Lossless Compression of Weak Electrical Signal of Ginseng Molecule Based on Discrete Wavelet Transform and Siesta Program

Abstract

This paper proposed the electron density of time series by using the Siesta software to calculate the weak electrical signals of ginseng molecule, combining with the lifting scheme DWT to remove ginseng molecular spatial redundancy. For the acquisition and identification of weak electrical signals of ginseng molecule in physical environment , based on the analysis of collection and identification’s principles, the noise coefficient is removed to reconstruct the signal and retain the useful signal components through applying the multi-decomposition of DWT transform to divide weak electrical signals of ginseng molecule into wavelet coefficients of different scales. The experimental results show that the multi-resolution analysis of DWT transform is performed for the weak electrical signal of ginseng molecule with different rhythms and different frequency ranges, and the weak electrical signal size of ginseng molecule before and after compression, the percentage of high frequency coefficients set to zero, and the average energy percentage after compression are, respectively, increased to 77.73%, 46.88%, and 99.99%. This algorithm operates fast enough to ease hardware implementation, providing an effective method for lossless compression of the weak electrical signals of ginseng molecule.

A New Algorithm for Feature Extraction of Plant Diseases and Insect Pests Signal Based on Optimal Wavelet Packet and Non-negative Matrix Factorization

“…not further decomposes the W (wavelet) space, and they can be further decomposed with wavelet packet to narrow the frequency spectrum window, which widens with the increase of j in the process of the orthogonal wavelet transform. And the most suitable time-frequency window or optimal basis can be obtained to analyze the signals [9].…”