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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Mosela, David Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. Computer Systems Engineering Tshwane South Africa Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-dept. exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing. © 2024 IEEE.

关键词： Highway administration

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THE DIGITAL engineering FACTORY: CONSIDERATIONS, CURRENT STATUS, AND LESSONS LEARNED

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INCOSE International Symposium 2024年第1期34卷 927-943页

作者： Gregory, Joe Salado, Alejandro O'Neal, Sharon Larez, Richardo Reda, C.J. Martell, Niko Martin, Evan Colson, Matthew Masterson, John Armenta, David Dept. of Systems and Industrial Engineering University of Arizona United States Electrical and Computer Engineering University of Arizona United States

In industry, the advancement of digital engineering and the digital thread aims to reduce the impact of knowledge ‘siloes’ by providing a way to integrate data across the entire system lifecycle and across multiple domains. In a typical engineering curriculum, however, courses are still treated as ‘siloes’, and students often do not have the opportunity to experience this industrially relevant approach to engineering. The Digital engineering Factory (DEF) is a digital engineering environment under development at the University of Arizona to support engineering students. The DEF supports students by providing access to multiple engineering tools and is structured using a ‘hub-and-spoke’ approach to consolidate data from these tools. Through this connected architecture, students can transfer data generated in a particular course to tools for use in other courses. Connecting course activities in this way enables students to experience a complete end-to-end system lifecycle. At its ‘hub’, the DEF uses Violet to integrate data from multiple sources, create a digital thread, and generate a graph representation of the dataset. This knowledge graph, written in the Ontological Modeling Language (OML), can be viewed in OML Rosetta and is structured according to the University of Arizona Ontology Stack (UAOS). The use of the UAOS and OML Rosetta allows instructors to leverage semantic web technologies to support teaching activities such as grading. In this paper, the authors review the objectives of the DEF, discuss the status of the project, and highlight current limitations and lessons learned with regards to its deployment. These may be useful to inform similar developments in industrial settings. Copyright © 2024 by Author Name. Permission granted to INCOSE to publish and use.

关键词： Life cycle

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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces 19

Dynamic Network Optimization with Deep Learning and Deployme...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Jang, Nayeon Bae, Jeonghoon Tianyu, Tao Byun, Gyurin Bui, Phuoc-Nguyen Lee, Tae-Jin Choo, Hyunseung Dept. of AI Systems Engineering Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks. © 2025 IEEE.

关键词： 5G mobile communication systems

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Training Swinging Door Trending Compression Algorithm for IoT devices 10

Training Swinging Door Trending Compression Algorithm for Io...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Correa, Juan David Arias Pinto, Alex Sandro Roschildt Montez, Carlos Post-graduate Program of Automation Systems Engineering Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Florianópolis Brazil Dept. of Automation and Systems Engineering Florianópolis Brazil

ISBN: (纸本)9798350373011

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device's resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms. © 2024 IEEE.

关键词： Data compression ratio

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Design of a Compact 2-Bit PIN Diode Reflection Type Phase Shifter using Radial Stub

Design of a Compact 2-Bit PIN Diode Reflection Type Phase Sh...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Bilawal, Farah Babaeian, Fatemeh Karmakar, Nemai Chandra Dept. of Electrical and Computer Systems Engineering Monash University Melbourne Australia

ISBN: (纸本)9798350379693

The goal of this research is to create an optimized hybrid coupler structure for a PIN diode reflection type phase shifter (RTPS) operating at microwave frequencies. To date, no phase shifter has been reported with the proposed technique, compactness, minimum phase error and balanced amplitude. Another limitation of designing such structures at such high frequency range, is that the 50Ω impedance lines' width approaches its length for given substrate thickness, that is undesirable. This study introduces a new method of developing a module at 100 Ω and translating it back to 50 Ω at the input and output terminals. Moreover, the proposed design is highly resilient to manufacturing constraints owing to the addition of radial stubs instead of lumped components, as reflective loads. Its performance with respect to its bandwidth coverage is better with 15% bandwidth enhancement, insertion loss around 3dB, and reflection co-efficient at all ports is better than -15dB over the whole operating bandwidth. The simulated and measured results validate the proposed design effectively. © 2024 IEEE.

关键词： Bandwidth

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Polarimetric Image Fusion Strategies for Improving Target Shape and Contrast in Remote Sensing Applications 21

Polarimetric Image Fusion Strategies for Improving Target Sh...

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Electro-Optical and Infrared systems: Technology and Applications XXI 2024

作者： Ratliff, Bradley M. Tyo, J. Scott Taylor, Michael Prikkel, Connor Dept. Electrical & Computer Engineering Dayton University DaytonOH United States Electrical & Computer Systems Engineering Dept. Monash University Melbourne Australia

ISBN: (纸本)9781510681088

Images from passive imaging polarimeters are often displayed in terms of their intensity (s0), degree of linear polarization (DoLP), and angle of polarization (AOP). The AOP and DoLP together generally provide information about shape and surface quality of imaged objects;however, the information conveyed in these images are often difficult to interpret, especially to an analyst unfamiliar with polarization phenomenology. Furthermore, it can be challenging to train machine learning approaches with direct polarimetric information due to the limited availability of application-specific data. Augmenting s0 images with polarimetric information in the form of contrast and shape enhancement thus allows for the data to remain in the domain of traditional EO-IR intensity imagery that is more familiar for both humans and trained networks. In this work we explore several strategies for effectively fusing polarimetric information into s0 imagery to improve target shape and contrast for both human and algorithmic consumption, with a particular focus on remote sensing and target detection applications. © 2024 SPIE.

关键词： Image fusion

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Artificial Intelligence (AI) for Improving Performance at the Cutting Edge of Medical Imaging 5

Artificial Intelligence (AI) for Improving Performance at th...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Nour, Samar M. Faculty of Engineering Dept. Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350381030

Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medical imaging has ushered in a new era in healthcare diagnosis and therapy. Artificial intelligence (AI) has shown impressive potential in enhancing accuracy, efficiency, and diagnostic performance across a range of medical imaging modalities by using the power of deep learning (DL), machine learning (ML), and computer vision. In this paper, we are trying to investigate the connection between artificial intelligence (AI) and medical imaging, concentrating on how AI-driven strategies are improving performance at the cutting edge of medical imaging technologies through the proposed architecture model. Furthermore, the paper also explores the limitations and opportunities that result from incorporating artificial intelligence (AI) into the use of medical imaging. The potential for artificial intelligence (AI) to transform image-guided therapies and its implications for personalized medicine are investigated. © 2023 IEEE.

关键词： Medical imaging

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning 2

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Skandarsini, R.R. Sangavi, M.S. Harika, Ravitla Varun, C.M. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798350365269

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact. © 2024 IEEE.

关键词： Predictive analytics

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Das, Rajdeep Sharma, Vaishnavi Kumari, Priyanka Raj, Abhishek Das, Sudipta De, Indrajit Institute of Engineering and Management Dept. of Computer Science and Business Systems Kolkata India

ISBN: (纸本)9798350390759

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space. © 2024 IEEE.

关键词： Support vector machines

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