New localization strategy for mobile robot transportation in life science automation using StarGazer sensor, time series modeling and Kalman filter processing

2015 ◽  
Author(s):  
Hui Liu ◽  
Norbert Stoll ◽  
Steffen Junginger ◽  
Kerstin Thurow
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Mobile Robot ◽  
Life Science ◽  
Time Series Modeling ◽  
Localization Strategy

Square-Root Cubature Kalman Filter and Its Application to SLAM of an Mobile Robot

ROBOT ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 186 ◽  
Author(s):  
Yifei KANG ◽  
Yongduan SONG ◽  
Yu SONG ◽  
Deli YAN ◽  
Danyong LI
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Square Root ◽  
Cubature Kalman Filter

Meta-Analysis and Time Series Modeling Allow a Systematic Review of Primary HIV-1 Drug-Resistant Prevalence in Latin America and Caribbean

2015 ◽  
Vol 13 (2) ◽  
pp. 125-142 ◽  
Author(s):  
Antonio Coelho ◽  
Ronald Moura ◽  
Ronaldo Silva ◽  
Anselmo Kamada ◽  
Rafael Guimaraes ◽  
...  
Keyword(s):  
Systematic Review ◽  
Time Series ◽  
Latin America ◽  
Meta Analysis ◽  
Drug Resistant ◽  
Time Series Modeling ◽  
Latin America And Caribbean ◽  
Hiv 1

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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