Evaluation of Neural Networks for Estimating Whole Velocity Fields

Estimating the continuum of quasars using the articial neural networks

A lot of absorption lines are in the bluewards of Lyα emission line of quasar which is well-known as Lyαforest. Most of absorption lines in this forest belong to the Lyα absorption of the neutral hydrogen in the inter-galactic medium (IGM). For high redshift quasars and in the continuum with low and medium resolution, there are no many regions without absorption, so that, the quasar continuum i...

Estimating Rainfall with Neural Networks and Conditional Random Fields

v To all those who have pushed me, pulled me, taught me, and challenged me. vi " Though the rain comes in torrents and the floodwaters rise and the winds beat against that house, it won't collapse because it is built on bedrock. " − Jesus Christ, Matthew 7:25 (New Living Translation) vii ACKNOWLEDGEMENTS First, I want to note that I worked with Dr. Michael Marder and Dr. Pradeep Raviku-mar at U...

Estimating Rainfall with Neural Networks and Conditional Random Fields

A PIV Algorithm for Estimating Time-Averaged Velocity Fields

A PIV algorithm is presented for estimating time-averaged or phase-averaged velocity fields. The algorithm can be applied to situations where signal strength is not sufficient for standard cross correlation techniques, such as a low number of particle images in an interrogation spot, or poor image quality. The algorithm can also be used to increase the spatial resolution of measurements by allo...

Estimating subsurface parameter fields for seismic migration: velocity model building

What migration sets-out to do Deriving parameters for migration: 1D versus 3D assumptions Inversion Estimating image uncertainty Resolution scale length Generic model building loop for ray-based tomography Parametric versus non-parametric autopicking Wide azimuth and multi-azimuth data Anisotropic Model Building Anisotropic pre-stack depth migration in the absence of well control Resolving near...