Noise Analysis Using Harmonic Wavelet Transform Combined with Approximate Entropy

This presents my university graduation project on "Noise Analysis Using Harmonic Wavelet Transform Combined with Approximate Entropy," which I hope will be valuable to the signal processing community. In this research, I developed an analytical approach that integrates harmonic wavelet transform with approximate entropy to characterize noise properties. The study investigates noise characteristics, generation mechanisms, and their impact on signal processing systems. The implementation involves decomposing signals using harmonic wavelet transform to extract frequency-specific components, followed by approximate entropy calculation to quantify the complexity and predictability of noise patterns. Key algorithms include wavelet coefficient computation through fast Fourier transform (FFT) implementations and entropy estimation using state vector reconstruction methods. This research aims to provide a novel methodology for noise analysis with potential applications in signal denoising, feature extraction, and system diagnostics. I hope this work inspires further investigations and serves as a reference for related studies in the field, particularly for implementing noise analysis algorithms in MATLAB or Python environments where wavelet transforms and entropy calculations can be efficiently programmed using libraries like PyWavelets or signal processing toolboxes.