Temporal and Spatial Phase Analyses of the Electrocardiogram Stratify Intra-Atrial and Intra-Ventricular Organization

2004 ◽  
Vol 51 (10) ◽  
pp. 1749-1764 ◽  
Author(s):  
S.M. Narayan ◽  
V. Bhargava
Keyword(s):  
Spatial Phase ◽  
Temporal And Spatial

Automated fringe pattern analysis in experimental mechanics: A review

1998 ◽  
Vol 33 (2) ◽  
pp. 105-125 ◽  
Author(s):  
J M Huntley
Keyword(s):  
Solid Mechanics ◽  
Automated Analysis ◽  
Random Errors ◽  
Orthotropic Plates ◽  
Fine Grid ◽  
Ball Impact ◽  
Spatial Phase ◽  
Bending Waves ◽  
Fringe Pattern Analysis ◽  
Temporal And Spatial

The paper reviews the main numerical techniques that have been developed to carry out fully automated analysis of fringe patterns resulting from solid mechanics experiments. These include temporal and spatial phase shifting interferometry, temporal and spatial phase unwrapping, and calculation of strain fields from the phase maps. Systematic and random errors associated with the various procedures are also analysed. A unified treatment for both speckle and smooth-wavefront interferograms is presented, and the common features underlying many of the algorithms are emphasized. The paper is illustrated with applications that include ball impact (moiré photography), bending waves in orthotropic plates (double-pulsed dual-reference wave holography) and finite strains in propellant grains (fine grid technique).

scholarly journals One-Step Three-Dimensional Phase Unwrapping Approach Based on Small Baseline Subset Interferograms

2020 ◽  
Vol 12 (9) ◽  
pp. 1473 ◽  
Author(s):  
Christina Esch ◽  
Joël Köhler ◽  
Karlheinz Gutjahr ◽  
Wolf-Dieter Schuh
Keyword(s):  
Spatial Information ◽  
Real Data ◽  
Single Step ◽  
Phase Unwrapping ◽  
Motion Model ◽  
Phase Gradient ◽  
Spatial Phase ◽  
Small Baseline Subset ◽  
Temporal And Spatial ◽  
Small Baseline

One of the most critical steps in a multitemporal D-InSAR analysis is the resolution of the phase ambiguities in the context of phase unwrapping. The Extended Minimum Cost Flow approach is one of the potential phase unwrapping algorithms used in the Small Baseline Subset analysis. In a first step, each phase gradient is unwrapped in time using a linear motion model and, in a second step, the spatial phase unwrapping is individually performed for each interferogram. Exploiting the temporal and spatial information is a proven method, but the two-step procedure is not optimal. In this paper, a method is presented which solves both the temporal and spatial phase unwrapping in one single step. This requires some modifications regarding the estimation of the motion model and the choice of the weights. Furthermore, the problem of temporal inconsistency of the data, which occurs with spatially filtered interferograms, must be considered. For this purpose, so called slack variables are inserted. To verify the method, both simulated and real data are used. The test region is the Lower-Rhine-Embayment in the southwest of North Rhine-Westphalia, a very rural region with noisy data. The studies show that the new approach leads to more consistent results, so that the deformation time series of the analyzed pixels can be improved.

Sign in / Sign up

Export Citation Format

Share Document