Abstract
The aim of this paper is to investigate the performance of the Low Complexity Welch Power Spectral Density Computation (PSDC). This algorithm is an improvement from Welch PSDC method to reduce the computational complexity of the method. The effect of the sampling rate and the input frequency toward to accuracy of frequency detection is being evaluated. From the experiment results, sampling rate nearest to the twice of the input frequency provides the highest accuracy which achieved 99%. The ability of the algorithm to perform complex signal also has been investigated.
Highlights
Frequency based sensor have grown rapidly in popularity
The aim of this paper is to investigate the performance of the Low Complexity Welch Power Spectral Density Computation (PSDC)
This algorithm is an improvement from Welch PSDC method to reduce the computational complexity of the method
Summary
Frequency based sensor have grown rapidly in popularity. The requirement of frequency analysis highly increases. The common process is the spectral density calculation. There are many methods of spectral density calculation been proposed, namely as Robust Spectral Density Estimation (RSDE), Independent Component Analysis (ICA), Multitaper Power Spectral Density Estimation (MPSDE), Low-complexity Welch Power Spectral Density (LCWPSD), B-Spline Windows Power Spectral Density Estimation and etc. LCWPSD is the most suitable for biosensor processing. It has been proposed to apply in biosensor application. Optimum parameter are required to enhance the performance of LCWPSD. Sections are going to discuss the details for LCWPSD
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
