Artificial Intelligence of Things Lab

We focuses on developing advanced deep learning-based fault detection and diagnosis (FDD) systems for complex industrial equipment. We aim to solve key challenges encountered in real-world settings, such as the scarcity of fault data and distribution shifts between training and operational environments. We develop algorithms that can detect both known and unknown fault types, even with very few faulty samples, by integrating open-set recognition (OSR). We research domain generalization techniques to ensure our FDD models remain reliable and accurate even when operating conditions change. We apply deep learning to analyze multivariate time-series data from various sensors to capture subtle signs of failure early. Head orientation tracking determines which way a user's head is facing. For more accurate and energy-efficient performance on edge devices, we utilizes Ultra-Wideband (UWB) technology with an extended Kalman filter for calibration. This approach enables precise orientation tracking using solely UWB measurements. Aim to make everyday mobile devices be accurate, accessible for natural interaction and context sensing. Mobile systems that emits and analyzes sound waves by on-board speaker and microphone. Learning-based inference and real-time prototyping to achieve robust, power-efficient system. Process of identifying, isolating, and analyzing malfunctions or failures within a system. Head orientation can be tracked with commodity device using UWB. Leverages a device’s built-in speaker and microphone to interpret its physical surroundings and user interactions. Transform a given utterance into a different emotional style while preserving the linguistic content. AI system that enables natural human-computer interaction through speech.