Comparison of Synchronous Detection and I. Cosϕ Shunt Active Filtering Algorithms

2006 ◽  
Author(s):  
G. Bhuvaneswari ◽  
M.G. Nair ◽  
S. Kumar Reddy
Keyword(s):  
Synchronous Detection ◽  
Filtering Algorithms ◽  
Active Filtering

System Identification and Echo Canceller with Adaptive Filtering Algorithms

2015 ◽  
Vol 3 (3) ◽  
pp. 30-34 ◽  
Author(s):  
B. Anitha ◽  
◽  
Srinivas Bachu ◽  
C. Sailaja ◽  
◽  
...  
Keyword(s):  
System Identification ◽  
Adaptive Filtering ◽  
Echo Canceller ◽  
Filtering Algorithms

Double Samples Synchronous Detection Sensor based on Up-Core Photonic Crystal Fiber

Optik ◽  
2020 ◽  
Vol 224 ◽  
pp. 165522
Author(s):  
Pibin Bing ◽  
Guifang Wu ◽  
Jialei Sui ◽  
Hongtao Zhang ◽  
Lian Tan ◽  
...  
Keyword(s):  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
Synchronous Detection ◽  
Crystal Fiber

Identification of Grid Impedance for Purposes of Voltage Feedback Active Filtering

2004 ◽  
Vol 2 (1) ◽  
pp. 6-10 ◽  
Author(s):  
A. Tarkiainen ◽  
R. Pollanen ◽  
M. Niemela ◽  
J. Pyrhonen
Keyword(s):  
Grid Impedance ◽  
Active Filtering ◽  
Voltage Feedback

Experimental Analysis of Filtering Algorithms for IMU-Based Applications Under Vibrations

2021 ◽  
Vol 70 ◽  
pp. 1-10
Author(s):  
Domenico Capriglione ◽  
Marco Carratu ◽  
Marcantonio Catelani ◽  
Lorenzo Ciani ◽  
Gabriele Patrizi ◽  
...  
Keyword(s):  
Experimental Analysis ◽  
Filtering Algorithms

scholarly journals How to train your differentiable filter

2021 ◽  
Author(s):  
Alina Kloss ◽  
Georg Martius ◽  
Jeannette Bohg
Keyword(s):  
Bayesian Filtering ◽  
Learning Approaches ◽  
Filtering Algorithms ◽  
Recursive Filtering ◽  
Process Dynamics ◽  
Noise Characteristics ◽  
Complex Models ◽  
End To End ◽  
Robotic Applications ◽  
And Training

AbstractIn many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

scholarly journals A Family of Shrinkage Adaptive-Filtering Algorithms

2013 ◽  
Vol 61 (7) ◽  
pp. 1689-1697 ◽  
Author(s):  
Zulfiquar Ali Bhotto ◽  
Andreas Antoniou
Keyword(s):  
Adaptive Filtering ◽  
Filtering Algorithms
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