Description

This Master Prompt develops frameworks for combining complementary measurements and analysing signals associated with interconnected spatial or relational structures. It addresses sensor synchronization, temporal and spatial alignment, data-level fusion, feature-level fusion, decision-level fusion and cross-representation learning. Drawing on the supplied full-text studies of visual–acoustic–thermal manufacturing monitoring, ultrasound representation learning and multichannel feature analysis, it examines when combined information improves estimation, classification or interpretation. Graph-related topics extend the coverage identified in the uploaded abstract collection and require verification against appropriate methodological sources before detailed implementation. These topics include graph construction, spectral representations, filtering, reconstruction and sensor placement. Particular attention is given to missing channels, uncertain relationships, topology assumptions, information redundancy and computational scalability. The framework distinguishes independent sensing from multiple views of one measurement and requires evidence that fusion or network structure adds value beyond simpler alternatives.