The team explored three interconnected thesis areas: 1. Industrial Sustainability & Emission Management (Methane Focused) - Investigating how real-time monitoring and AI-driven analytics can help industrial operations identify, quantify, and reduce methane leaks and emissions at the source. 2. Predictive Maintenance Using Spiking Neural Networks - Exploring the use of biologically-inspired spiking neural networks (SNNs) for energy-efficient, edge-deployable predictive maintenance in industrial machinery, where traditional deep learning is too power-hungry. 3. Attitude Adjustment for CubeSats - Investigating lightweight algorithms for orientation and attitude control in small-form-factor satellites (CubeSats), where computational resources are severely constrained.
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