Projects

Contributed to the INDICATE-FH project focusing on novel diagnostic methods for food intolerances. My role involved developing computer-vision algorithms using OpenCV and TensorFlow for medical image analysis, implementing deep-learning models for automated detection of diagnostic markers, and collaborating with medical researchers on data collection, preprocessing, and validation.

The project brings together researchers from multiple disciplines to create innovative diagnostic and treatment approaches for food intolerance conditions.

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Developed autoencoder-based models for signal-to-noise ratio (SNR) analysis and denoising. The work involved benchmarking different bottleneck designs, loss functions, and noise models, then optimizing model architectures for measurable improvements in denoising accuracy.

This research contributes to broader applications of machine learning in signal processing and data analysis.

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Developed indoor navigation systems for autonomous mobile robots using Bluetooth beacon and WiFi localization technologies. The system combined multi-sensor fusion for accurate positioning, implemented path planning algorithms with dynamic obstacle avoidance, and enabled real-time autonomous navigation in indoor environments.

This project contributed to autonomous robot navigation research and practical deployment of localization technologies in constrained indoor spaces.

Developed an autonomous lawn mower system using advanced image processing and waypoint-based outdoor navigation. The system implements computer-vision algorithms to detect and classify grass height (long vs. short grass), enabling dynamic blade adjustment for optimal cutting performance. Navigation operates without perimeter wire constraints, using GPS waypoint-based planning for autonomous outdoor operation.

This project demonstrates integration of perception, control, and autonomous navigation in a practical robotic application.