4 papers
Machine Learning Integrated in Wavelet Shrinkage (MLShrink)
Dixon Vimalajeewa, Vijini Lakmini, Brani Vidakovic
Data encountered in practice are frequently contaminated by additive noise, and wavelet shrinkage remains a fundamental tool for recovering underlying signals in nonparametric esti…
SCOPE Shrinkage: A Unified Framework for Wavelet Denoising
Dixon Vimalajeewa, Vijini Lakmini, Malith Premarathna +2
We introduce Symmetric CDF Oriented Probability Enhanced (SCOPE) shrinkage, a unified family of sign-preserving shrinkage rules constructed from centered cumulative distribution fu…
A Multiscale Approach for Enhancing Weak Signal Detection
Dixon Vimalajeewa, Ursula U. Muller, Brani Vidakovic
Stochastic resonance (SR), a phenomenon originally introduced in climate modeling, enhances signal detection by leveraging optimal noise levels within non-linear systems. Tradition…
A Noise Resilient Approach for Robust Hurst Exponent Estimation
Malith Premarathna, Fabrizio Ruggeri, Dixon Vimalajeewa
Understanding signal behavior across scales is vital in areas such as natural phenomena analysis and financial modeling. A key property is self-similarity, quantified by the Hurst…