A study on three-dimensional (spatio-temporal) noise characteristics of letterbox EPITV

1995 ◽  
Vol 41 (4) ◽  
pp. 113-120
Author(s):  
Tomoyuki Mishina ◽  
Makoto Okui ◽  
Fumio Okano
2021 ◽  
Vol 237 ◽  
pp. 109544
Author(s):  
Gustavo E. Coelho ◽  
Maria Graça Neves ◽  
António Pascoal ◽  
Álvaro Ribeiro ◽  
Peter Frigaard

2017 ◽  
Vol 125 (9) ◽  
pp. 097004 ◽  
Author(s):  
Maria Foraster ◽  
Ikenna C. Eze ◽  
Emmanuel Schaffner ◽  
Danielle Vienneau ◽  
Harris Héritier ◽  
...  

2011 ◽  
Vol 19 (3) ◽  
pp. 189
Author(s):  
Karsten Rodenacker ◽  
Klaus Hahn ◽  
Gerhard Winkler ◽  
Dorothea P Auer

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.


2021 ◽  
Vol 143 (6) ◽  
Author(s):  
Abdullah Y. Usmani ◽  
K. Muralidhar

Abstract Fluid loading within an intracranial aneurysm is difficult to measure but can be related to the shape of the flow passage. The outcome of excessive loading is a fatal hemorrhage, making it necessary for early diagnosis. However, arterial diseases are asymptomatic and clinical assessment is a challenge. A realistic approach to examining the severity of wall loading is from the morphology of the aneurysm itself. Accordingly, this study compares pulsatile flow (Reynolds number Re = 426, Womersley number Wo = 4.7) in three different intracranial aneurysm geometries. Specifically, the spatio-temporal movement of vortices is followed in high aspect ratio aneurysm models whose domes are inclined along with angles of 0, 45, and 90 deg relative to the plane of the parent artery. The study is based on finite volume simulation of unsteady three-dimensional flow while a limited set of particle image velocimetry experiments have been carried out. Within a pulsatile cycle, an increase in inclination (0–90 deg) is seen to shift the point of impingement from the distal end toward the aneurysmal apex. This change in flow pattern strengthens helicity, drifts vortex cores, enhances spatial displacement of the vortex, and generates skewed Dean's vortices on transverse planes. Patches of wall shear stress and wall pressure shift spatially from the distal end in models of low inclination (0–45 deg) and circumscribe the aneurysmal wall for an inclination angle of 90 deg. Accordingly, it is concluded that high angles of inclination increase rupture risks while lower inclinations are comparatively safe.


2011 ◽  
pp. 272-293
Author(s):  
Junmei Wang ◽  
Wynne Hsu ◽  
Mong Li Lee

Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behavior of objects in the three-dimensional space of time and spatial coordinates. Although the complex and intrinsic relationships among the spatio-temporal data limit the usefulness of conventional data mining techniques to discover the patterns in the spatio-temporal databases, they also lead to opportunities for mining new classes of patterns in spatio-temporal databases. This chapter provides a survey of the work done for mining patterns in spatial databases and temporal databases, and the preliminary work for mining patterns in spatio-temporal databases. We highlight the unique challenges of mining interesting patterns in spatio-temporal databases. We also describe two special types of spatio-temporal patterns: location-sensitive sequence patterns and geographical features for location-based service patterns.


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