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Objectives: Length of day variation (ΔLOD) predictions play an important role in tracking and navigation of deep-space detector, precise determination of artificial satellite orbit and climate forecasting. In full consideration of the time-varying characteristics of ΔLOD, we present the application of a hybrid technique for predicting ΔLOD. Methods: The ΔLOD predictions are generated by means of the combination of singular spectrum analysis (SSA) extrapolation for the linear trend, annual and semiannual oscillations in ΔLOD based on an iterative interpolation strategy, and autoregressive integrated moving average (ARIMA) stochastic prediction of SSA remaining residuals, referred to as SSA+ARIMA. In order to evaluate the effectiveness of this approach, the ΔLOD predictions up to 365 d into the future are calculated year-by-year for the 2-year period from Jan. 1,2000 to Dec. 31, 2001 using the data covering the previous 10 years from International Earth Rotation and Reference Systems Service C04 series. The prediction results are analyzed and compared with those obtained by machine learning methods such as back propagation neural network (BPNN), general regression neural network (GRNN) and Gaussian process (GP). Results: It is shown that the accuracy of the predictions are better than that by machine learning methods in terms of the mean absolute error (MAE) of predictions, especially for medium and long-term predictions. Compared with the predictions obtained by the BPNN, GRNN and GP, the MAE of the proposed SSA+ARIMA predictions up to 30 d and 365 d in future is reduced by 39% and 61%, respectively. Conclusions: The proposed method provides a new solution for ΔLOD prediction. [ABSTRACT FROM AUTHOR]

Objectives: Backscatter coefficient (σ 0) is one of observations of satellite radar altimetry, which is widely used in the processes of surface state monitoring, snow thickness inversion, data calibration and verification of satellite altimeters, and other fields. The geophysical data record data of Jason-2 is used to extract and isolate the Ku-band σ 0 data of the Tibetan Plateau (TP). Methods: Taking the GlobeLand30 2020 version data as the basis for surface classification, σ 0 is given surface attributes by latitude and longi‑ tude data. And we obtain the time-varying sequences of σ 0 under different types of surface features from De‑ cember 2008 to September 2016. The singular spectrum analysis principle is used to extract the σ 0 time change trend and period information, and the period results are analyzed by fast Fourier transform. Results: The results show that the σ 0 is higher in waters and wetland areas, and is lower in permanent snow and ice areas. There are multiple period signals of σ 0 in the TP. Conclusions: The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual σ 0 change is not significant. The other re‑ gions have significant annual and semi-annual cycles of σ 0 variability, and the amplitude of variability is not consistent across regions, with different regions corresponding to different changes in σ 0 trends. [ABSTRACT FROM AUTHOR]

Many existing arrhythmia researches focus on the separation of different frequency characteristic components in the ECG signal. However, the contribution of different subsequences to the final target decision-making is lack of research and analysis. In order to enhance the impact of high-contribution subsequences on the classifier, a recognition method combining variable weight singular spectrum analysis and deep learning is proposed. Multiple subsequences are obtained through singular spectrum analysis. The Gini coefficient under the random forest is calculated by the singular value of each sequence and used as the weight. The sequence samples with variable weights are used to train the neural network model, which can mine useful information more efficiently and further improve the recognition accuracy. The accuracy rate of final arrhythmia recognition is 98. 35%, and Macro-Fl is 97. 95%. Compared with the traditional fixed weight, the proposed recognition method of variable weight has a significant improvement in various performance indicators. [ABSTRACT FROM AUTHOR]

