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    UtilizingMachineLearningTechniquesfortheAssessmentof ConcurrentKernelExecutionPerformanceonGPUs
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-09-30) Sunny, Md. Sadid Ehsan; Khara, Yar Muhammad; Nasirullah; Saif, Sam An
    The increasing demand for high-performance computing has positioned Graphics Processing Units (GPUs) as critical components in various computational fields, including scientific simulations, machine learning, and graphics rendering. This thesis explores the utilization of machine learning techniques to assess and optimize the performance of concurrent kernel execution on GPUs. Concurrent kernel execution allows multiple kernels to run simultaneously on a GPU, improving resource utilization and overall throughput. However, this approach introduces challenges related to resource contention and performance variability. By leveraging machine learning models, we can predict performance metrics, identify bottlenecks, and optimize scheduling strategies. This research presents a comprehensive framework that integrates predictive modeling, classification algorithms, and deep learning techniques to enhance the assessment of concurrent kernel execution performance. Through extensive experimentation and analysis, we demonstrate the effectiveness of our approach in improving GPU performance.
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    Future Hybrid Energy System of Photovoltaic and Fuel Cell For Kanifing in The Gambia
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-13) Sanyang, Ousman; Ali, Akram; Ceesay, Lamin J; Bayo, Sarjo
    The present research shows the outcomes of an optimal grid-connected with photovoltaic and fuel cell system design for Kanifing in the Gambia west Africa. The most efficient hybrid renewable power system is chosen by testing its performance and utilizing integrated modeling, simulation, optimization, and control methodologies. The key objective is to design a grid-connected with photovoltaic and fuel cell energy system with high utilization of clean energy, low greenhouse gas emissions, and a low cost of energy to meet the Kanifing's electric load. The performance and cost of the hybrid power system configurations using load executing and phase charging control techniques were assessed using hourly simulations, modeling, and optimization. Getting electricity is a significant difficulty in Africa. Although there is a lot of potential for using solar energy, there is little investment in renewable energy projects. Thus, a lot of people continue to rely on personal diesel generators, which emit large amounts of pollutants and harm both the environment and people. Situated in the Sunbelt, Gambia is one of the countries in Africa endowed with an extremely high solar irradiation potential. HOMER simulation software was used to determine the optimal configurations and sizes. A comparison is made between several hybrid combinations and a regular system. The studies showed that the suggested system had nearly lowered costs and CO2 emissions by 39% and 79%, respectively. The annual carbon footprint with avoided CO2 emissions is approximately 151,751 kg. The outcomes demonstrated that implementing a hybrid power system might be a reliable and profitable way to achieve social and environmental advantages in isolated rural and urban electrification
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    AI Trained IoT Based Automated Solar Panel Cleaning System
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-25) Arika, All-Mumtahina; Fahim, Iffat Nowshin; Uddin, Jamal; Hossain, Salman
    The collection of dust significantly decreases the efficiency of photovoltaic (PV) modules. In order to reduce the impact of dust on photovoltaic (PV) systems in a cost-efficient way, it is important to use optimal cleaning methods. The determination of the interval is required. In order to achieve this goal, machine learning (ML) models can be employed to identify the level of dust on photovoltaic (PV) systems that exceeds a predetermined threshold. This study aims to examine the effects of dust on photovoltaic (PV) systems in Bangladesh and suggests a new machine learning (ML) classification approach for detecting dust. Additionally, a cleaning system will be developed. Multiple machine learning classifiers were deployed and their performance was assessed. The Artificial Neural Network (ANN) emerged as the top-performing model, with an accuracy of 98.11%. When the machine learning model detects dust, the user can activate the water sprinkler cleaning system remotely. This technology successfully eliminates dust by spraying pressured water over the panel. The proposed cleaning mechanism successfully improved the efficiency of dusty PV modules to match that of clean modules (14.87%). A quantitative analysis was conducted to measure the reduction in productivity as a monetary loss in order to evaluate the feasibility of the cleaning system. The findings indicate that the suggested cleaning technique is financially feasible for photovoltaic systems with capacities above 2.89 kWp.
