scholarly journals High-fidelity indoor MIMO radio access for 5G and beyond based on legacy multimode fiber and real-time analog-to-digital-compression

2020 ◽  
Author(s):  
Paikun Zhu ◽  
Yuki Yoshida ◽  
Atsushi Kanno ◽  
Naokatsu Yamamoto ◽  
Ken-ichi Kitayama
Keyword(s):  
Real Time ◽  
High Fidelity ◽  
Multimode Fiber ◽  
Analog To Digital ◽  
Radio Access ◽  
Digital Compression

Robust and high fidelity real-time hybrid substructuring

2021 ◽  
Vol 157 ◽  
pp. 107720
Author(s):  
Christina Insam ◽  
Arian Kist ◽  
Henri Schwalm ◽  
Daniel J. Rixen
Keyword(s):  
Real Time ◽  
High Fidelity

Introducing MantisBot: Hexapod robot controlled by a high-fidelity, real-time neural simulation

2015 ◽  
Author(s):  
Nicholas S. Szczecinski ◽  
David M. Chrzanowski ◽  
David W. Cofer ◽  
Andrea S. Terrasi ◽  
David R. Moore ◽  
...  
Keyword(s):  
Real Time ◽  
High Fidelity ◽  
Hexapod Robot ◽  
Neural Simulation

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Effectiveness of High Fidelity Video-Assisted Real-Time Simulation: A Comparison of Three Training Methods for Acute Pediatric Emergencies

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Ester H. A. J. Coolen ◽  
Jos M. T. Draaisma ◽  
Marije Hogeveen ◽  
Tim A. J. Antonius ◽  
Charlotte M. L. Lommen ◽  
...  
Keyword(s):  
Real Time ◽  
High Fidelity ◽  
Training Methods ◽  
Real Time Simulation ◽  
Pediatric Emergencies ◽  
Video Assisted ◽  
Time Simulation
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