Phước Trung Nguyễn - Academia.edu (original) (raw)
Papers by Phước Trung Nguyễn
The Journal of Korean Institute of Communications and Information Sciences, 2019
Http Www Theses Fr, Feb 2, 2012
s 0 f (r)dr. Grâce à ces résultats, on donne une nouvelle construction de la trace initiale et qu... more s 0 f (r)dr. Grâce à ces résultats, on donne une nouvelle construction de la trace initiale et quelques résultats d'unicité et de non-unicité de solutions dont la donnée initiale n'est pas bornée. Dans la troisième partie, on élargit le cadre de nos investigations et généralise les résultats obtenus dans la deuxième partie au cas où l'opérateur est non-linéaire. En particulier, on s'intéresse à des propriétés qualitatives de solutions positives de l'équation ∂ t u − ∆ p u + f (u) = 0 dans R N × (0, ∞) où p > 1, ∆ p u = div(|∇u| p−2 ∇u) et f est une v RÉSUMÉ fonction continue, croissante, positive et satisfaisant f (0) = 0 = f −1 (0). Si p > 2N N +1 , on fournit une condition suffisante portant sur f pour l'existence et l'unicité des solutions fondamentales de données initiales kδ 0 et on étudie la limite, lorsque k → ∞, qui dépend du fait que f −1 et F −1/p soient intégrables à l'infini ou pas, où F (s) = s 0 f (r)dr. On donne aussi de nouveaux résultats de non-unicité de solutions avec donnée initiale non bornée. Si p ≥ 2, on prouve que toute solution positive admet une trace initiale dans la classe de mesures de Borel régulières positives. Finalement on applique les résultats ci-dessus au cas modèle f (u) = u α ln β (u + 1) avec α > 0 et β > 0. Mots clés : équations elliptiques quasilinéaires, singularités isolées, mesures de Radon, mesures de Borel, capacités de Bessel, trace au bord, singularités éliminables, absorption faiblement sur-linéaire, trace initiale, condition de Keller-Osserman, équations de la chaleur dégénérées. vi Boundary trace of solutions to elliptic Hamilton-Jacobi equations and initial trace of solutions to heat equations with superlinear absorption
La presente invention concerne un procede de limitation du courant en sortie d'un variateur d... more La presente invention concerne un procede de limitation du courant en sortie d'un variateur de vitesse (V) pour moteur electrique asynchrone triphase (M), ledit variateur (V) fonctionnant selon une loi de commande (LC) de type U/F pure. Ledit procede se caracterise en ce qu'il consiste a calculer a l'aide d'une fonction de limitation une valeur de correction a la frequence du stator (Wstat) puis a lui appliquer, de maniere a obtenir, dans la loi de commande en tension (LC) de type U/F, une frequence du stator corrigee (Wstatc). Selon la loi de commande en tension de type U/F, le procede consiste ensuite a partir de cette frequence du stator corrigee (Wstatc) pour obtenir la tension (Vqref) de commande appliquee au moteur. L'invention concerne egalement un systeme de limitation du courant pouvant mettre en oeuvre ledit procede.
The main goal of safe aquaculture is responsible and sustainable production which is safe for the... more The main goal of safe aquaculture is responsible and sustainable production which is safe for the consumer and maintains environmental integrity. The purpose of a safe aquaculture zone is to create a cluster of farms within a defined boundary where biosecurity and safe aquaculture practices are undertaken
Lecture Notes in Computer Science, 2010
This paper presents an automatic speech-based classification scheme to classify speaker character... more This paper presents an automatic speech-based classification scheme to classify speaker characteristics. In the training phase, speech data are grouped into speaker groups according to speakers’ gender, age and accent. Voice features are then extracted to feature vectors which are used to train speaker characteristic models with different techniques which are Vector Quantization, Gaussian Mixture Model and Support Vector Machine.
