Theses, Dissertations & Reports
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Item Smart Fishing Boat Monitoring And Alert System(Department of Electronic and Telecommunication Engineering, 2023-07) Islam, ArifulIn this project, we have implemented a smart monitoring system for fishing boat to increase operational efficiency for fishing industry to meet sufficient global demands for seafood. The project focuses on solving multiple problems faced by fishing boats, including measuring the temperature and space within fish containers, determining the boat's height through altitude measurement, monitoring air pressure, detecting overloads, implementing an alert system in risky situations, detecting over-rolling, and incorporating an automatic lighting system. To achieve these objectives, we will utilize a combination of advanced sensors, microcontrollers, and monitoring devices. The system ensures optimal conditions for fish storage, provides precise information about weather forecasting , prevent exceeding the boat's maximum capacity, identifying excessive boat rolling and ensuring safe and easy operations. By implementing this Smart Monitoring and alert System, fishing boat operators can enhance operational efficiency, optimize resource utilization, and minimize risks. The project aims to contribute to the sustainable growth of the fishing industry by providing advanced monitoring and safety solutions.Item Skin Cancer Detection Using DenseNet-121 Model(Department of Electronic and Telecommunication Engineering, 2023-07) Islam, ArifulThe field of computer vision is currently undergoing significant research into skin cancer classification and detection. Many researchers used various deep convolutional neural networks to enhance the performance of the current systems. Numerous efforts were made in the past to identify skin cancer. To increase performance and accuracy, several researchers use various techniques. In this thesis project, I'm attempting to build a model for identifying skin cancer based on a technology (DenseNet-121). For training and testing purposes in the identification of skin cancer, I used a dataset. 92% accuracy is demonstrated by the suggested modelItem Traffic Surveillance to detect wrong-way vehicles by image processing(Department of Electronic and Telecommunication Engineering, 2023-07) Islam, ArifulWrong-way driving is one of the throughout the process causes of road accidents and traffic blocks all over the worldwide. By detecting wrong-way vehicles, the frequency of accidents may be minimized and traffic congestion can be alleviated. With the rising popularity of real time traffic management systems and thanks to the availability of cheaper cameras, the surveillance video has become a big source of data. In this study, we present an autonomous wrong-way vehicle identification system using on-road security camera footage. Our system operates in three stages: the recognition of vehicles from the video frame by utilizing the You Only Look Once (YOLO) method, track each vehicle in a specific zone of interest using the centroid tracking technique, and identify the wrong-way of driving vehicles. YOLO is incredibly precise in object detection and the centroid tracking method can track any moving item efficiently. Experimenting with several traffic recordings indicates that our proposed system can recognize and identify any wrong-way vehicle in varied light and weather situations. The system is very simple and straightforward to apply.