Common Filtering Algorithms Research Articles

Research on Off-Axis Integrated Cavity Output Spectrum Signal Denoising Based on CSGWO-SVMD-SVD method

To mitigate the susceptibility of absorption lines to noise interference during gas measurement using the off-axis integrated cavity output spectroscopy (OA-ICOS) technique, thereby enhancing measurement accuracy, this paper introduces a novel denoising method. This method synergistically integrates Grey Wolf Optimization (GWO) and Cuckoo Search (CS) with Singular Value Decomposition (SVD) and Sequential Variational Mode Decomposition (SVMD). Initially, the optimal solution for the quadratic penalty coefficient in SVMD is ascertained via iterative optimization using the CSGWO algorithm. Subsequently, a circulant matrix is established to extract the singular values of each modal component. A threshold is set to discriminate between noise and useful signals, and the singular values corresponding to noise are nullified. The useful signal components are then reconstructed, yielding the final processed signal. The proposed algorithm was applied to both simulated and experimental target signals and compared with common filtering algorithms such as WT, VMD-WTD, and S-G. In the experimental signal processing results, the signal-to-noise ratio (SNR) of the absorption spectrum signal improved from 21.4 to 39.95, and the correlation coefficient increased from 0.99715 to 0.99946. Results indicate that the proposed algorithm exhibits superior identification and noise suppression capabilities compared to other algorithms. After signal processing using the CSGWO-SVMD-SVD algorithm, the accuracy and stability of signal data detected based on off-axis integration cavity output spectroscopy technology have been greatly improved.

Infrared small target detection algorithm based on filter kernel combination optimization learning method

Infrared small target detection in complex backgrounds remains a big challenge due to its small size and weak energy, which makes it easy to be submerged in complex backgrounds. Common filtering algorithms are difficult to apply to various complex scenes and generate many false alarms and poor robustness. Therefore, this paper proposes an infrared small target detection algorithm based on filter kernel combination optimization learning method. For the sample image block use typical filter kernels and weight coefficients to calculate the combination of filtering results, and construct the loss function of the results and the true value. The optimal weight coefficients are learned using an optimization method, which effectively achieves the fusion of multiple features, then we build a sample library containing comprehensive priori information. In the implementation, an online inference method based on similarity measure is proposed to match test images with sample library image blocks to obtain optimal combination weights. By utilizing the comprehensive priori information to enhance the adaptive ability of the algorithm for various complex backgrounds. Finally, get the position of the infrared small target. The experimental results show that our algorithm has excellent detection performance for infrared small targets with complex background and low signal to clutter ratio, and the AUC value is 0.9980, which is more robust than other comparative algorithms.

Robotic Arm Control System Based on AI Wearable Acceleration Sensor

The position of mechanical arm in people’s life is getting higher and higher. It replaces the function of human arm, moving and moving in space. Generally, the structure is composed of mechanical body, controller, servo mechanism, and sensor, and some specified actions are set to complete according to the actual production requirements. The manipulator has flexible operation, good stability, and high safety, so it is widely used in industrial automation production line. With the development of science and technology, many practical production requirements for the function of the manipulator are more and more refined, especially in the high-end research field. For example, medical devices, automobile manufacturing, deep-sea submarines, and space station maintenance put forward higher requirements for it. In terms of miniaturization and precision, it can meet the needs of scientific research and actual production. But these are inseparable from the motion control system technology. This paper mainly introduces the research of manipulator control system based on AI wearable acceleration sensor, aiming to provide some ideas and directions for the research of wearable manipulator. This paper presents the research method of manipulator control system based on AI wearable acceleration sensor, including the establishment of manipulator kinematics model, common filtering algorithm, and PI algorithm of speed control system. It is used for the research and experiment of manipulator control system based on AI wearable acceleration sensor. The experimental results show that the average matching rate of the manipulator control system based on AI wearable acceleration sensor is as high as 88.89%, and the stability of the feature descriptor is high.

Volleyball track acquisition and intelligent analysis technology

Volleyball has been developing rapidly since 1996. It has been widely used both in competitive events and among ordinary people. However, with the continuous improvement of the sports level, the traditional manual training methods cannot meet the existing technical requirements. It is a mainstream method to analyze the track of volleyball by the computer in volleyball training. However, there are still some technical problems such as low precision and incomplete analysis. Therefore, this paper puts forward the research of volleyball track acquisition and intelligent analysis technology. In this paper, the shortcomings of the existing technology are systematically analyzed, and on this basis, the optimization and improvement scheme is proposed. The core technology of this project is to improve the original image preprocessing technology and strengthen the system’s feature extraction ability. Finally, combined with the CAMSHAFT moving object tracking algorithm, the technical scheme of this paper is formed. Through a series of technical improvements, the system effectively improves the ability to extract and analyzing the track of volleyball. In order to further verify the practical effectiveness of this scheme, a number of comparative experiments including algorithm accuracy comparison experiment, trajectory recognition detection, and algorithm signal-to-noise ratio verification are carried out. The object of comparison is the current mainstream common filtering algorithm. Through the analysis of experimental data, this method is more accurate than the common filtering algorithm in the extraction of volleyball trajectory, which effectively improves the comprehensive performance and robustness of the traditional method.

Application of L1 Trend Filtering Technology on the Current Time Domain Spectroscopy of Dielectrics

The current time domain spectroscopy of dielectrics provides important information for the analysis of dielectric properties and mechanisms. However, there is always interference during the testing process, which seriously affects the analysis of the test results. Therefore, the effective filtering of current time domain spectroscopy is particularly necessary. L1 trend filtering can estimate the trend items exactly in a set of time series. It has been widely used in the fields of economics and sociology. Therefore, this paper attempts to apply L1 trend filtering to the current time domain spectroscopy. Firstly, polarization and depolarization currents are measured in the laboratory. Then the test results are filtered by L1 trend filtering and the filtering effects are compared with several common filtering algorithms, such as a sliding mean filter and Savitzky–Golay smoothing filter. Finally, the robustness and time complexity of L1 trend filtering are analyzed. The filtering results show that because the polarization currents vary in a wide range of the time domain (about 2–3 orders of magnitude), smooth and undistorted curves in the whole test time range can hardly be obtained through common filtering algorithms, while they can be obtained by L1 trend filtering. The results of robustness analysis and time complexity analysis show that L1 trend filtering can extract the trend items accurately in the time series under given different noise levels, and the execution time is also lower than 176.67 s when the number of tested points is no more than 20,000. Those results show that L1 trend filtering can be applied to the time domain current spectroscopy of dielectrics.