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Using these sampled data sets, we propose an artificial neural network (ANN) model to estimate signal quality parameters of high\u2010speed signals. The sampled values can clearly show the characteristics of the waveform after amplitude sorting, which will significantly reduce the error of parameter calculation, and a relatively simple ANN model can be used. Using 25\u2010Gb\/s non\u2010return\u2010to\u2010zero signals as an example, the ANN inputs 40 signal sampling data through a hidden layer with 7 neurons and can estimate five types of signal quality parameters: Q\u2010factor, signal\u2010to\u2010noise ratio, time jitter, rise time, and fall time. The mean square error of the calculated time jitter was 11.8%, and those of the other parameters were lower than 10%. The simple ANN model will be easier to implement in hardware. A field\u2010programmable gate array was used to implement the ANN hardware for estimating signal quality parameters. The calculated average errors of the five signal quality parameters between software and hardware methods were less than 1%. The implemented hardware estimation of high\u2010speed signal quality parameters could be used for real\u2010time signal quality monitoring in high\u2010speed data transmission modules and devices.<\/jats:p>","DOI":"10.1002\/cta.4557","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T04:18:32Z","timestamp":1744604312000},"page":"7260-7270","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010Time Monitoring of High\u2010Speed Signal Quality Using Asynchronous Sampling, Amplitude Sorting, and Artificial Neural Network and Its FPGA Implementation"],"prefix":"10.1002","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2172-9912","authenticated-orcid":false,"given":"Jau\u2010Ji","family":"Jou","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering National Kaohsiung University of Science and Technology  Kaohsiung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun\u2010Liang","family":"Yang","sequence":"additional","affiliation":[{"name":"Graduate Institute of Advanced Semiconductor Technology, Industry\u2010Academia Innovation College National Taiwan University of Science and Technology  Taipei City Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih\u2010Lung","family":"Tseng","sequence":"additional","affiliation":[{"name":"Department of Green Energy and Information Technology National Taitung University  Taitung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun\u2010Chen","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering National Kaohsiung University of Science and Technology  Kaohsiung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun\u2010Yuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering National Kaohsiung University of Science and Technology  Kaohsiung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"A.Tartaglia R.Magri andA.Deho \u201cOptical Solutions for 5G: Technologies and Network Architectures \u201d inIEEE 21st International Conference on Transparent Optical Networks We.D2.1. 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