
Variational Mode Decompostion Part 2: The Maths
This blog focuses on the mathematics of VMD algorithm. It talks about how VMD uses Constrained Optimization and Hilbert Transform to convert the signal into multiple modes.
A mix of signal processing and deep learning
Pursuing Master's in Artificial Intelligence and Data Science. My research area is to combine signal processing ideas with machine learning and deep learning methods. My Publication domains are data processing, data modelling, Bioinformatics, image processing and time-series analysis. I love exploring different algorithms with different data and create interesting applications out of it. I share my experiences via writing blogs and YouTube Videos.
Compared dna2vec, lshvec, and rna2vec based on vaccine degradation prediction using GRU and CNN-GRU. Results indicate that dna2vec performed the best
Performed Spectral analysis on Sacred Groves multispectral images to analyze the vegetation content from Satellite images.
Compared Variational Mode Decomposition, Dynamic Mode Decomposition, Wavelet Decomposition algorithms based on pitch estimation of piano notes.
Comparing DMD with prony and FFT for estimation of parameters in Electric grids. The work concludes that DMD is a fast and accurate method for estimating the parameters.
This blog focuses on the mathematics of VMD algorithm. It talks about how VMD uses Constrained Optimization and Hilbert Transform to convert the signal into multiple modes.
Variational Mode Decomposition is very less popular algorithm with a huge potential. It is combined with machine learning and Deep learning methods and shown very promising results. This blogs gives a small introduction to VMD algorithm.
A simple blog which gives a basic idea to implement Neural Networks from strach. It is necessary to get an intution of how Forward propogation and Backward propogation works.