Robust Multi-Modal Data Fusion Framework

Design an advanced data fusion system capable of integrating heterogeneous data sources with different sampling rates, formats, and quality levels. Develop a flexible architecture using deep learning techniques like attention mechanisms and transformer models to align and merge multimodal data. Implement comprehensive data quality assessment, automated feature alignment, and uncertainty propagation methods. Create a system that can handle missing data, detect semantic relationships, and generate consistent representations across diverse data types.