scholarly journals Function Estimation Using Data-Adaptive Kernel Smoothers—How Much Smoothing?

1994 ◽  
Vol 8 (4) ◽  
pp. 402 ◽  
Author(s):  
K. S. Riedel ◽  
A. Sidorenko ◽  
James R. Matey
Keyword(s):  
Function Estimation ◽  
Adaptive Kernel ◽  
Data Adaptive ◽  
Using Data ◽  
Kernel Smoothers

scholarly journals Fast Feature-Preserving Approach to Carpal Bone Surface Denoising

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2379
Author(s):  
Ibrahim Salim ◽  
A. Hamza
Keyword(s):  
Objective Function ◽  
Linear Equations ◽  
Noise Model ◽  
Carpal Bone ◽  
Geometric Framework ◽  
Sparse System ◽  
Adaptive Kernel ◽  
Feature Preserving ◽  
Data Adaptive ◽  
Matrix Balancing

We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. The proposed approach is formulated as a constrained optimization problem whose objective function consists of a fidelity term specified by a noise model and a regularization term associated with prior data. Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. Minimizing the objective function reduces it to iteratively solve a sparse system of linear equations via the conjugate gradient method. Extensive experiments on noisy carpal bone surfaces demonstrate the effectiveness of our approach in comparison with existing methods. We perform both qualitative and quantitative comparisons using various evaluation metrics.

Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier

2015 ◽  
Vol 53 (12) ◽  
pp. 1345-1360 ◽  
Author(s):  
Sulaimon Ibrahim ◽  
Pradeep Chowriappa ◽  
Sumeet Dua ◽  
U. Rajendra Acharya ◽  
Kevin Noronha ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Neuro Fuzzy ◽  
Data Adaptive ◽  
Using Data

scholarly journals Passive Acoustic Mapping Using Data-Adaptive Beamforming Based on Higher Order Statistics

2018 ◽  
Vol 37 (12) ◽  
pp. 2582-2592 ◽  
Author(s):  
Erasmia Lyka ◽  
Christian M. Coviello ◽  
Catherine Paverd ◽  
Michael D. Gray ◽  
Constantin C. Coussios
Keyword(s):  
Order Statistics ◽  
Adaptive Beamforming ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Data Adaptive ◽  
Using Data ◽  
Passive Acoustic ◽  
Acoustic Mapping

Artifact suppression from EEG signals using data adaptive time domain filtering

2012 ◽  
Vol 97 ◽  
pp. 297-308 ◽  
Author(s):  
Md. Khademul Islam Molla ◽  
Md. Rabiul Islam ◽  
Toshihisa Tanaka ◽  
Tomasz M. Rutkowski
Keyword(s):  
Time Domain ◽  
Eeg Signals ◽  
Artifact Suppression ◽  
Adaptive Time ◽  
Data Adaptive ◽  
Using Data

Automatic seismogram analysis: Statistical phase picking and locating methods using one-station three-component data

1992 ◽  
Vol 82 (2) ◽  
pp. 860-869 ◽  
Author(s):  
Matti Tarvainen
Keyword(s):  
Maximum Likelihood Method ◽  
Statistical Data ◽  
Polarization State ◽  
Adaptive Method ◽  
Likelihood Method ◽  
Arrival Times ◽  
On Line ◽  
The Difference ◽  
Data Adaptive ◽  
Using Data

Abstract Seismic events at local and regional distances are located using data from the Vaasa (VAF 63.0 °N, 22.7 °E) three-component station in western Finland. The analysis is performed off-line after pseudo on-line detections. The continuous data are first recorded on long disk loops. These loops can cover 10 days of data. The events are picked using Murdock-Hutt detector and after that they are recorded as separate files for further analysis. The direction of approach, which estimates the polarization state of the signal, is determined by the well-established maximum likelihood method. The difference between the arrival times of P and S phases is used to estimate the distance. Phases observed up to 185 km distance from the source are assumed to be Pg and Sg and beyond that Pn and Sn. The onset times of the phases are estimated by a statistical data-adaptive method. The onsets calculated in this way are very accurate when compared with earlier methods like STA/LTA. Epicenter locations are similar to those of FINSA network reported in Helsinki bulletins. The median of the location differences is about 50 km.

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