemented successfully in the fields of medicine and healthcare. Medical practice is governed by clinical guidelines, protocols and standards in order to ensure positive outcomes for the patient. Various disciplines within medicine have increasingly adopted the use of technology such as databases and communication systems to enhance healthcare services. Currently, AI has limited adoptability in clinical use, possibly due to challenges in implementation and existence of disparity in communication between AI scientists and medical personnel. In this review, we explore the key challenges that AI researchers face in terms of persuading medical practitioners to utilize AI systems for clinical practice. Industry best practices such as the development of specific quality standards and enhancing interdisciplinarity could help to bridge the gap between computer scientists, medical practitioners and medical administrators. © 2021 IEEE.

关键词： Medical imaging

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Experimental Analysis of U-Net and Mask R-CNN for Segmentation of Synthetic Liquid Spray

Experimental Analysis of U-Net and Mask R-CNN for Segmentati...

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2022 IEEE International Conference on computing, ICOCO 2022

作者： Pathan, Refat Khan Lim, Wei Lun Lau, Sian Lun Ho, Chiung Ching Khare, Prashant Koneru, Rahul Babu Sunway University School of Engineering and Technology Dept. of Computing and Information Systems Bandar Sunway Malaysia Sunway University School of Engineering and Technology Dept. of Engineering Bandar Sunway Malaysia Sunway University Sunway DataXSight Research Cluster Bandar Sunway Malaysia University of Cincinnati Department of Aerospace Engineering CincinnatiOH45221 United States University of Maryland Department of Aerospace Engineering College ParkMD20742 United States

ISBN: (纸本)9781665489966

In digital image processing, segmentation is a process by which we can partition an image based on some variables to extract necessary elements. Unlike typical objects, it is complicated to segment dynamic objects from a synthetic fluid dataset where properties like position and shape change over time. Experiments on image segmentation over this dataset are conducted using U-Net (semantic segmentation) and Mask R-CNN (instance segmentation) to compare their results. The training dataset is generated from seven labelled images through data augmentation. Training on 1000 and validating on 200 images, Mask R-CNN achieved more epochs quickly. Around 1000 epochs for Mask R-CNN and 500 epochs for U-Net, both models reached a similar result in terms of F1 score and can segment the object in the new images. © 2022 IEEE.

关键词： Semantic Segmentation

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