Description

This Master Prompt develops quantitative representations that transform signals into interpretable descriptors for estimation, classification, monitoring and scientific investigation. It addresses time-domain statistics, spectral indicators, envelope features, time–frequency descriptors, entropy measures, phase space reconstruction, nonlinear dynamics and multichannel relationships. Drawing on the supplied phase space reconstruction review, multivariate link dispersion entropy study, spectrophotometric calibration paper, EEG decoding study and signal-based industrial monitoring research, it examines both handcrafted and learned representations. It also covers principal component analysis, partial least squares, manifold and tensor representations, feature selection and dimensionality reduction where justified. Particular emphasis is placed on mathematical definition, parameter sensitivity, signal-length dependence, noise robustness, physical meaning and prevention of information leakage. The framework requires feature quality to be established through independent validation rather than attractive visual separation, complex terminology or high performance on dependent samples.