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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2024年第17期84卷 1-23页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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The Balance Model for V2X System with Variable Infrastructure

The Balance Model for V2X System with Variable Infrastructur...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Plotnikov, Pavel V. Tambovtsev, Gleb I. Vladyko, Andrei G. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Information Technology and Software Engineering Saint Petersburg Russia The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Radioelectronic Systems and Robotics Saint Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the optimization problem and seeks a balance equation between key performance-influencing parameters in a Vehicle-to-Everything (V2X) network. A novel mathematical optimization model is proposed, focusing on the interaction between On-Board Units (OBUs) and both stationary and mobile Roadside Units (RSUs/mRSUs). The optimization problem is expressed through a multi-objective function considering critical parameters such as data transmission latency, energy consumption, and resource availability. The study demonstrates how RSU density and mobility impact key network performance indicators, including system stability and data loss percentage during peak hours. This work provides a structured analytical approach to optimizing Intelligent Transportation System (ITS) infrastructure, ensuring more reliable and efficient communication between vehicles. © 2025 IEEE.

关键词： Motor transportation

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Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking 17th

Enhanced Kalman with Adaptive Appearance Motion SORT for G...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Anh, Duy Le Dinh Tran, Kim Hoang Nguyen, Quang-Thuc Le, Ngan Hoang University of Arkansas FayettevilleAR United States Faculty of Information Technology and Software Engineering Lab University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819609161

Despite recent progress, Multi-Object Tracking (MOT) continues to face significant challenges, particularly its dependence on prior knowledge and predefined categories, complicating the tracking of unfamiliar objects. Generic Multiple Object Tracking (GMOT) emerges as a promising solution, requiring less prior information. Nevertheless, existing GMOT methods, primarily designed as OneShot-GMOT, rely heavily on initial bounding boxes and often struggle with variations in viewpoint, lighting, occlusion, and scale. To overcome the limitations inherent in both MOT and GMOT when it comes to tracking objects with specific generic attributes, we introduce Grounded-GMOT, an innovative tracking paradigm that enables users to track multiple generic objects in videos through natural language descriptors. Our contributions begin with the introduction of the G2MOT dataset, which includes a collection of videos featuring a wide variety of generic objects, each accompanied by detailed textual descriptions of their attributes. Following this, we propose a novel tracking method, KAM-SORT, which not only effectively integrates visual appearance with motion cues but also enhances the Kalman filter. KAM-SORT proves particularly advantageous when dealing with objects of high visual similarity from the same generic category in GMOT scenarios. Through comprehensive experiments, we demonstrate that Grounded-GMOT outperforms existing OneShot-GMOT approaches. Additionally, our extensive comparisons between various trackers highlight KAM-SORT’s efficacy in GMOT, further establishing its significance in the field. Project page: https://***/G2MOT. The source code and dataset will be made publicly available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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Deep/Federated Learning Algorithms for Ultrasound Breast Cancer Image Enhancement

Deep/Federated Learning Algorithms for Ultrasound Breast Can...

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2023 International Conference on Microelectronics, ICM 2023

作者： Waly, Sarah M. Taha, Radwa Elghany, Mohamed A. Abd Salem, Mohammed A.-M German University in Cairo Faculty of Media Engineering & Technology Egypt German University in Cairo Faculty of Information Engineering & Technology Egypt Tu Darmstadt Integrated Electronics Systems Lab Germany

ISBN: (纸本)9798350380828

Breast cancer stands as a leading cause of global cancer-related deaths among women, emphasizing the urgency for advancements in early detection and precise diagnosis to enhance patient outcomes. This study addresses the pressing need for improved breast cancer imaging through the integration of deep learning *** paper specifically investigates the application of U-Net architecture and Federated Averaging (FedAvg) techniques for enhancing ultrasound breast cancer images thereby assisting radiologists in accurate cancer diagnosis. Leveraging two authentic datasets, we conduct experiments varying the number of epochs (50, 100, and 200) for FedAvg with U-Net. Our findings demonstrate promising accuracy values: 89.4716, 89.48, and 89.48, showcasing the effectiveness of our ***, we explore the impact of U-Net alone and in conjunction with FedAvg on the segmentation process of ultrasound breast cancer images. This dual-pronged investigation offers insights into the synergistic potential of these methodologies for image *** conclusion, our study contributes to the ongoing efforts for improved breast cancer imaging. Through the fusion of U-Net and FedAvg, our approach demonstrates significant promise, providing a valuable avenue for advancing medical image analysis in the context of breast cancer diagnosis and treatment planning. © 2023 IEEE.

