Enhanced Hilbert-Huang Transform Algorithm with Adaptive IMF Screening

Application Background: Empirical Mode Decomposition (EMD) decomposes signals into monocomponent signals called Intrinsic Mode Functions (IMFs), enabling instantaneous frequency calculation through Hilbert transform. The primary challenge in practical Hilbert-Huang transform applications is the endpoint effect. Our solution introduces an adaptive spurious IMF filtering algorithm using residue-to-original-signal correlation coefficient as threshold. Key Technology: Complex signal decomposition into monocomponent signals requires each IMF to satisfy two conditions: (1) Extremum and zero-crossing counts must be equal or differ by one throughout the data length; (2) The mean of upper and lower envelopes must be zero at any point. The implementation involves adaptive sifting with envelope interpolation and statistical boundary handling.

Edge Feature Extraction: Local Gradient Maximums and Orientation Detection

Edge feature extraction involves calculating local maximums and orientations of image gradients. In practical implementations, differential operators are formalized and computed using fast convolution functions. Common operators include gradient operators, Laplacian operators, and Canny operators. The Canny edge detector represents a modern approach with superior edge detection performance, gaining widespread adoption. It utilizes the first derivative of Gaussian functions to achieve optimal balance between noise suppression and edge detection precision.