2011 5th International IEEE/EMBS Conference on Neural Engineering, 2011
In EEG-based classification problem, most of currently used features are univariate and extracted... more In EEG-based classification problem, most of currently used features are univariate and extracted from single channels. However EEG signals recorded from multiple channels for a brain activity are correlated, features extracted from the EEG signals should reflect relationships among those channels. For this reason, we propose and apply a bivariate feature called Combined Short-Window BiVariate AutoRegressive model (CSWBVAR) for EEG classification problems. Given a pair of channels, we firstly divide each of them in to overlapping segments or short windows, and then estimate BVAR parameters for each pair of segments. CSWBVAR is formed by combining extracted BVAR parameters together with a pre-defined overlapping window parameter. We analyzed and compared CSWBVAR feature and univariate feature using the dataset III for motor imagery problem of BCI Competition II (2003). Preliminary results show that using CSWBVAR feature can improve classification accuracy up to 7% comparing with using univariate one with the same linear-kernel SVM classifier.
Lecture Notes in Computer Science, 2013
The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for pers... more The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low conductivity of the human skull and the EEG data have unknown distribution. We propose a multi-sphere support vector data description (MSSVDD) method to reduce noise and to provide a mixture of hyperspheres that can describe the EEG data distribution. We also propose a MSSVDD universal background model (UBM) to model impostors in person verification. Experimental results show that our proposed methods achieved lower verification error rates than other verification methods.
Lecture Notes in Computer Science, 2013
Extracting age and gender information from EEG data has not been investigated. This information i... more Extracting age and gender information from EEG data has not been investigated. This information is useful in building automatic systems that can classify a person into gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve performance of brain-computer interface systems. In this paper, we propose a framework based on PARAFAC and SVM that can automatically classify age and gender using EEG data. We also propose a method using N-PLS and SVM to improve the classification rate. Experimental results for the proposed method are presented.
2014 22nd International Conference on Pattern Recognition, 2014
Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a ... more Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a data description for a dataset while support vector classification (SVC) aims at separating data of two classes without providing a data description. This paper proposes a unified approach to both SVDD and SVC that aims at separating data of two classes and at the same time provides a data description. A trade off parameter is introduced to control the balance between describing the data and maximising the margin. Experimental results are provided to evaluate the proposed approach.
2012 Fourth International Conference on Communications and Electronics (ICCE), 2012
ABSTRACT Support vector data description (SVDD) has been widely used in pattern classification, h... more ABSTRACT Support vector data description (SVDD) has been widely used in pattern classification, however it does not provide high performance in brain-computer interface (BCI) classification problems since brain signals are noisy and chaotic. Brain data have distinct distributions and hence a hyper-sphere in SVDD could not well describe the data. We propose in this paper a multi-sphere approach to SVDD to have a better description for the brain data. We also propose a fuzzy clustering approach to optimize SVDD parameters. Experiments on the brain data set III for motor imagery problem in BCI Competition II were conducted to compare performance of SVDD and multi-sphere SVDD.
2014 International Joint Conference on Neural Networks (IJCNN), 2014
Person identification using electroencephalogram (EEG) as biometric has been widely used since it... more Person identification using electroencephalogram (EEG) as biometric has been widely used since it is capable of achieving high identification rate. Epilepsy is one of the brain disorders that involves in the EEG signal and hence it may have impact on EEG-based person identification systems. However, this issue has not been investigated. In this paper, we perform person identification on two groups of subjects, normal and epileptic to investigate the impact of epilepsy on the identification rate. Autoregressive model (AR) and Approximate entropy (ApEn) are employed to extract features from these two groups. Experimental results show that epilepsy actually have impacts depending on feature extraction method used in the system.
