Simulation of microbubble displacement and normalized singular spectrum area measurements of microbubble signals in a vessel phantom

2021 ◽  
Author(s):  
Yi Huang ◽  
Elizabeth Herbst ◽  
John A. Hossack
Keyword(s):  
Singular Spectrum ◽  
Spectrum Area

scholarly journals Validation of Normalized Singular Spectrum Area as a Classifier for Molecularly Targeted Microbubble Adherence

2019 ◽  
Vol 45 (9) ◽  
pp. 2493-2501 ◽  
Author(s):  
Elizabeth B. Herbst ◽  
Sunil Unnikrishnan ◽  
Alexander L. Klibanov ◽  
F. William Mauldin ◽  
John A. Hossack
Keyword(s):  
Singular Spectrum ◽  
Spectrum Area

Microbubble signal classification using normalized singular spectrum area based filtering methods

2017 ◽  
Author(s):  
Elizabeth B. Herbst ◽  
Shiying Wang ◽  
Alexander L. Klibanov ◽  
F. William Mauldin ◽  
John A. Hossack
Keyword(s):  
Signal Classification ◽  
Singular Spectrum ◽  
Spectrum Area

Validation of normalized singular spectrum area as a classifier for targeted microbubble enhancement

2019 ◽  
Author(s):  
Elizabeth B. Herbst ◽  
Shiying Wang ◽  
Alexander L. Klibanov ◽  
F. William Mauldin ◽  
John A. Hossack
Keyword(s):  
Singular Spectrum ◽  
Spectrum Area

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.

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