Prompt Description

APPROVED A publication-ready causal diagram illustrating the utility of kurtosis in distinguishing transducers is presented. The diagram is structured into three horizontal layers: Physical Source (device-level causes), Signal Manifestation, and Statistical Feature & Classification. Each layer contains rectangular boxes with concise text labels, interconnected by arrows to depict causal relationships. **Top Layer (Physical Source):** * Material microstructure variability * Piezoelectric crystal differences * Assembly imperfections / micro-cracks **Middle Layer (Signal Manifestation):** * Non-Gaussian amplitude distribution * Extreme tail events / peak deviations * Nonlinear response patterns **Bottom Layer (Statistical Feature & Classification):** * Kurtosis captures tail/extreme deviations * High kurtosis → device-specific signature * Classifier separates devices based on kurtosis Arrows connect elements from the top layer to the middle layer, indicating how physical variations manifest as signal characteristics. Arrows then connect elements from the middle layer to the bottom layer, showing how kurtosis captures these signal features and enables device classification.