International Conference on Communications and Electronics 2010, 2010
An automatic voice-based classification system of speaker characteristics including age, gender a... more An automatic voice-based classification system of speaker characteristics including age, gender and accent is presented in this paper. Speakers are grouped according to their characteristics and their speech features are then extracted to train speaker group models using different classification techniques. Finally fusion of classification results for those speaker groups is performed to obtain results for each speaker characteristic. The ANDOSL Australian speech database consisting of 108 speakers and 21600 long utterances was used for system evaluation. Experiments showed high performance for the proposed classification of speaker characteristics. Keywords-component; speaker characteristics; speech processing; vector quantization; Gaussian mixture model; support vector machine. I. INTRODUCTION Speaker characteristics can be divided in to relatively stable characteristics and transient characteristics. Stable speaker characteristics comprise physiological and anatomical factors such as gender and age. Transient speaker characteristics comprise stress and emotional state. Stable speaker characteristics are easier to recognise [1]. The most important stable speaker characteristics will be mentioned below. Classifying speaker characteristics is an important task in Dialog Systems, Speech Synthesis, Forensics, Language Learning, Assessment Systems, and Speaker Recognition Systems [2]. In Human-Computer Interaction applications, the interaction between users and computers taking place at the speech-driven user interface. For example, Spoken Dialogs Systems provide services in domains of finance, travel, scheduling, tutoring, or weather. The systems need to gather automatically information from the user in order to provide timely and relevant services. Most telephone-based services today use spoken dialog systems to either route calls to the appropriate agent or even handle the complete service by an automatic system. Another example of Human-Computer Interaction application is Computer-aided Learning and Assessment systems. The systems provide interactive recording and playback of user's input speech, feedback regarding acoustic speech features, recognizing the input, and interpreting interaction to act as a conversation partner. Besides customizing to the native language of the language learner, learning systems may have to be tailored towards particular accents, for example the E-Language Learning System program between the U.S. Department of Education
International Conference on Fuzzy Systems, 2010
Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ... more Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ employs a clustering technique to convert a feature vector set in to a cluster center set to model the feature vector set. Some clustering techniques have been applied to improve VQ. However VQ is not always effective because data features are treated equally although their importance may not be the same. Some automated feature weighting techniques have been proposed to overcome this drawback. This paper reviews those weighting techniques and proposes a general scheme for selecting any pair of clustering and feature weighting techniques to form a fuzzy feature weighting-based VQ modelling technique. Besides the current techniques, a number of new feature weightingbased VQ techniques is proposed and their evaluations are also presented.
2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013
ABSTRACT The effects of age and gender on EEG signal have been investigated in clinical psychophy... more ABSTRACT The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented.
2009 34th IEEE Photovoltaic Specialists Conference (PVSC), 2009
Page 1. PREPARATION OF Ti02 THIN FILM USING MODIFIED DOCTOR-BLADE METHOD FOR IMPROVEMENT OF DYE-S... more Page 1. PREPARATION OF Ti02 THIN FILM USING MODIFIED DOCTOR-BLADE METHOD FOR IMPROVEMENT OF DYE-SENSITIZED SOLAR CELL Tan-Phat Huynh', Thi-Thao Hoanq', Phuoc-Hiep Nguyen1 , Thanh-Nam Tran1 , The-Vinh Nguyen 2 ,* ...
Advances in Intelligent Systems and Computing, 2014
ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user aut... more ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user authentication with many security advantages of being difficult to fake, impossible to observe or intercept, unique, and alive person recording require. The difficulty is that EEG signals are very weak and subject to the contamination from many artifact signals. However, for the applications in human health, true EEG signals, without the contamination, is highly desirable, but for the purposes of authentication, where stable and repeatable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose an EEG-based authentication method, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements. These tasks can be single or combined, depending on the level of security required. Our experiment showed that using EEG artifacts for user authentication in multilevel security systems is promising.
Advanced Data Mining and Applications, 2013
User authentication plays an important role in security systems. In general, there are three type... more User authentication plays an important role in security systems. In general, there are three types of authentications: password based, token based, and biometrics based. Each of them has its own merits and drawbacks. Recently, the research communities successfully explore the possibility that electroencephalography (EEG) being as a new type of biometrics in person recognition, and hence the prospect of using EEG in user authentication is promising. An EEG-based user authentication system has the combined advantages of both password based and biometric based authentication systems, yet without their drawbacks. In this paper we propose to use EEG to authenticate users in multilevel security systems where users are asked to provide EEG signal for authentication by performing motor imagery tasks. These tasks can be single or combined, depending on the level of security required. The analysis and processing of EEG signals of motor imagery will be presented through our experimental results.
The 2011 International Joint Conference on Neural Networks, 2011
... for Novelty Detection Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma and Dharmendra Sharma ... Ho... more ... for Novelty Detection Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma and Dharmendra Sharma ... However this work was not presented in detail, the Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 31 – August 5, 2011 ...
