Adaptive Morphological Filtering Algorithm for Effective Nonlinear Signal Filtering

This implementation demonstrates how adaptive morphological filtering algorithms effectively process nonlinear signals. The algorithm dynamically determines structural element dimensions by analyzing signal extreme points, allowing it to better adapt to signal characteristics and enhance filtering performance. Key implementation aspects include: - Automatic detection of local maxima and minima to calculate optimal structural element parameters - Adaptive adjustment of element height based on signal amplitude variations - Dynamic length determination through peak interval analysis The algorithm's application enables effective noise removal, signal smoothing, and accurate extraction of targeted signal features. In practice, this involves creating flexible structuring elements that morph according to signal properties, typically implemented through functions like imdilate and imerode with dynamically generated structural elements. This makes adaptive morphological filtering particularly valuable in signal processing applications dealing with complex, non-stationary signals where fixed-parameter filters underperform.