关键词： Image enhancement

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Identifying and Involving the Real End User in software Development: Towards a Pattern Language 27

Identifying and Involving the Real End User in Software Deve...

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Daud Haiderzai, Mohammad Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

software development demand and identification of end users involvement have been increasing rapidly, but identifying the real end user and involving them in software development is challenging for software developers reported in the literature. One of the problems is the difficulty of designing the business domain. It takes longer to find the actual business domain when different and inexpert domain end users are engaged with developers. Another problem is the difficulty of incorporating all types of end users needs in software development. Several expectations and different information make the development process more complex and overload the project scope. software development also takes more time, and the project goal is challenging. The above problems are the leading causes that create a complex process and difficulty in software development. To overcome these problems, we established a pattern language composed of eight organizational patterns observed and documented during in practical work on many projects. These patterns and other known organizational patterns relation are described in a patterns story of a real company. © 2022 ACM.

关键词： software design

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Organizational Patterns for Improving the Understanding between a Customer and the Development Team 27

Organizational Patterns for Improving the Understanding betw...

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Khattab, Mohammad Ismail Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

During a software development process, a customer and the development team need to communicate and understand each other. Poor communication between a customer and the development team is one of the most common challenges in the software development process, which leads to software failures and further increases the budget. In our practical experience and from the literature, we found that lack of customer involvement, no common communication language, complex business processes, end-user resistance toward system development, lack of trust, lack of leadership, and and end-user capacity are the main challenges toward the development team understanding. We bring six newly discovered patterns that address these problems and improve the understanding between a customer and the development team during the software development process. We also bring some of the known organizational patterns that also mitigate these problems into relation to the newly discovered patterns. To create a pattern language from these patterns, we illustrated the sequences of the patterns so that the development team and customer could understand each other. © 2022 ACM.

关键词： software design

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Toward Organizational Pattern Ontology 27

Toward Organizational Pattern Ontology

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Waseeb, Shakirullah Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

Organizational patterns of agile software development are proven practices for dealing organizational principles. Finding and selecting the right pattern is difficult. One way to select a pattern is to follow the sequence and compositions given in a pattern language. However, in general, pattern languages do not always reveal every reliable connection. Patterns are described in informal and unstructured text, making it difficult to understand their connections. This work attempts to provide a conceptual ontology model for organizational patterns-describing the collection of concepts (terms) and their relations. This contribution can be an attempt to express, expose, and share semantic knowledge between patterns using ontology. The resulting ontology can be used on top of the organizational patterns repositories to help retrieve patterns based on their logical connections. © 2022 ACM.

关键词： Semantics

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Comparison of Visual Occupancy Detection Approaches for Parking Lots and Dedicated Containerized REST-API Server Application 9

Comparison of Visual Occupancy Detection Approaches for Park...

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9th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2022

作者： Petričko, Andrej Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

With the development of new technology, the urban infrastructure, which is necessary to meet the social, economic, and physical needs of the population, is also gradually improving. Therefore, cities face significant urban planning challenges. With the increasing population and number of cars on the road parking space occupancy detection techniques are of great importance for efficient parking space management. Knowing the availability of parking spaces within a city in real-time and informing residents can be of great help in reducing the number of car park councils, reducing traffic congestion in cities, or improving the scalability of car parks and the time needed to find empty parking space. In many car parks, ground sensors are used to determine occupancy status. However, this requires the installation and maintenance of these sensors in every parking space. This can be expensive, especially in car parks with a large number of available spaces. With increasing computing power, new approaches to parking space occupancy detection using computer vision are emerging. However, these approaches are tailored to the site of application. In this work, we propose a comparison of two general approaches: classification and object detection. The reliability, precision, and scalability of these approaches are compared using a REST-API server application implemented in Python. In order to get as close as possible to general use, we use docker, which enables us easier develop and deploy this application independently of the hardware. © 2022 Copyright for this paper by its authors.

关键词： Object detection

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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