New detector and data processing procedure to measure velocity angular distribution function of magnetized relativistic electrons

2019 ◽  
Vol 942 ◽  
pp. 162349
Author(s):  
A.V. Arzhannikov ◽  
M.A. Makarov ◽  
D.A. Samtsov ◽  
S.L. Sinitsky ◽  
V.D. Stepanov
Keyword(s):  
Distribution Function ◽  
Angular Distribution ◽  
Data Processing ◽  
Relativistic Electrons ◽  
Angular Distribution Function ◽  
Processing Procedure ◽  
Data Processing Procedure

scholarly journals A Method to Correct the Thermal Dome Effect of Pyranometers in Selected Historical Solar Irradiance Measurements

2007 ◽  
Vol 24 (3) ◽  
pp. 529-536 ◽  
Author(s):  
Qiang Ji
Keyword(s):  
Data Processing ◽  
Thermal Effect ◽  
Case Studies ◽  
Solar Irradiance ◽  
Research Initiative ◽  
Processing Procedure ◽  
Field Campaigns ◽  
Sky Conditions ◽  
Data Processing Procedure

Abstract In using pyranometers to measure solar irradiance, it is important to know the magnitudes and the consequences of the thermal effect, which is introduced by the glass domes of the instruments. Historically, the thermal dome effect was not monitored on a regular basis. Case studies show that, due to the thermal dome effect, the output of the pyranometers altered from less than 5 W m−2 in the nighttime to over 20 W m−2 around noontime during the Aerosol Recirculation and Rainfall Experiment (ARREX) in 1999 and the Southern African Fire–Atmosphere Research Initiative (SAFARI) in 2000 field campaigns, depending on sky conditions. A calibration and data processing procedure with the thermal dome effect incorporated has been tested to resolve the issue. It is demonstrated that the intrinsic calibration constants of the pyranometers can be obtained if two pyranometers are used side by side, and the thermal dome effect may be inferred whenever a pyranometer and a pyrgeometer are collocated.

scholarly journals RECONSTRUCTION OF INDOOR MODELS USING POINT CLOUDS GENERATED FROM SINGLE-LENS REFLEX CAMERAS AND DEPTH IMAGES

2015 ◽  
Vol XL-4/W5 ◽  
pp. 99-102
Author(s):  
F. Tsai ◽  
T.-S. Wu ◽  
I.-C. Lee ◽  
H. Chang ◽  
A. Y. S. Su
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Three Dimensional ◽  
Point Clouds ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Indoor Environments ◽  
Single Lens ◽  
Processing Procedure ◽  
Data Processing Procedure

This paper presents a data acquisition system consisting of multiple RGB-D sensors and digital single-lens reflex (DSLR) cameras. A systematic data processing procedure for integrating these two kinds of devices to generate three-dimensional point clouds of indoor environments is also developed and described. In the developed system, DSLR cameras are used to bridge the Kinects and provide a more accurate ray intersection condition, which takes advantage of the higher resolution and image quality of the DSLR cameras. Structure from Motion (SFM) reconstruction is used to link and merge multiple Kinect point clouds and dense point clouds (from DSLR color images) to generate initial integrated point clouds. Then, bundle adjustment is used to resolve the exterior orientation (EO) of all images. Those exterior orientations are used as the initial values to combine these point clouds at each frame into the same coordinate system using Helmert (seven-parameter) transformation. Experimental results demonstrate that the design of the data acquisition system and the data processing procedure can generate dense and fully colored point clouds of indoor environments successfully even in featureless areas. The accuracy of the generated point clouds were evaluated by comparing the widths and heights of identified objects as well as coordinates of pre-set independent check points against in situ measurements. Based on the generated point clouds, complete and accurate three-dimensional models of indoor environments can be constructed effectively.

3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data

2018 ◽  
Vol 2672 (45) ◽  
pp. 14-22 ◽  
Author(s):  
Yuan Sun ◽  
Hao Xu ◽  
Jianqing Wu ◽  
Jianying Zheng ◽  
Kurt M. Dietrich
Keyword(s):  
High Resolution ◽  
Data Processing ◽  
Extraction Procedure ◽  
Estimation Method ◽  
Connected Vehicle ◽  
Vehicle Trajectories ◽  
Vehicle Data ◽  
Stop Sign ◽  
Processing Procedure ◽  
Data Processing Procedure

High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.

Sign in / Sign up

Export Citation Format

Share Document