The Journal of Korean Institute of Communications and Information Sciences, 2019
Http Www Theses Fr, Feb 2, 2012
s 0 f (r)dr. Grâce à ces résultats, on donne une nouvelle construction de la trace initiale et qu... more s 0 f (r)dr. Grâce à ces résultats, on donne une nouvelle construction de la trace initiale et quelques résultats d'unicité et de non-unicité de solutions dont la donnée initiale n'est pas bornée. Dans la troisième partie, on élargit le cadre de nos investigations et généralise les résultats obtenus dans la deuxième partie au cas où l'opérateur est non-linéaire. En particulier, on s'intéresse à des propriétés qualitatives de solutions positives de l'équation ∂ t u − ∆ p u + f (u) = 0 dans R N × (0, ∞) où p > 1, ∆ p u = div(|∇u| p−2 ∇u) et f est une v RÉSUMÉ fonction continue, croissante, positive et satisfaisant f (0) = 0 = f −1 (0). Si p > 2N N +1 , on fournit une condition suffisante portant sur f pour l'existence et l'unicité des solutions fondamentales de données initiales kδ 0 et on étudie la limite, lorsque k → ∞, qui dépend du fait que f −1 et F −1/p soient intégrables à l'infini ou pas, où F (s) = s 0 f (r)dr. On donne aussi de nouveaux résultats de non-unicité de solutions avec donnée initiale non bornée. Si p ≥ 2, on prouve que toute solution positive admet une trace initiale dans la classe de mesures de Borel régulières positives. Finalement on applique les résultats ci-dessus au cas modèle f (u) = u α ln β (u + 1) avec α > 0 et β > 0. Mots clés : équations elliptiques quasilinéaires, singularités isolées, mesures de Radon, mesures de Borel, capacités de Bessel, trace au bord, singularités éliminables, absorption faiblement sur-linéaire, trace initiale, condition de Keller-Osserman, équations de la chaleur dégénérées. vi Boundary trace of solutions to elliptic Hamilton-Jacobi equations and initial trace of solutions to heat equations with superlinear absorption
La presente invention concerne un procede de limitation du courant en sortie d'un variateur d... more La presente invention concerne un procede de limitation du courant en sortie d'un variateur de vitesse (V) pour moteur electrique asynchrone triphase (M), ledit variateur (V) fonctionnant selon une loi de commande (LC) de type U/F pure. Ledit procede se caracterise en ce qu'il consiste a calculer a l'aide d'une fonction de limitation une valeur de correction a la frequence du stator (Wstat) puis a lui appliquer, de maniere a obtenir, dans la loi de commande en tension (LC) de type U/F, une frequence du stator corrigee (Wstatc). Selon la loi de commande en tension de type U/F, le procede consiste ensuite a partir de cette frequence du stator corrigee (Wstatc) pour obtenir la tension (Vqref) de commande appliquee au moteur. L'invention concerne egalement un systeme de limitation du courant pouvant mettre en oeuvre ledit procede.
The main goal of safe aquaculture is responsible and sustainable production which is safe for the... more The main goal of safe aquaculture is responsible and sustainable production which is safe for the consumer and maintains environmental integrity. The purpose of a safe aquaculture zone is to create a cluster of farms within a defined boundary where biosecurity and safe aquaculture practices are undertaken
Lecture Notes in Computer Science, 2010
This paper presents an automatic speech-based classification scheme to classify speaker character... more This paper presents an automatic speech-based classification scheme to classify speaker characteristics. In the training phase, speech data are grouped into speaker groups according to speakers’ gender, age and accent. Voice features are then extracted to feature vectors which are used to train speaker characteristic models with different techniques which are Vector Quantization, Gaussian Mixture Model and Support Vector Machine.
