Detection and classification of a cylindrical target partially or completely buried in thin sand/water mixture by time‐frequency representation

2008 ◽  
Vol 123 (5) ◽  
pp. 3599-3599 ◽  
Author(s):  
Gerard Maze ◽  
Dominique Decultot ◽  
Katia Cacheleux ◽  
Romain Liétard
Keyword(s):  
Water Mixture ◽  
Time Frequency ◽  
Frequency Representation ◽  
Cylindrical Target

scholarly journals Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO BasedK-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Suraj ◽  
Purnendu Tiwari ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Motor Imagery ◽  
Execution Time ◽  
Feature Vector ◽  
Time Frequency ◽  
Real World Application ◽  
Frequency Representation ◽  
High Level ◽  
Electroencephalogram Eeg ◽  
The Brain

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO basedK-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO basedK-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) basedK-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

scholarly journals Voltage variations identification using gabor transform and rule-based classification method

2020 ◽  
Vol 10 (1) ◽  
pp. 681
Author(s):  
Weihown Tee ◽  
M. R. Yusoff ◽  
M. Faizal Yaakub ◽  
A. R. Abdullah
Keyword(s):  
Mathematical Model ◽  
Classification Method ◽  
Gabor Transform ◽  
Voltage Sag ◽  
Rule Based ◽  
Time Frequency ◽  
Voltage Variation ◽  
Frequency Representation ◽  
The Mathematical Model

This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method. The technique was tested by using mathematical model of Power Quality (PQ) disturbances based on the IEEE Std 519-2009. The PQ disturbances focused were the voltage variations, which included voltage sag, swell and interruption. A total of 80 signals were simulated from the mathematical model in MATLAB and used in this study. The signals were analyzed by using Gabor Transform and the signal pattern, time-frequency representation (TFR) and root-mean-square voltage graph were presented in this paper. The features of the analysis were extracted, and rules were implemented in rule-based classification to identify and classify the voltage variation accordingly. The results showed that this method is easy to be used and has good accuracy in classifying the voltage variation.

Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG

2017 ◽  
Vol 27 (04) ◽  
pp. 1750005 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Qing Cai ◽  
Yu-Xuan Yang ◽  
Na Dong ◽  
Shan-Shan Zhang
Keyword(s):  
Epileptic Seizure ◽  
Healthy Subjects ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Eeg Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Brain States ◽  
Optimal Kernel

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

Classification of a cylindrical target buried in a thin sand-water mixture using acoustic spectra

2010 ◽  
Vol 127 (3) ◽  
pp. 1328-1334 ◽  
Author(s):  
Dominique Décultot ◽  
Romain Liétard ◽  
Gérard Maze
Keyword(s):  
Water Mixture ◽  
Cylindrical Target ◽  
Acoustic Spectra
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