scholarly journals Development of a real-time wave field reconstruction TEM system (II): correction of coma aberration and 3-fold astigmatism, and real-time correction of 2-fold astigmatism

Microscopy ◽  
2018 ◽  
Vol 67 (1) ◽  
pp. 37-45
Author(s):  
Takahiro Tamura ◽  
Yoshihide Kimura ◽  
Yoshizo Takai
Keyword(s):  
Wave Field ◽  
Real Time ◽  
Field Reconstruction ◽  
Time Correction ◽  
Coma Aberration

Development of a real-time wave field reconstruction TEM system (I): incorporation of an auto focus tracking system

2017 ◽  
Vol 66 (3) ◽  
pp. 172-181 ◽  
Author(s):  
Takahiro Tamura ◽  
Yoshihide Kimura ◽  
Yoshizo Takai
Keyword(s):  
Wave Field ◽  
Real Time ◽  
Tracking System ◽  
Field Reconstruction ◽  
Auto Focus

Real-Time Sound Field Transmission System by Using Wave Field Reconstruction Filter and Its Evaluation

2014 ◽  
Vol E97.A (9) ◽  
pp. 1840-1848 ◽  
Author(s):  
Shoichi KOYAMA ◽  
Ken'ichi FURUYA ◽  
Hisashi UEMATSU ◽  
Yusuke HIWASAKI ◽  
Yoichi HANEDA
Keyword(s):  
Wave Field ◽  
Real Time ◽  
Sound Field ◽  
Transmission System ◽  
Field Reconstruction ◽  
Reconstruction Filter

High speed interferometric device for real-time correction of aero-optic effects

1995 ◽  
Author(s):  
Rod Clark ◽  
John Karpinsky ◽  
Gregg Borek ◽  
Eric Johnson
Keyword(s):  
Real Time ◽  
High Speed ◽  
Time Correction

Coupled Hydraulic and Kalman Filter Model for Real-Time Correction of Flood Forecast in the Three Gorges Interzone of Yangtze River, China

2013 ◽  
Vol 18 (11) ◽  
pp. 1416-1425 ◽  
Author(s):  
Xiao-Ling Wu ◽  
Xiao-Hua Xiang ◽  
Chuan-Hai Wang ◽  
Xi Chen ◽  
Chong-Yu Xu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Yangtze River ◽  
Flood Forecast ◽  
Three Gorges ◽  
Filter Model ◽  
The Three Gorges ◽  
Time Correction

scholarly journals Real-time three dimensional wind field reconstruction from nacelle LiDAR measurements

2018 ◽  
Vol 1037 ◽  
pp. 032037
Author(s):  
F. Guillemin ◽  
H.-N. Nguyen ◽  
G. Sabiron ◽  
D. Di Domenico ◽  
M. Boquet
Keyword(s):  
Real Time ◽  
Wind Field ◽  
Three Dimensional ◽  
Field Reconstruction ◽  
Lidar Measurements

Electronic barometric altimeter in real time correction

2008 ◽  
Author(s):  
C. E. Lin ◽  
W. C. Huang ◽  
C. W. Hsu ◽  
C. C. Li
Keyword(s):  
Real Time ◽  
Time Correction

scholarly journals Method of automatic correction of neutron monitor data for precipitation in the form of snow in real time

2021 ◽  
Vol 7 (3) ◽  
pp. 120-126
Author(s):  
Valery Yanchukovsky ◽  
Vasiliy Kuz'menko
Keyword(s):  
Snow Cover ◽  
Real Time ◽  
Neutron Monitor ◽  
Count Rate ◽  
Continuous Monitoring ◽  
Laser Rangefinder ◽  
Flux Intensity ◽  
Time Correction ◽  
Neutron Monitor Data ◽  
Monitor Data

We have carried out an experimental study of the influence of precipitation in the form of snow on measurements of the neutron flux intensity near Earth's surface. We have examined the state of the snow cover and its density, and found out that the density depends on the depth of the snow cover. Using the experimental results, we estimate the neutron absorption path in the snow. Changes in snow cover by 10–12 cm at a depth of 80 cm are shown to cause variations in the monitor count rate with an amplitude of 0.9 %. At the snow depth of 80 cm, the neutron monitor count rate decreases by about 8 %. The observed variations should be attributed to the meteorological effects of cosmic rays. The absorption coefficient of neutrons in the snow was also found from the correlation between the count rate of the neutron monitor and the amount of snow above the detector. We propose a real-time correction of the neutron monitor data for precipitation in the form of snow. For this purpose, we implement continuous monitoring of the amount of snow cover. The monitoring is provided by a snow meter made using a laser rangefinder module. We discuss the results obtained.

Leveraging coherent wave field analysis and deep learning in fiber-optic seismology

2021 ◽  
Author(s):  
Benjamin Schwarz ◽  
Korbinian Sager ◽  
Philippe Jousset ◽  
Gilda Currenti ◽  
Charlotte Krawczyk ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Field ◽  
Real Time ◽  
Learning Strategies ◽  
Data Storage ◽  
Large Scale ◽  
Fiber Optic ◽  
Positive Impact ◽  
Field Analysis ◽  
Coherent Wave

<p><span>Fiber-optic cables form an integral part of modern telecommunications infrastructure and are ubiquitous in particular in regions where dedicated seismic instrumentation is traditionally sparse or lacking entirely. Fiber-optic seismology promises to enable affordable and time-extended observations of earth and environmental processes at an unprecedented temporal and spatial resolution. The method’s unique potential for combined large-N and large-T observations implies intriguing opportunities but also significant challenges in terms of data storage, data handling and computation.</span></p><p><span>Our goal is to enable real-time data enhancement, rapid signal detection and wave field characterization without the need for time-demanding user interaction. We therefore combine coherent wave field analysis, an optics-inspired processing framework developed in controlled-source seismology, with state-of-the-art deep convolutional neural network (CNN) architectures commonly used in visual perception. While conventional deep learning strategies have to rely on manually labeled or purely synthetic training datasets, coherent wave field analysis labels field data based on physical principles and enables large-scale and purely data-driven training of the CNN models. The shear amount of data already recorded in various settings makes artificial data generation by numerical modeling superfluous – a task that is often constrained by incomplete knowledge of the embedding medium and an insufficient description of processes at or close to the surface, which are challenging to capture in integrated simulations.</span></p><p><span>Applications to extensive field datasets acquired with dark-fiber infrastructure at a geothermal field in SW Iceland and in a town at the flank of Mt Etna, Italy, reveal that the suggested framework generalizes well across different observational scales and environments, and sheds new light on the origin of a broad range of physically distinct wave fields that can be sensed with fiber-optic technology. Owing to the real-time applicability with affordable computing infrastructure, our analysis lends itself well to rapid on-the-fly data enhancement, wave field separation and compression strategies, thereby promising to have a positive impact on the full processing chain currently in use in fiber-optic seismology.</span></p>

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