Modified Sliding Singular Spectrum Analysis based Non-Contact Type Detection of Separated Instrument in Root Canal Therapy using Low Frequency Ultrasonic Pattern Transceiver Design

2021 ◽  
pp. 1-1
Author(s):  
Rajiv Kapoor ◽  
Deepak Sharma ◽  
Aarchishya Kapoor
Keyword(s):  
Spectrum Analysis ◽  
Root Canal ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Root Canal Therapy ◽  
Contact Type ◽  
Transceiver Design ◽  
Singular Spectrum

scholarly journals Multivariate Extension of the Singular Spectrum Analysis A New Tool in Understanding the Intraseasonal-Oscillation (ISO) of Philippines Summer Monsoon and its Association with Extreme Rainfall Events

2020 ◽  
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Intraseasonal Oscillation ◽  
Low Frequency ◽  
Extreme Rainfall ◽  
Active Phase ◽  
Relative Strength ◽  
Extreme Rainfall Events ◽  
Singular Spectrum ◽  
Increasing Trend

<p>The intraseasonal low-frequency oscillation is studied by using gridded rainfall dataset from 1979 to present. Multivariate Singular Spectrum Analysis (SSA) was used in the extraction of these modes. We have found out that there is a significant increasing trend in the relative strength of low frequency ISO. However, this happened only after the years 1979 to 1984 was removed in the trend analysis. A previous study found that the increased rainfall during the break and transition phase of low-frequency ISO could be a contributing factor to the decreasing trend during the active phase. In this study, the implication is rather the opposite - the increasing trend in rainfall, in terms of extreme events occurrence during the active phase of low-frequency ISO had been the contributor to the increasing trend of low-frequency ISO.</p>

scholarly journals Filter Characteristics in Image Decomposition with Singular Spectrum Analysis

2016 ◽  
Vol 08 (01) ◽  
pp. 1650002 ◽  
Author(s):  
Kenji Kume ◽  
Naoko Nose-Togawa
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Image Data ◽  
Original Data ◽  
Multidimensional Data ◽  
Singular Spectrum ◽  
Symmetry Properties ◽  
Frequency Components ◽  
Differential Type

Singular spectrum analysis is developed as a nonparametric spectral decomposition of a time series. It can be easily extended to the decomposition of multidimensional lattice-like data through the filtering interpretation. In this viewpoint, the singular spectrum analysis can be understood as the adaptive and optimal generation of the filters and their two-step point-symmetric operation to the original data. In this paper, we point out that, when applied to the multidimensional data, the adaptively generated filters exhibit symmetry properties resulting from the bisymmetric nature of the lag-covariance matrices. The eigenvectors of the lag-covariance matrix are either symmetric or antisymmetric, and for the 2D image data, these lead to the differential-type filters with even- or odd-order derivatives. The dominant filter is a smoothing filter, reflecting the dominance of low-frequency components of the photo images. The others are the edge-enhancement or the noise filters corresponding to the band-pass or the high-pass filters. The implication of the decomposition to the image denoising is briefly discussed.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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