scholarly journals Non-Parametric Source Term Uncertainty Estimation.

2020 ◽  
Author(s):  
Dusty Brooks
Keyword(s):  
Source Term ◽  
Uncertainty Estimation ◽  
Parametric Source ◽  
Non Parametric

scholarly journals Source localization for multiple speech sources using low complexity non-parametric source separation and clustering

2011 ◽  
Vol 91 (8) ◽  
pp. 1781-1788 ◽  
Author(s):  
M. Swartling ◽  
B. Sällberg ◽  
N. Grbić
Keyword(s):  
Source Localization ◽  
Source Separation ◽  
Low Complexity ◽  
Parametric Source ◽  
Non Parametric

scholarly journals Unimodality-Constrained Matrix Factorization for Non-Parametric Source Localization

2019 ◽  
Vol 67 (9) ◽  
pp. 2371-2386 ◽  
Author(s):  
Junting Chen ◽  
Urbashi Mitra
Keyword(s):  
Source Localization ◽  
Matrix Factorization ◽  
Parametric Source ◽  
Non Parametric

Uncertainty Estimation in Flood Inundation Mapping: An Application of Non-parametric Bootstrapping

2017 ◽  
Vol 33 (4) ◽  
pp. 611-619 ◽  
Author(s):  
M. Faghih ◽  
M. Mirzaei ◽  
J. Adamowski ◽  
J. Lee ◽  
A. El-Shafie
Keyword(s):  
Uncertainty Estimation ◽  
Flood Inundation ◽  
Parametric Bootstrapping ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Non Parametric

Non-parametric Source Reconstruction via Kernel Temporal Enhancement for EEG Data

2017 ◽  
pp. 443-450
Author(s):  
C. Torres-Valencia ◽  
J. Hernandez-Muriel ◽  
W. Gonzalez-Vanegas ◽  
A. Alvarez-Meza ◽  
A. Orozco ◽  
...  
Keyword(s):  
Source Reconstruction ◽  
Eeg Data ◽  
Parametric Source ◽  
Non Parametric

ESTIMATION OF PRESSURE DROP FOR NON-NEWTONIAN LIQUID FLOW THROUGH BENDS USING ADAPTIVE NON-PARAMETRIC MODEL

2020 ◽  
Vol 47 (1) ◽  
pp. 59-69
Author(s):  
Suman Debnath ◽  
Anirban Banik ◽  
Tarun Kanti Bandyopadhyay ◽  
Mrinmoy Majumder ◽  
Apu Kumar Saha
Keyword(s):  
Pressure Drop ◽  
Liquid Flow ◽  
Parametric Model ◽  
Newtonian Liquid ◽  
Flow Through ◽  
Non Parametric

SISO QFT Design With Non-Parametric Uncertainties

1993 ◽  
Author(s):  
Yossi Chait ◽  
Yakov Z. Tsypkin
Keyword(s):  
Parametric Uncertainties ◽  
Non Parametric

Contrast-Enhanced MRI with Magnetization Transfer Effect in the Imaging of Brain Metastatic Lesions

2017 ◽  
pp. 8-17
Author(s):  
A. A. Ermakova ◽  
O. Yu. Borodin ◽  
M. Yu. Sannikov ◽  
S. D. Koval ◽  
V. Yu. Usov
Keyword(s):  
Comparative Analysis ◽  
Roc Analysis ◽  
Magnetization Transfer ◽  
Metastatic Lesion ◽  
Transfer Effect ◽  
Wilcoxon Test ◽  
Post Contrast ◽  
Metastatic Lesions ◽  
The Brain ◽  
Non Parametric

Purpose: to investigate the diagnostic opportunities of contrast  magnetic resonance imaging with the effect of magnetization transfer effect in the diagnosis of focal metastatic lesions in the brain.Materials and methods.Images of contrast MRI of the brain of 16  patients (mean age 49 ± 18.5 years) were analysed. Diagnosis of  the direction is focal brain lesion. All MRI studies were carried out  using the Toshiba Titan Octave with magnetic field of 1.5 T. The  contrast agent is “Magnevist” at concentration of 0.2 ml/kg was  used. After contrasting process two T1-weighted studies were  performed: without T1-SE magnetization transfer with parameters of pulse: TR = 540 ms, TE = 12 ms, DFOV = 24 sm, MX = 320 × 224  and with magnetization transfer – T1-SE-MTC with parameters of pulse: ΔF = −210 Hz, FA(МТС) = 600°, TR = 700 ms, TE = 10 ms,  DFOV = 23.9 sm, MX = 320 x 224. For each detected metastatic  lesion, a contrast-to-brain ratio (CBR) was calculated. Comparative  analysis of CBR values was carried out using a non-parametric  Wilcoxon test at a significance level p < 0.05. To evaluate the  sensitivity and specificity of the techniques in the detection of  metastatic foci (T1-SE and T1-SE-MTC), ROC analysis was used. The sample is divided into groups: 1 group is foci ≤5 mm in size, 2  group is foci from 6 to 10 mm, and 3 group is foci >10 mm. Results.Comparative analysis of CBR using non-parametric Wilcoxon test showed that the values of the CBR on T1-weighted  images with magnetization transfer are significantly higher (p  <0.001) that on T1-weighted images without magnetization transfer. According to the results of the ROC analysis, sensitivity in detecting  metastases (n = 90) in the brain on T1-SE-MTC and T1-SE was  91.7% and 81.6%, specificity was 100% and 97.6%, respectively.  The accuracy of the T1-SE-MTC is 10% higher in comparison with  the technique without magnetization transfer. Significant differences (p < 0.01) between the size of the foci detected in post-contrast T1- weighted images with magnetization transfer and in post-contrast  T1-weighted images without magnetization transfer, in particular for  foci ≤5 mm in size, were found. Conclusions1. Comparative analysis of CBR showed significant (p < 0.001)  increase of contrast between metastatic lesion and white matter on  T1-SE-MTC in comparison with T1-SE.2. The sensitivity, specificity and accuracy of the magnetization transfer program (T1-SE-MTC) in detecting foci of  metastatic lesions in the brain is significantly higher (p < 0.01), relative to T1-SE.3. The T1-SE-MTC program allows detecting more foci in comparison with T1-SE, in particular foci of ≤5 mm (96% and 86%, respectively, with p < 0.05).

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