Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.

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The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

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On the basis of the spectral analysis for the Lax pair, a Riemann–Hilbert problem of the combined nonlinear Schrödinger and Gerdjikov–Ivanov equation is established. Using the inverse scattering transformation and the Riemann–Hilbert approach, the combined nonlinear Schrödinger and Gerdjikov–Ivanov equation is studied. As an application, N-soliton solutions of the combined nonlinear Schrödinger and Gerdjikov–Ivanov equation are obtained. In addition, some figures are given to illustrate the soliton characteristics of the nonlinear integrable equation.

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Conducting optical coatings for the visible light range are commonly made of Indium Tin Oxide (ITO), but ITO is unsuitable for near-infrared telecommunications wavelengths because it can become absorptive after extended illumination. In this paper we show an alternative approach which uses conventional coating materials to create either non-conducting or conducting antireflection (AR) coatings that are effective over a fairly broad spectral region ( lambdalong/lambdashort approximately 1.40) and also usable for a wide range of angles of incidence (0-38 masculine, or 0-55 masculine) in the telecom wavelength range. Not only is the transmittance of windows treated with such coatings quite high, but they can be made to have extreme polarization independence (low polarization dependent loss values). A number of such coating designs are presented in the paper. A prototype of one of the conducting AR coating designs was fabricated and the measurements were found to be in reasonable agreement with the calculated performance. Such AR coatings should be of interest for telecommunication applications and especially for anti-static hermetic packaging of MEMS devices such as optical switches.