2011 5th International IEEE/EMBS Conference on Neural Engineering, 2011
In EEG-based classification problem, most of currently used features are univariate and extracted... more In EEG-based classification problem, most of currently used features are univariate and extracted from single channels. However EEG signals recorded from multiple channels for a brain activity are correlated, features extracted from the EEG signals should reflect relationships among those channels. For this reason, we propose and apply a bivariate feature called Combined Short-Window BiVariate AutoRegressive model (CSWBVAR) for EEG classification problems. Given a pair of channels, we firstly divide each of them in to overlapping segments or short windows, and then estimate BVAR parameters for each pair of segments. CSWBVAR is formed by combining extracted BVAR parameters together with a pre-defined overlapping window parameter. We analyzed and compared CSWBVAR feature and univariate feature using the dataset III for motor imagery problem of BCI Competition II (2003). Preliminary results show that using CSWBVAR feature can improve classification accuracy up to 7% comparing with using univariate one with the same linear-kernel SVM classifier.
Lecture Notes in Computer Science, 2013
The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for pers... more The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low conductivity of the human skull and the EEG data have unknown distribution. We propose a multi-sphere support vector data description (MSSVDD) method to reduce noise and to provide a mixture of hyperspheres that can describe the EEG data distribution. We also propose a MSSVDD universal background model (UBM) to model impostors in person verification. Experimental results show that our proposed methods achieved lower verification error rates than other verification methods.
Lecture Notes in Computer Science, 2013
Extracting age and gender information from EEG data has not been investigated. This information i... more Extracting age and gender information from EEG data has not been investigated. This information is useful in building automatic systems that can classify a person into gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve performance of brain-computer interface systems. In this paper, we propose a framework based on PARAFAC and SVM that can automatically classify age and gender using EEG data. We also propose a method using N-PLS and SVM to improve the classification rate. Experimental results for the proposed method are presented.
2014 22nd International Conference on Pattern Recognition, 2014
Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a ... more Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a data description for a dataset while support vector classification (SVC) aims at separating data of two classes without providing a data description. This paper proposes a unified approach to both SVDD and SVC that aims at separating data of two classes and at the same time provides a data description. A trade off parameter is introduced to control the balance between describing the data and maximising the margin. Experimental results are provided to evaluate the proposed approach.
2012 Fourth International Conference on Communications and Electronics (ICCE), 2012
ABSTRACT Support vector data description (SVDD) has been widely used in pattern classification, h... more ABSTRACT Support vector data description (SVDD) has been widely used in pattern classification, however it does not provide high performance in brain-computer interface (BCI) classification problems since brain signals are noisy and chaotic. Brain data have distinct distributions and hence a hyper-sphere in SVDD could not well describe the data. We propose in this paper a multi-sphere approach to SVDD to have a better description for the brain data. We also propose a fuzzy clustering approach to optimize SVDD parameters. Experiments on the brain data set III for motor imagery problem in BCI Competition II were conducted to compare performance of SVDD and multi-sphere SVDD.
2014 International Joint Conference on Neural Networks (IJCNN), 2014
Person identification using electroencephalogram (EEG) as biometric has been widely used since it... more Person identification using electroencephalogram (EEG) as biometric has been widely used since it is capable of achieving high identification rate. Epilepsy is one of the brain disorders that involves in the EEG signal and hence it may have impact on EEG-based person identification systems. However, this issue has not been investigated. In this paper, we perform person identification on two groups of subjects, normal and epileptic to investigate the impact of epilepsy on the identification rate. Autoregressive model (AR) and Approximate entropy (ApEn) are employed to extract features from these two groups. Experimental results show that epilepsy actually have impacts depending on feature extraction method used in the system.
