Selected Work
A focused collection of engineering and data science projects spanning computer vision, sensor systems, FPGA development, IoT, machine learning, and Python automation.
The objective of this project was to develop a cost-effective, accurate, and easy distribution method by utilizing existing site surveillance cameras to measure water levels in rivers. Throughout the project, I deepened my understanding of computer vision algorithms, honed my skills in OpenCV, and gained practical experience with the application of deep learning models.
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The objective of this project was to design a classic space fighter game using the Terasic Cyclone V FPGA development board. Through this project, I strengthened my skills in VHDL programming, designing combinational and sequential logic and system engineering design.
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This project focused on designing a capacitive sensor to measure water levels in a tank. Through this work, I deepened my knowledge in advanced sensor technology, electronic circuit design, and electrical theory, as well as conducting simulation using MATLAB.
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In this project, I evaluated the Kalman Filter to enhance the accuracy of location tracking for an autonomous weed-spraying vehicle developed by the JCU spin-out company AutoWee. This experience deepened my understanding of various Kalman Filter types, statistical analysis techniques, and rigorous testing methodologies.
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In this project, I developed an online temperature monitoring system using LoPy and The Things Network (TTN). I gained hands-on experience with LoRa technology, IoT sensors, and system integration, while also deepening my understanding of the future potential of the Internet of Things.
Source Code
The objective of this project was to assess the performance of Machine Learning (ML) and Artificial Neural Networks (ANN) in predicting the Australian unemployment rate over the past three decades. It evaluated multiple models built using various ML algorithms. Through this project, I strengthened my understanding of ML and ANN algorithms and gained hands-on experience using TensorFlow.
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This project allowed me to strengthen my understanding of four key data mining techniques: Principal Component Analysis (PCA), K-means clustering, Naïve Bayes classification, and association rule mining. K-means clustering was used to identify critical areas and timeframes of criminal activity, while Naïve Bayes classification demonstrated high accuracy under specific conditions. Association rule mining uncovered hidden patterns and relationships within the dataset, providing deeper insights into the data.
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This study investigates changes in flying fox habitats and populations in Queensland, Australia, between 2007 and 2021, as well as their connection to climate change. Through this project, I gained valuable experience in sourcing and processing large datasets by extracting data from over 400 weather stations across Queensland and integrating it with location-based information to enhance the main dataset.
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The decline in the population growth rate has become a critical issue in many countries worldwide. This study aims to investigate the correlated factors in the decline of the population growth rate.
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In this project, I assisted a startup in developing remote scripts for file and data processing using Python. Collaborating with a professional software engineer, I enhanced my skills in writing clean, well-structured code and gained hands-on experience with the project management platform Asana.
Source Code
I created an Instagram account that posts daily AI-generated images through Facebook content publishing API based on significant historical events from the same date. Through this project, I gained hands-on experience with API integration, Python programming, image generation using AI models, web automation, and automated image processing.
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