Objectives: There are obvious seasonal variations in the GPS height time series, which affect the improvement of precision and can be corrected by both mathematical modelling and geophysical mechanisms. Compared to least square fitting, singular spectrum analysis (SSA) can extract random seasonal signals effectively through signal reconstruction, which is unaffected by the assumed sinusoidal waves. According to the elastic loading theory, the gravity recovery and climate experiment (GRACE) can be used to calculate the vertical surface displacement caused by changes in terrestrial water storage. Methods: This paper mainly studies the feasibility of correcting the seasonal variations in GPS heights using SSA and GRACE inversion results. The height time series of 27 GPS stations in Australia with a time span of from 5 to 10 years were chosen and combined with GRACE simultaneous inversions. Results: Because the spatial resolutions of GRACE are coarse and the loading displacement is much more sensitive to near‑field mass changes than far‑field ones, the amplitudes of GRACE ‑inferred hydrological loading deformations are significantly smaller than GPS. The weighted root mean square (WRMS) are reduced at 22 stations after GRACE ‑inferred displacement corrections, and the correlation coefficients between deformations estimated by GPS and GRACE range from 0.12 to 0.78 with a mean value of 0.43, indicating that GPS and GRACE results have good consistency and correlation. SSA is used to extract the annual signals of vertical displacements derived from GPS and GRACE, and contribution rates of singular spectral variance of annual signals are 21.60% and 34.48%, respectively, expressing that annual signals are the main components of GRACE inferred results. Geographical climatic conditions have a significant impact on the consistency of annual signals derived from GPS and GRACE. Compared with the arid areas in central and western Australia, the amplitude and phase of annual signals derived from GPS and GRACE are more consistent in the northern region with seasonal rainfall. Furthermore, cross wavelet transform (XWT) finds that the vertical displacement series derived from GPS and GRACE of each station have a significant resonance period of one year. The circular average phase angles of GPS/GRACE at the period closet to 1 cycle per year (cpy) outside the cone of influence range from − 74.03° to 67.23° . The mean XWT ‑ based semblances range from 0.28 to 0.99 with an average value of 0.79, showing that there is a significant positive correlation between the annual variations derived from GPS and GRACE. Conclusions: Overall, GRACE‑inferred deformations can explain the annual variations of GPS‑derived displacements, particularly in areas with high hydrological loading. It is possible to correct the annual signals of GPS heights by GRACE inversions, but the effect is not as good as the SSA‑filtered annual signals. [ABSTRACT FROM AUTHOR]

In order to effectively extract useful information from time series of the global naviga- tion satellite system(GNSS) sites, and improve the modeling accuracy of coordinate time series, this paper proposes a nonlinear motion modeling method combining wavelet decomposition and singular spectrum analysis. Experiments were carried out using global positioning system(GPS) vertical coordinate time series of 11 stations around the world from 1999 to 2018. Methods: Firstly, the coordinate time series is decom-posed into different scales by wavelet decomposition.And then the singular spectrum analysis(SSA) is per- formed on the high-frequency part and the low -frequency part of each layer. Finally, combine the fitting value that is the fitting coordinate time series, and the fitting effect of the new method was evaluated. Results: The results show that, compared with the simple singular spectrum analysis method, the new method can extract useful information such as trend and period more accurately from the limited scale of the time series with noise. And reduce the partial period items in the singular spectrum analysis method to some extent, for example, the seasonal period item and the monthly period item are regarded as the probability of noise rejec- tion, and the modeling accuracy is improved by 26%. Conclusions: The purpose is to propose a nonlinear motion modeling method based on wavelet decomposition and singular spectrum analysis, so as to improve the modeling accuracy of coordinate time series. [ABSTRACT FROM AUTHOR]

For traditional data fusion algorithms, the fusion performance of high-noise, large-scale and complex-structure time series data is poor. A hybrid neural network data fusion algorithm (i.e. the SCLG algorithm) was proposed to solve this problem. Firstly, the time series data were decomposed and reconstructed with the singular spectrum analysis algorithm to eliminate noise. Secondly, the spatial and short-term characteristics of the data were extracted by means of the deep convolutional neural network. Thirdly, the long short-term memory neural network and the gated recurrent unit neural network were introduced to extract data features in the time dimension. Finally, the fully connected layer was applied to integrate the main information and output the final decision. The experimental results from the SP&500 and AQI data sets show that, the proposed algorithm is superior to DCNN, CNN-LSTM and FDL in terms of fusion performance and stability. [ABSTRACT FROM AUTHOR]

This paper proposed a new speech concealment algorithm for many speech concealment algorithms with small hidden capacity and bad transparency. Firstly, it compressed and encoded the concealed information by G. 729A, and then applied the lifting wavelet transform and singular spectrum analysis to the audio information hiding. Experiments show that the audio file embedded in the hidden information by the algorithm not only has a significant improvement in the hidden information capacity, but also has good transparency, and can also resist certain attacks. The algorithm in concealed capacity, transparency and robustness reaches a better balance. [ABSTRACT FROM AUTHOR]