International Conference on Communications and Electronics 2010, 2010
An automatic voice-based classification system of speaker characteristics including age, gender a... more An automatic voice-based classification system of speaker characteristics including age, gender and accent is presented in this paper. Speakers are grouped according to their characteristics and their speech features are then extracted to train speaker group models using different classification techniques. Finally fusion of classification results for those speaker groups is performed to obtain results for each speaker characteristic. The ANDOSL Australian speech database consisting of 108 speakers and 21600 long utterances was used for system evaluation. Experiments showed high performance for the proposed classification of speaker characteristics. Keywords-component; speaker characteristics; speech processing; vector quantization; Gaussian mixture model; support vector machine. I. INTRODUCTION Speaker characteristics can be divided in to relatively stable characteristics and transient characteristics. Stable speaker characteristics comprise physiological and anatomical factors such as gender and age. Transient speaker characteristics comprise stress and emotional state. Stable speaker characteristics are easier to recognise [1]. The most important stable speaker characteristics will be mentioned below. Classifying speaker characteristics is an important task in Dialog Systems, Speech Synthesis, Forensics, Language Learning, Assessment Systems, and Speaker Recognition Systems [2]. In Human-Computer Interaction applications, the interaction between users and computers taking place at the speech-driven user interface. For example, Spoken Dialogs Systems provide services in domains of finance, travel, scheduling, tutoring, or weather. The systems need to gather automatically information from the user in order to provide timely and relevant services. Most telephone-based services today use spoken dialog systems to either route calls to the appropriate agent or even handle the complete service by an automatic system. Another example of Human-Computer Interaction application is Computer-aided Learning and Assessment systems. The systems provide interactive recording and playback of user's input speech, feedback regarding acoustic speech features, recognizing the input, and interpreting interaction to act as a conversation partner. Besides customizing to the native language of the language learner, learning systems may have to be tailored towards particular accents, for example the E-Language Learning System program between the U.S. Department of Education
International Conference on Fuzzy Systems, 2010
Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ... more Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ employs a clustering technique to convert a feature vector set in to a cluster center set to model the feature vector set. Some clustering techniques have been applied to improve VQ. However VQ is not always effective because data features are treated equally although their importance may not be the same. Some automated feature weighting techniques have been proposed to overcome this drawback. This paper reviews those weighting techniques and proposes a general scheme for selecting any pair of clustering and feature weighting techniques to form a fuzzy feature weighting-based VQ modelling technique. Besides the current techniques, a number of new feature weightingbased VQ techniques is proposed and their evaluations are also presented.
2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013
ABSTRACT The effects of age and gender on EEG signal have been investigated in clinical psychophy... more ABSTRACT The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented.
2009 34th IEEE Photovoltaic Specialists Conference (PVSC), 2009
Page 1. PREPARATION OF Ti02 THIN FILM USING MODIFIED DOCTOR-BLADE METHOD FOR IMPROVEMENT OF DYE-S... more Page 1. PREPARATION OF Ti02 THIN FILM USING MODIFIED DOCTOR-BLADE METHOD FOR IMPROVEMENT OF DYE-SENSITIZED SOLAR CELL Tan-Phat Huynh', Thi-Thao Hoanq', Phuoc-Hiep Nguyen1 , Thanh-Nam Tran1 , The-Vinh Nguyen 2 ,* ...
Advances in Intelligent Systems and Computing, 2014
ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user aut... more ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user authentication with many security advantages of being difficult to fake, impossible to observe or intercept, unique, and alive person recording require. The difficulty is that EEG signals are very weak and subject to the contamination from many artifact signals. However, for the applications in human health, true EEG signals, without the contamination, is highly desirable, but for the purposes of authentication, where stable and repeatable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose an EEG-based authentication method, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements. These tasks can be single or combined, depending on the level of security required. Our experiment showed that using EEG artifacts for user authentication in multilevel security systems is promising.
Advanced Data Mining and Applications, 2013
User authentication plays an important role in security systems. In general, there are three type... more User authentication plays an important role in security systems. In general, there are three types of authentications: password based, token based, and biometrics based. Each of them has its own merits and drawbacks. Recently, the research communities successfully explore the possibility that electroencephalography (EEG) being as a new type of biometrics in person recognition, and hence the prospect of using EEG in user authentication is promising. An EEG-based user authentication system has the combined advantages of both password based and biometric based authentication systems, yet without their drawbacks. In this paper we propose to use EEG to authenticate users in multilevel security systems where users are asked to provide EEG signal for authentication by performing motor imagery tasks. These tasks can be single or combined, depending on the level of security required. The analysis and processing of EEG signals of motor imagery will be presented through our experimental results.
The 2011 International Joint Conference on Neural Networks, 2011
... for Novelty Detection Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma and Dharmendra Sharma ... Ho... more ... for Novelty Detection Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma and Dharmendra Sharma ... However this work was not presented in detail, the Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 31 – August 5, 2011 ...