scholarly journals Parallel complexity analysis with temporal session types

2018 ◽  
Vol 2 (ICFP) ◽  
pp. 1-30 ◽  
Author(s):  
Ankush Das ◽  
Jan Hoffmann ◽  
Frank Pfenning
Keyword(s):  
Complexity Analysis ◽  
Parallel Complexity ◽  
Session Types

The Complexity Analysis Tool

1988 ◽  
Author(s):  
Paul E. Janusz
Keyword(s):  
Complexity Analysis ◽  
Analysis Tool

A Parallel Complexity Model for Functional Languages.

1994 ◽  
Author(s):  
Guy Belloch ◽  
John Greiner
Keyword(s):  
Functional Languages ◽  
Parallel Complexity

Connectivity and Competition in Airline Networks: A Complexity Analysis of Lufthansa's Network

2011 ◽  
Author(s):  
Aura Reggiani ◽  
Peter Nijkamp ◽  
Alessandro Cento
Keyword(s):  
Complexity Analysis ◽  
Airline Networks

scholarly journals Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm

2020 ◽  
Author(s):  
Sergio Vitale ◽  
Giampaolo Ferraioli ◽  
Vito Pascazio
Keyword(s):  
Complexity Analysis ◽  
Edge Preserving

Centered error entropy Kalman filter with application to satellite attitude determination

2021 ◽  
pp. 014233122110198
Author(s):  
Baojian Yang ◽  
Lu Cao ◽  
Dechao Ran ◽  
Bing Xiao
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Attitude Determination ◽  
Complexity Analysis ◽  
Iteration Algorithm ◽  
Robust Algorithms ◽  
Convergence Conditions ◽  
Satellite Attitude ◽  
Heavy Tailed ◽  
Non Gaussian

Due to unavoidable factors, heavy-tailed noise appears in satellite attitude estimation. Traditional Kalman filter is prone to performance degradation and even filtering divergence when facing non-Gaussian noise. The existing robust algorithms have limited accuracy. To improve the attitude determination accuracy under non-Gaussian noise, we use the centered error entropy (CEE) criterion to derive a new filter named centered error entropy Kalman filter (CEEKF). CEEKF is formed by maximizing the CEE cost function. In the CEEKF algorithm, the prior state values are transmitted the same as the classical Kalman filter, and the posterior states are calculated by the fixed-point iteration method. The CEE EKF (CEE-EKF) algorithm is also derived to improve filtering accuracy in the case of the nonlinear system. We also give the convergence conditions of the iteration algorithm and the computational complexity analysis of CEEKF. The results of the two simulation examples validate the robustness of the algorithm we presented.

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