scholarly journals Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2282 ◽  
Author(s):  
David Lee ◽  
Sang-Hoon Park ◽  
Sang-Goog Lee
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Motor Imagery ◽  
Gaussian Mixture ◽  
Brain Computer Interfaces ◽  
Feature Vectors ◽  
Computer Interfaces ◽  
Combined Feature ◽  
And Training

scholarly journals POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

2016 ◽  
Vol XLI-B7 ◽  
pp. 341-345
Author(s):  
S. Rouabah ◽  
M. Ouarzeddine ◽  
B. Azmedroub
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Urban Areas ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
Polarimetric Sar ◽  
Data Set ◽  
Feature Vectors ◽  
Sar Data

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.

scholarly journals GMM-UBM Based Modeling for Language Identification using New Feature Vectors

2020 ◽  
Vol 9 (4) ◽  
pp. 3034-3039
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Language Identification ◽  
Training Data ◽  
Background Model ◽  
Indian Languages ◽  
Feature Vectors ◽  
Universal Background Model ◽  
New Feature

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.

Vehicle Appearance Model for Recognition System Considering the Change of Imaging Condition

2009 ◽  
Vol 13 (4) ◽  
pp. 463-469 ◽  
Author(s):  
Keiji Kuwabara ◽  
◽  
Yoshikazu Yano ◽  
Shigeru Okuma ◽  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Recognition System ◽  
The Other ◽  
Appearance Model ◽  
Imaging Condition ◽  
Successive Change ◽  
Imaging Conditions ◽  
And Training

We have proposed a technique to recognize a vehicle. In this technique, Gaussian Mixture Model (GMM) is adopted as a classifier. Vehicle appearance changed by imaging conditions such as time, weather and so on, and GMM parameters are also changed by imaging conditions. To recognize vehicle accurately, we have prepared some GMM tuned with the imaging conditions. On the other hand, it is impossible to prepare GMM because imaging condition changes successively. In this paper, we propose a method for estimating GMM and for training GMM parameters which reflect the successive change of imaging condition. Experimental results show that GMM parameters are estimated accurately and training of GMM are speeded up by proposed method.

Health Assessment of Hydraulic Servo System Based on Fractal Analysis and Gaussian Mixture Model

2015 ◽  
Vol 764-765 ◽  
pp. 703-707
Author(s):  
Xuan Wang ◽  
Hong Mei Liu ◽  
Chen Lu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fractal Analysis ◽  
Health Assessment ◽  
Servo System ◽  
Gaussian Mixture ◽  
Servo Systems ◽  
Feature Vectors ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

A hydraulic servo system is a typical feedback control system. Health assessment of a hydraulic servo system is usually difficult to realize when traditional methods based on sensor signals are utilized. An approach for health assessment of hydraulic servo systems based on multi-fractal analysis and Gaussian mixture model (GMM) is proposed in this study. A GRNN neural network is employed to establish a fault observer for the hydraulic servo system. The observer is utilized to simulate the system output under normal state. The residue is then generated by subtracting the estimated output from the actual output. The residue’s feature is extracted by fractal analysis. After the feature extraction, the overlap between the current feature vectors and the normal feature vectors is obtained by applying GMM. The confidence value (CV) can be obtained in advance; this value is employed to characterize the health degree of the current state and consequently implement the health assessment of the hydraulic servo system. Lastly, two common types of fault, namely, burst and gradual, are applied to validate the effectiveness of the proposed method.

scholarly journals POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

2016 ◽  
Vol XLI-B7 ◽  
pp. 341-345
Author(s):  
S. Rouabah ◽  
M. Ouarzeddine ◽  
B. Azmedroub
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Urban Areas ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
Polarimetric Sar ◽  
Data Set ◽  
Feature Vectors ◽  
Sar Data

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.

scholarly journals A Tandem Feature Extraction Approach for Arrhythmia Identification

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 976
Author(s):  
Javier Tejedor ◽  
David G. Marquez ◽  
Constantino A. Garcia ◽  
Abraham Otero
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Feature Vector ◽  
Gaussian Mixture ◽  
Hermite Functions ◽  
Identification System ◽  
Feature Vectors ◽  
Original Feature

Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart disease, especially for the identification of arrhythmias. With the advances in electronic technology, there are nowadays sensors that enable the recording of the ECG during the daily life of the patient and its wireless transmission to healthcare facilities. This type of information has a great potential to detect cardiac diseases in their early stages and to permit early interventions before the patient’s health deteriorates. However, to usefully exploit the large volume of information obtained from ambulatory ECG, pattern recognition techniques that are capable of automatically analyzing it are required. Tandem feature extraction techniques have proven to be useful for the processing of physiological parameters such as the electroencephalogram (EEG) and speech. However, to the best of our knowledge, they have never been applied to the ECG. In this paper, the utility of tandem feature extraction for the identification of arrhythmias is studied. The coefficients of a regression using Hermite functions are used to create a feature vector that represents the heartbeat. A multiple-layer perceptron (MLP) is trained using these features and its posterior probability outputs are used to extend the original feature vector. Finally, a Gaussian mixture model (GMM) is trained on the extended feature vectors, which is then used in a GMM-based arrhythmia identification system. This approach has been validated using the MIT-BIH Arrhythmia database. The accuracy of the Gaussian mixture model increased by 15.8% when applied over the extended feature vectors, compared to its application over the original feature vectors, showing the potential of tandem feature extraction for ECG analysis and arrhythmia identification.

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