Description

This Master Prompt develops a systematic framework for improving signal quality while preserving information relevant to the research objective. It addresses raw-data auditing, baseline correction, detrending, normalization, resampling, segmentation, digital filtering, adaptive filtering, artifact identification, outlier handling, missing observations and signal reconstruction. Drawing on the supplied studies of physiological signal cleansing, constrained adaptive filtering, weak periodic feature extraction, tool-wear monitoring, spectral analysis and neural model selection, it accommodates conventional, statistical, decomposition-based and learning-based approaches. Particular emphasis is placed on distinguishing unwanted interference from informative transients, controlling amplitude and phase distortion, preventing information leakage and evaluating performance beyond visual smoothness. The framework connects preprocessing decisions with noise assumptions, reference-signal availability, downstream requirements and computational constraints while preserving a traceable record of every transformation applied to the original observations.