ing at the multi-carrier filter bank transmission system for coherent optical offset quadrature amplitude modulation (CO-FBMC/OQAM) technique, a pilot-based time-domain phase noise compensation algorithm is proposed. A time-domain phase noise compensation model is established, that is, phase noise is approximated by time-domain extended discrete cosine transform (DCT). Phase noise includes common phase error (CPE) and non-CPE phase noise, which can be obtained by estimating DCT coefficients. In order to calculate these DCT coefficients, the CPE is estimated by pilot-based extended Kalman filter (EKF). Then, some of the data with high error probability after CPE compensation are discarded, and only the remaining CPE compensation data are retained for pre-decision to predict the sender data. Finally, the proposed algorithm is simulated and verified in a back-to-back CO-FBMC/OQAM system with a baud rate of 32 GBaud. The results show that compared with an improved phase search (M-BPS) algorithm, the spectral efficiency of the proposed algorithm decreases by 0.5%--2.0%. For 64-order QAM systems with subcarriers M=256 or 512, the linewidth delay product tolerance of the proposed algorithm is still much larger than that of the M-BPS algorithm, but its complexity is only half that of the M-BPS algorithm.
.ing at the problems that the linear dimension reduction method of three-dimensional (3D) fluorescence spectra of algae is not ideal and the model recognition accuracy is low, a classification model is constructed by using local linear embedding (LLE) algorithm to reduce the dimension and using the golden sine algorithm (Gold-SA) to optimize the support vector machine (SVM). The 3D fluorescence spectrum data of algae after dimension reduction by LLE algorithm is used as the input of SVM, and other two dimension reduction methods are compared. The results show that LLE algorithm has the best dimension reduction effect and the highest recognition accuracy. In order to further improve category recognition ability, the Gold-SA is used to optimize SVM and establish a Gold-SA-SVM model, and the other four classification models are compared. The results show that the classification recognition accuracy, precision, recall rate, and F1 score of the Gold-SA-SVM model are significantly improved, and the method can accurately realize the classification of Aureococcus anophagefferens, Chlorella, and Synechococcus elongatus, providing an effective reference for the research of brown tide.
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