Peak extraction and noise reduction of IMS signal based on wavelet transform algorithm

Signal processing is an integral technique in the analysis of ion mobility spectrometer’s (IMS) data. IMS data are especially appropriate for wavelet transform because of the uniform Gaussian peak shapes of spectra. These peaks need to be distinguished from higher frequency signals such as noise signals. Today, wavelet transform is one of the most common compression and denoising methods applied to IMS data [1]. This transform is a mathematical transformation for graded decomposing signals [2]. Wavelets admit complex data to be decomposed into elementary forms at different positions and scales and finally reconstructed with high accuracy. Signal transmission is based on transmission of a series of numbers. The series representation of a function is important in all types of signal transmission. The wavelet representation of a function is a new technique that is the improved version of Fourier transform. In this paper, we present an improved algorithm that combines the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) to find out the advantages of wavelet transform compared to Fourier transform. The raw data used in this study has been acquired from a home-made ion mobility spectrometer which designed and constructed in Institute of Material and Energy. The results revealed that the proposed method has a good performance to find peaks and reduce the noise.