Research On Acquisition And Processing Of Nano-Quantum Fluorescence Spectra Based On Embedded System

In daily life, substances are emitted or reflected spectrum. Spectral analysis is based on a substance ingredient absorption and reflection characteristics of electromagnetic waves to have qualitative and quantitative analysis. In order to improve the accuracy of spectral analysis, effective method must be taken to get the original signal corresponding pretreatment.Based on a modular spectrometer market applications and integration requirements, this paper using existing spectrometer and embedded technology designed a spectrum acquisition system. It enables the system to complete the signal acquisition, storage and other functions independent of human-computer interaction,get rid of the bondage of the personal computer.The signal is collected by using the spectrometer, which is inevitably affected in different noise sources, and analytical accuracy of spectrum signal is greatly reduced.An improved denoising method with threshold is presented by analyzing wavelet principal, which is applied to signal denoising and the defects existed in classical denoising method by using soft and hard threshold. The improved method both overcome the defect, in which the hard threshold method generate discontinuities and the soft threshold method generate a constant variation, which retains useful signal as much as possible. Symlets wavelet is used as a wavelet function with four series,which combines with the per-level threshold determining by the Birge-Massart tactics pattern to deal with fluorescence spectrum signal of CdSe quantum dots denoising in the experiment. Simulation results demonstrate that the rebuilt signal of the improved denoising method by using threshold, which has bigger signal-to-noise ratio and energy accounting and smaller mean squared error than the classical denoising method by using soft and hard threshold.For the case of sampled data point spectrum acquisition system often can't coincide with the feature point. In this paper, by analyzing the commonly used method of discrete data modeling, select the curve fitting method which is moresuitable for more spectral data points to establish the spectral data model. Under the premise of the calculation model taking into account the convenience and suitability of the project, use of three kinds of curve fitting model on the sampling data points fitting within ten order. Through analysis and comparison the quantitative evaluation index just like SSE,RMSE and R-square, finally established the best fitting model of spectral data points in this paper.