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    Integration of Smart IoT Energy Monitoring System for Solar Powered Microgrid: Case Study Sierra Leone
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-28) Kamara, Alie Alusine; Suma, Sheku Saidu; Sillah, Abubakarr
    A smart IoT energy monitoring system for a solar-powered microgrid in Sierra Leone is implemented and presented in this study. Microgrids that are powered by solar energy have emerged as a promising approach to address the issues of consistent and sustainable energy availability in developing countries, especially in rural places[1]. For these microgrids to operate as best they can, however, effective resource management and monitoring are necessary. Here, we use Internet of Things (IoT) technology and Homer Pro software to create and install an advanced energy monitoring system. Homer Pro software is used for system design, optimization, and simulation, and it is integrated with Internet of Things (IoT) devices for data collecting, transfer, and analysis[2]. Real-time data on energy production, use, and storage is provided by the energy monitoring system. It also has an easy-to-use interface for controlling and monitoring in real-time. Users can monitor energy usage, spot inefficiencies, and optimize energy consumption thanks to the system's remote monitoring and control capabilities[3]. In this study, we examine viable substitutes for traditional power generating systems with the goal of delivering electricity in a cost-effective, dependable, and sustainable way. These substitutes include the utilization of solar renewable energy sources and less carbon-intensive technology. In addition, the study addresses the scalability, future prospects, and socioeconomic effects of smart IoT energy monitoring systems in relation to sustainable development[4]. In summary, this study advances the use of IoT applications in energy systems and provides workable solutions to improve resilience, efficiency, and access to energy in Sierra Leone.
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    Plasmonic Coupling and Thermal Effects on Photothermal Response of Randomly Distributed Nanoparticles
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-27) Nayeem, Hasnat Mohammad; Fahim, Abrar; Adib, Md. Ahasanul
    Plasmonic coupling has attracted considerable attention in research due to its promising applications in thermoplasmonics, which is increasingly utilized across various nanotechnologies, particularly in the fields of biology and medicine. Applications include photothermal cancer therapy, drug delivery, nanosurgery, and photothermal imaging, thanks to their capability to significantly enhance electromagnetic fields. Localized Surface Plasmon Resonances (LSPR) in metal nanostructures enable substantial electromagnetic field enhancement and precise localization at the nanoscale. The characteristics of LSPR, including the resonance wavelengths, can be adjusted by altering the geometry of the nanostructures and are highly sensitive to changes in the surrounding refractive index. The interaction of localized plasmon resonances can lead to the formation of new hybrid modes that individual nanostructures cannot support, surmounting some limitations of standalone LSPR and facilitating novel applications and the active manipulation of plasmon resonances. In this thesis work, we explore how plasmonic coupling influences the photothermal behavior of randomly distributed silver nanoparticles. We used the discrete dipole approximation method and thermal Green’s function to compute the spatial temperature profiles of illuminated nanoparticles. Our findings suggest that plasmonic coupling among nanoparticles in a random assembly, along with thermal accumulation, induces a photothermal response that differs from that observed in isolated nanoparticles. We qualitatively analyzed the individual effects of plasmonic coupling and thermal accumulation on temperature increases in nanoparticle assemblies. Our results indicate that at wavelengths far from a single nanoparticle’s plasmonic resonance, plasmonic coupling among clustered nanoparticles can lead to significant temperature increases, an effect not anticipated in the absence of plasmonic coupling. Conversely, at the resonance wavelength of a single nanoparticle, plasmonic coupling results in a lesser temperature rise compared to a group of non-coupled nanoparticles. These insights enhance our understanding of the photothermal dynamics in random nanoparticle systems, with significant implications for their use in biological applications
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    Hysteresis Characteristics Analysis for Ferroelectric TFTS and It’s Application to Dram
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-26) Mahmud, Shah Md. Ashik; Islam, Shadid; Wornob, Shafaiet Newaz; Basher, Md.Khairul
    This thesis presents a brief analysis of the hysteresis characteristics of various ferroelectric materials. Ferroelectric materials shows spontaneous remanent polarization that can be modified by an external field. This property is crucial for applications in non-volatile memories, actuators, and sensors. By examining hysteresis loops, the study aims to understand the distinct behavior of ferroelectric materials under different electric fields and their impact on practical applications. Experimental and theoretical approaches are used to elucidate the mechanisms driving hysteresis in ferroelectrics, providing insights into optimizing material performance for specific applications.
