Computational Spectral-Depth Imaging with a Compact System

Author(s):  
Mingde Yao ◽  
Zhiwei Xiong ◽  
Lizhi Wang ◽  
Dong Liu ◽  
Xuejin Chen
Keyword(s):  
2014 ◽  
Author(s):  
Karam Mohamed Hafez ◽  
Pradip Kumar Mukherjee ◽  
Mudavakkat Anandan ◽  
Aisha Al-Ghareeb ◽  
Wael Abdel Alim Zahran ◽  
...  
Keyword(s):  

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Clara Callenberg ◽  
Zheng Shi ◽  
Felix Heide ◽  
Matthias B. Hullin

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.


Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1592-1603 ◽  
Author(s):  
Yonghe Sun ◽  
Fuhao Qin ◽  
Steve Checkles ◽  
Jacques P. Leveille

A beam implementation is presented for efficient full‐volume 3-D prestack Kirchhoff depth migration of seismic data. Unlike conventional Kirchhoff migration in which the input seismic traces in time are migrated one trace at a time into the 3-D image volume for the earth’s subsurface, the beam migration processes a group of input traces (a supergather) together. The requirement for a supergather is that the source and receiver coordinates of the traces fall into two small surface patches. The patches are small enough that a single set of time maps pertaining to the centers of the patches can be used to migrate all the traces within the supergather by Taylor expansion or interpolation. The migration of a supergather consists of two major steps: stacking the traces into a τ-P beam volume, and mapping the beams into the image volume. Since the beam volume is much smaller than the image volume, the beam migration cost is roughly proportional to the number of input supergathers. The computational speedup of beam migration over conventional Kirchhoff migration is roughly proportional to [Formula: see text], the average number of traces per supergather, resulting a theoretical speedup up to two orders of magnitudes. The beam migration was successfully implemented and has been in production use for several years. A factor of 5–25 speedup has been achieved in our in‐house depth migrations. The implementation made 3-D prestack full‐volume depth imaging feasible in a parallel distributed environment.


First Break ◽  
2016 ◽  
Vol 34 (9) ◽  
Author(s):  
Marielle Ciotoli ◽  
Sophie Beaumont ◽  
Julien Oukili ◽  
Øystein Korsmo ◽  
Nicola O'Dowd ◽  
...  

Author(s):  
Peter Thoman ◽  
Alexander Hirsch ◽  
Markus Wippler ◽  
Robert Hranitzky

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