Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

2016 ◽  
Author(s):  
Kyeong-sang Lee ◽  
Sungwon Choi ◽  
Minji Seo ◽  
Chang suk Lee ◽  
Noh-hun Seong ◽  
...  
Keyword(s):  
Snow Cover ◽  
Dynamic Time Warping ◽  
Solar Spectrum ◽  
Detection Algorithm ◽  
Time Warping ◽  
Dynamic Time

Collision Detection on Industrial Robots in Repetitive Tasks Using Modified Dynamic Time Warping

Robotica ◽  
2019 ◽  
Vol 38 (10) ◽  
pp. 1717-1736
Author(s):  
Zaviša Gordić ◽  
Kosta Jovanović
Keyword(s):  
Collision Detection ◽  
Dynamic Time Warping ◽  
Reference Signal ◽  
Detection Algorithm ◽  
Industrial Robots ◽  
Absolute Difference ◽  
Time Warping ◽  
Online Application ◽  
Dynamic Time ◽  
Matched Samples

SUMMARYThis paper presents a non-model-based collision detection algorithm for robots without external sensors and with closed control architecture. A reference signal of repetitive motion is recorded from the robot operation. To detect collisions, the reference is compared with measurements from the robot. One of the key contributions is a novel approach to optimal matching of compared signals, which is ensured by the newly developed modified Dynamic Time Warping (mDTW) method presented in this paper. One of the main improvements of the mDTW is that it enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. In addition to mDTW, two complementary decision rules are developed to identify collisions. The first rule, based on the absolute difference between compared matched samples, uses statistically determined thresholds to perform rapid detection of unambiguous collisions. The second rule is based on Eigen values of the covariance matrix of matched samples, and it employs its higher sensitivity to detect collisions with lower intensity. Results from experimental validation of the proposed collision algorithm on two industrial robots are shown.

Daily estimates of Landsat fractional snow cover driven by MODIS and dynamic time-warping

2018 ◽  
Vol 216 ◽  
pp. 635-646 ◽  
Author(s):  
Ethan E. Berman ◽  
Douglas K. Bolton ◽  
Nicholas C. Coops ◽  
Zoltan K. Mityok ◽  
Gordon B. Stenhouse ◽  
...  
Keyword(s):  
Snow Cover ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Fractional Snow Cover ◽  
Dynamic Time

scholarly journals Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 651 ◽  
Author(s):  
Jesus D. Ceron ◽  
Christine F. Martindale ◽  
Diego M. López ◽  
Felix Kluge ◽  
Bjoern M. Eskofier
Keyword(s):  
Dynamic Time Warping ◽  
Dead Reckoning ◽  
Detection Algorithm ◽  
Time Warping ◽  
Trajectory Reconstruction ◽  
Position Errors ◽  
Controlled Environments ◽  
Dynamic Time ◽  
Pedestrian Dead Reckoning ◽  
New Algorithms

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing.

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