Efficient algorithm to find a jointly optimal time-frequency segmentation using time-varying filter banks

1995 ◽  
Author(s):  
Cormac Herley ◽  
Zixiang Xiong ◽  
Kannan Ramchandran
Keyword(s):  
Efficient Algorithm ◽  
Filter Banks ◽  
Optimal Time ◽  
Time Varying ◽  
Time Frequency

Optimizing Learning Convergence Speed and Converged Error for Precision Motion Control

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Douglas A. Bristow ◽  
Andrew G. Alleyne ◽  
Marina Tharayil
Keyword(s):  
Motion Control ◽  
Convergence Speed ◽  
Optimal Time ◽  
Time Varying ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Filter Bandwidth ◽  
Precision Motion Control ◽  
Time Frequency ◽  
Precision Motion

This brief paper considers iterative learning control (ILC) for precision motion control (PMC) applications. This work develops a methodology to design a low pass filter, called the Q-filter, that is used to limit the bandwidth of the ILC to prevent the propagation of high frequencies in the learning. A time-varying bandwidth Q-filter is considered because PMC reference trajectories can exhibit rapid changes in acceleration that may require high bandwidth for short periods of time. Time-frequency analysis of the initial error signal is used to generate a shape function for the bandwidth profile. Key parameters of the bandwidth profile are numerically optimized to obtain the best tradeoff in converged error and convergence speed. Simulation and experimental results for a permanent-magnet linear motor are included. Results show that the optimal time-varying Q-filter bandwidth provides faster convergence to lower error than the optimal time-invariant bandwidth.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

scholarly journals Time-varying filters and filter banks: some basic principles

1996 ◽  
Vol 44 (12) ◽  
pp. 2971-2987 ◽  
Author(s):  
See-May Phoong ◽  
P.P. Vaidyanathan
Keyword(s):  
Filter Banks ◽  
Time Varying ◽  
Basic Principles
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