Passionate about creating innovative digital solutions with cutting-edge technology
I am an undergraduate student in Electrical Engineering with a concentration in Information Technology. Currently, I am an active member of the vision program team at EWS Bascorro (Humanoid Soccer Robot), focusing on developing computer vision solutions.
In addition, I am also interested in web development.
Here are some projects I have worked on
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A lightweight, browser-based anomaly reporting app built with Google Apps Script that validates inputs, timestamps entries, and auto-summarizes morning/evening reports into Google Sheets — fast, reliable, and ready for daily operations.
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A modern, responsive personal portfolio website with a futuristic/cyberpunk visual style, smooth animations, and clear sections for Home, About, Skills, Projects, and Contact — optimized for quick navigation and showcasing work.
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A lightweight static web matrix calculator built with HTML, CSS, and vanilla JavaScript, supporting eigenvalues/eigenvectors, several matrix decompositions (LU, Cholesky, Doolittle, Crout), and a clean, responsive UI for quick numerical exploration.
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A responsive Streamlit app for matrix analysis (eigenvalues/vectors, diagonalization, LU, Cholesky, Doolittle, Crout) built with Python, NumPy, and SciPy — ideal for quick interactive exploration and teaching/demo purposes.
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A responsive static portfolio website (Portfolio Website — Beta) built as a hands-on exercise while learning HTML and CSS; it showcases a Jumbotron, About, Projects, and Contact sections with Bootstrap-based layout and lightweight JS animations.
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An automatic hand dryer simulation for Proteus — microcontroller-driven code (assembly, .a51) controls sensor-triggered drying, motor activation, and safety/time logic for hands-free operation.
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A simulated Automatic Parking System implemented in 8051 assembly and validated in Proteus — handles vehicle detection, barrier control, occupancy counting, and user feedback for a basic contactless parking controller prototype.
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A ROS2-based YOLOv5 object detection system that captures images from a camera, runs real-time inference, and displays detected objects with bounding boxes and labels — ideal for robotics applications, research, and demonstrations.
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A ROS2-integrated YOLOv12 object detection node that captures video frames, runs real-time inference with Ultralytics YOLOv12, and publishes annotated results as ROS image messages — optimized for robotics perception, research, and demos.