Discrete-time Frequency-Locked-Loop filters for parameters estimation of sinusoidal signals

2013 ◽  
Author(s):  
Francesco Tedesco ◽  
Alessandro Casavola ◽  
Giuseppe Fedele
Keyword(s):  
Discrete Time ◽  
Parameters Estimation ◽  
Time Frequency ◽  
Sinusoidal Signals

Parameter estimation of discrete-time sinusoidal signals: A nonlinear control approach

Automatica ◽  
2019 ◽  
Vol 109 ◽  
pp. 108510
Author(s):  
Teng Jiang ◽  
Dabo Xu ◽  
Tianshi Chen ◽  
Andong Sheng
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Control ◽  
Discrete Time ◽  
Control Approach ◽  
Sinusoidal Signals

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

A subspace algorithm for the identification of discrete time frequency domain power spectra

Automatica ◽  
1997 ◽  
Vol 33 (12) ◽  
pp. 2147-2157 ◽  
Author(s):  
Peter Van Overschee ◽  
Bart De Moor ◽  
Wouter Dehandschutter ◽  
Jan Swevers
Keyword(s):  
Frequency Domain ◽  
Discrete Time ◽  
Power Spectra ◽  
Time Frequency ◽  
Subspace Algorithm

scholarly journals Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Asrul Adam ◽  
Mohd Ibrahim Shapiai ◽  
Mohd Zaidi Mohd Tumari ◽  
Mohd Saberi Mohamad ◽  
Marizan Mubin
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Domain Analysis ◽  
Peak Detection ◽  
Parameters Estimation ◽  
Eeg Signals ◽  
Classification Rate ◽  
Swarm Optimization ◽  
Time Frequency

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

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