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    Design of a Prototype of Smart Agriculture Powered by SPV
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-23) Siddo, Issaka Soumaila; Abubakar, Al-mustapha Babangida; Fari, Ahmad Mahmud
    This project presents an IOT based smart irrigation system in order to optimize watering schedules for plants and lawns. The system uses an Arduino Uno microcontroller in conjunction with IoT(Internet of Things) sensors to measure soil moisture, temperature, and humidity, as well as a weather station to collect data on ambient conditions. The microcontroller processes this information and controls irrigation valves or motors to deliver water only when necessary. The system also includes a user interface for manual control and scheduling, and it can be connected to a network for remote monitoring and management. To further improve the sustainability and efficiency of the system, the project integrates a water fetching system that draws water from a well. This allows the system to supplement the water supply for irrigation and reduces reliance on external sources of water. In addition, the project incorporates a solar tracking system that maximizes energy generation from the solar panels to recharge the battery that powers the microcontroller. The results of the study demonstrate that the smart irrigation system significantly reduces water usage and enhances plant health when compared to traditional irrigation methods. This project presents a promising solution to the challenges of water scarcity and inefficient irrigation practices and has the potential to contribute to the development of more sustainable and environmen- tally friendly systems.
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    Traffic Vehicle Detection of Dhaka City using Deep Learning Algorithm
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-07-07) Sadat, Md Atiq Aziz; Islam, Md Touhidul; Noman, Abu
    The fast urbanization of Dhaka City has resulted in substantial traffic congestion, requiring effective management techniques. This study investigates the use of deep learning methods, namely convolutional neural networks (CNN), to detect automobiles in traffic in Dhaka. The CNN is trained using photos collected from several places throughout the city. The model is specifically designed to provide optimal performance in detecting objects in real-time, effectively overcoming the difficulties presented by the city's high population density and varied traffic conditions. Preprocessing techniques like picture augmentation and normalization improve the model's ability to handle different scenarios, and its performance is assessed using precision, recall, and F1-score measures. The results demonstrate that the deep learning model outperforms conventional methods in terms of both accuracy and speed, implying significant enhancements for traffic monitoring and management. This study highlights the capacity of deep learning in addressing urban traffic issues, hence facilitating the development of sophisticated intelligent transportation systems in Dhaka.
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    Thesis: Smart Agricultural Robot Based on Computer Vision
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-07-08) Houssam, Mohamed Islam; Omar, Njutapmvoui Mbah Mohamed; Coulibaly, Moussa
    The agricultural sector is using smart agricultural robots, particularly harvesting robots, to increase production, reduce labor costs, and maximize resource use. These robots automate fruit selection, alleviating manpower shortages and lowering manual harvesting costs. A stable power supply system, an ESP32 microcontroller for control, servo motors for precise movement, ultrasonic sensors for obstacle detection, and an ESP32-CAM for picture capture are all essential components. Software technologies such as Fusion 360 help to design the robot's construction, while MATLAB Simulink and Simscape enhance the robot arm's dynamics. Using the YOLO v3 model, the robot detects fruit accurately in real time. These technologies not only improve efficiency and lower costs, but they also encourage sustainable agricultural practices. Future developments aim to enhance autonomy, integrate advanced sensors for better environmental monitoring, and adapt robots to various agricultural settings, ensuring continued innovation in food production technologies.
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    Designing A Renewable Power Integration System for Jigjiga yar village In Somalia
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-07-05) Muhumed, Mustafe Mukhtar; Djae, Chabane Ali; Moussa, Safa Omar
    This project addresses the critical energy challenges faced by the Somali region by developing a hybrid renewable energy system. The existing energy infrastructure heavily depends on diesel generators, leading to prohibitively high costs, significant environmental pollution, and limited access to reliable electricity. Our proposed solution integrates solar photovoltaics (PV), wind turbines, and battery storage with the existing diesel generator infrastructure to create a more sustainable and economically viable energy system. We collected and analyzed data on electrical consumption from 268 homes, solar irradiation, ambient temperature, and wind speed. The hybrid system was designed and simulated using HOMER software, which demonstrated a substantial reduction in the tariff rate by 22%, a decrease in pollutants by 29.34%, and the generation of 28.4% excess energy for future use or potential sale into the grid. The economic analysis revealed a significant reduction in the Levelized Cost of Energy (LCOE) to $0.480 per kWh, compared to $0.7 per kWh for the current system and $1.35 per kWh for the base system. This project showcases a viable approach to achieving sustainable energy in the Village, with considerable economic and environmental benefits that can be replicated in other regions facing similar energy challenges.