Phase-shift interferometry measured transmission matrix of turbid medium: Three-step phase-shifting interference better than four-step one

Abstract

Transmission matrix (TM) is an important tool for controlling light focusing, imaging, and communication through turbid media. It can be measured by 3-step (TM3) or 4-step (TM4) phase-shifting interference, but the similarities and differences of the transmission matrices obtained by the two methods are rarely reported. Therefore, we make a quantitative comparison of the peak light intensity, signal-to-noise ratio, and average background of 24 × 24 = 576 focal points between paired samples (TM3-TM4) through the Wilcoxon rank sum test, and discuss the singular value of the transmission matrix and the focal peak. The comparative results of peak light intensity and signal-to-noise ratio show that there is a significant difference between the 3-step phase shift and the 4-step phase shift transmission matrixes. The focusing effect of the former is significantly better than that of the latter; interest concentrates on the focal intensity and singular value. The reciprocal of the singular value is proportional to the squared intensity, which is in accordance with singular value theory. The results of comparison of peak light intensity and signal-to-noise ratio strongly suggest that 3-step phase shift should be selected and used in applying the phase shift method to the measurement of the transmission matrix; and the singular value is of great significance in quantifying the focusing, imaging, and communication quality of the transmission matrix.

Design of communication transmission prediction model based on data mining algorithm

“…The results are shown in Table 1 and Figure 4. It can be found that the average prediction accuracy of the information transmission interference of the mobile communication system in this model is 96.477%, while the average prediction accuracy of the information transmission interference of the mobile communication system in ref [4] is 87.202%, which shows that this model can reduce the prediction error of the information transmission of the mobile communication system, improve the information transmission effect of the mobile communication system, and has obvious advantages.…”

“…The result is shown in Figure 3. In order to further verify the superiority of DM in information transmission interference prediction of mobile communication system, the ref [4] method is selected for comparative testing in the same experimental environment. The results are shown in Table 1 and Figure 4.…”

“…Zhang, X. C uses three kinds of classifiers: KNN( K nearest neighbor), RF (random forest) and SVM (support vector machine), and the classification accuracy reaches 81.75%, 96.32% and 99.15% respectively 4 . Kim, R proposed each information is expressed by the relative position and offset of an RGB component, and the same information character can be expressed by different codes, and the information prediction infrastructure is given by using the activation diffusion model, so as to predict the mobile network intrusion information 5 .…”