Research on clustering analysis of eye diagram point set of digital signal based on equivalent time sampling


  • Jingkai Ni



Equivalent time sampling, Error sum of squares, Time complexity, clustering.


With the continuous development and improvement of communication technology, the research and application of high-frequency signals are becoming more and more important, especially in important fields such as electronic communication, aerospace and aviation. The analysis of high-frequency signals is the most basic and most important. High-frequency digital signals are mainly obtained by equivalent time sampling and sequential sampling. This paper first analyzes the basic principles of equivalent time sampling and real-time sampling, and compares the advantages and disadvantages of the two and the limitations of each sampling method through various indicators. After that, the eye diagram point set based on equivalent time sampling is clustered and analyzed. By comparing the contour coefficients, the sum of squared errors, and the time complexity of each clustering algorithm, the clustering method of the eye diagram point set is further optimized, and the most efficient and accurate clustering algorithm is selected. The clustering algorithm is optimized by increasing the multi-selective convergence threshold based on each cluster center.After many experiments and simulations, from the perspective of various clustering indicators, there is no significant difference between the K-Means clustering algorithm and the K-Mediods clustering algorithm in the case of fewer data points. However, in the case of relatively large data points, the K-Mediods clustering algorithm is more accurate and efficient than the K-Means clustering algorithm. Moreover, compared with the original K-Mediods clustering algorithm, the clustering effect of the K-Mediods clustering algorithm after multi-selective optimization in terms of convergence threshold is reflected in both the sum of squared errors and the contour coefficient. Both have better accuracy.


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Yang Kun. Study on Transient Sampling and Signal Conditioning Circuits of Sampling Oscilloscope [D]. North Central University, 2023.

Liu Jingjing. Research on random sampling model and its signal processing algorithm [D]. University of Electronic Science and Technology of China2018.

Du Xiuli; li Shixin; qiu Shaoming; chen Bo. Time discrimination based random equivalent sampling short time measurement method [J]. Instrument technology and sensor.2016, (11):114-117+122.

Theodore W. Hansy. Optical wave interference sampling oscilloscope [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition).2021, 33(05):699-713.

Liu Jianbo, Guo Wenxiu, Zhang Jie and so on. Equivalent Time Sampling Based on FPGA [J]. Electronic Design Engineering.2015, 23(02):122-124+129.

Cheng C, Desheng R, Yanlin Y, et al. TOPSIS based multi-fidelity Co-Kriging for multiple response prediction of structures with uncertainties through real-time hybrid simulation[J].Engineering Structures,2023,280

Feng Xinru, Jingning, Yinziyan. High-speed chirp signal recovery based on equivalent sampling [J]. Single-chip microcomputer and embedded system applications, 2023, 23 (09): 37-40.

Li Haitao, Ruan Linbo, Tian Geng. Sequential equivalent sampling method based on cascaded step delay and its implementation [J]. Automation Instrumentation, 2020, 41 (10): 74-77.

Zhixin T, Dingkai Z, Shunhe H, et al.A Hierarchical Random Graph Efficient Sampling Algorithm Based on Improved MCMC Algorithm[J].Electronics,2022,11(15):2396-2396.

Gao Qiang, Ma Shimin, Liu Jie. Vector network analyzer eye diagram intersection point estimation method [J]. Metrology.2023, 44(05):777-782.

Zhan C, Bai K, Tu B, et al.Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging[J].Sensors,2024,24(2).

Guo Kenan. Data detection technology based on fusion improved K-means clustering algorithm [J]. Electronic Design Engineering, 2024, 32 (05): 41-45.

Wang W, Lou B, Li X, et al.Intelligent maintenance frameworks of large-scale grid using genetic algorithm and K-Mediods clustering methods[J].World Wide Web: Internet and Web Information Systems,2020,23(3):1177-1195.

Zhang Shangyao. Research on runoff prediction and multi-model combination based on multi-feature-driven machine learning [D]. Xi 'an University of Technology, 2023.

Chen X, Güttel S. Fast and explainable clustering based on sorting [J]. Pattern Recognition, 2024, 150110298-.




How to Cite

Ni, J. (2024). Research on clustering analysis of eye diagram point set of digital signal based on equivalent time sampling. Highlights in Science, Engineering and Technology, 105, 273-282.