majid kiamini - Academia.edu (original) (raw)

Papers by majid kiamini

Research paper thumbnail of A high_performance steganographic method using JPEG and PSO algorithm

2008 IEEE International Multitopic Conference, 2008

In this paper, we present a novel method to embed secret message in the cover-image so that the i... more In this paper, we present a novel method to embed secret message in the cover-image so that the interceptors will not notice about the existence of the hidden data. The basic concept of the proposed method is by simple Least Significant Bit (LSB) substitution. In order to improve the quality of stego-image and to increase the secret message capacity and security level, we inspire by the work of Li and Wang [1] which splits the cover-image into n blocks of 8 X 8 pixels and the secret message into n partitions. Then we apply Particle Swarm Optimization (PSO) algorithm to search approximate optimal solutions and to find an optimal substitution matrix for transforming the secret message in each block, instead of finding only one optimal substitution matrix for the whole cover-image as in [1]. The quality of the resulting stegoimage, the secret message capacity and the security level of the proposed method are calculated and compared to other methods. Experimental results show that the proposed method outperforms the JPEG and Quantization Table Modification (JQTM) method and the Li and Wang's work in image quality, embedding capacity and security level.

Research paper thumbnail of Feature Conditioning Based on DWT Sub-Bands Selection on Proposed Channels in BCI Speller

Journal of Biomedical Science and Engineering, 2017

In this paper, we present a novel and efficient scheme for detection of P300 component of the eve... more In this paper, we present a novel and efficient scheme for detection of P300 component of the event-related potential in the Brain Computer Interface (BCI) speller paradigm that needs significantly less EEG channels and uses a minimal subset of effective features. Removing unnecessary channels and reducing the feature dimension resulted in lower cost and shorter time and thus improved the BCI implementation. The idea was to employ a proper method to optimize the number of channels and feature vectors while keeping high accuracy in classification performance. Optimal channel selection was based on both discriminative criteria and forward-backward investigation. Besides, we obtained a minimal subset of effective features by choosing the discriminant coefficients of wavelet decomposition. Our algorithm was tested on dataset II of the BCI competition 2005. We achieved 92% accuracy using a simple LDA classifier, as compared with the second best result in BCI 2005 with an accuracy of 90.5% using SVM for classification which required more computation, and against the highest accuracy of 96.5% in BCI 2005 that used SVM and much more channels requiring excessive calculations. We also applied our proposed scheme on Hoffmann's dataset to evaluate the effectiveness of channel reduction and achieved acceptable results.

Research paper thumbnail of Optimizing feature vectors and removal unnecessary channels in BCI speller application

Journal of Biomedical Science and Engineering, 2013

In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) syst... more In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) system in speller application based on single-trial classification of electroencephalogram (EEG) signal. The idea is to employ proper methods for reducing the number of channels and optimizing feature vectors. Removal unnecessary channels and reducing feature dimension result in cost decrement, time saving and improve the BCI implementation eventually. Optimal channels will be gotten after two stages sifting. In the first stage, the channels reduced up to 30% based on channels of the important event related potential (ERP) components and in the next stage, optimal channels were extracted by backward forward selection (BFS) algorithm. Also we will show that suitable single-trial analysis requires applying proper feature vector that was constructed by recognizing important ERP components, so as to propose an algorithm to distinguish less important features in feature vectors. F-Score criteria used to recognize effective features which created more discrimination between different classes and feature vectors were reconstructed based on effective features. Our algorithm has tested on dataset II of BCI competition III. The results show that we achieve accuracy up to 31% in single-trial, which is better than the performance of winner who is in this competition (about 25.5%). Also we use simple classifier and few channels to compute output performances while more complicated classifier and all channels are used by them.

Research paper thumbnail of Elimination of Ocular Artifacts from EEG signals using the wavelet transform and empirical mode decomposition

2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009

Abstract Electroencephalogram (EEG) is a biological signal that represents the electrical activit... more Abstract Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. EEG signal may be highly distorted by the eye-blinks and movements of the eyeballs that are collectively known as ocular artifacts (OA). OA severely limit the utility ...

Research paper thumbnail of Observer-based fuzzy control of a three phase four wire PWM inverter

2008 IEEE International Multitopic Conference, 2008

Page 1. Observer-Based Fuzzy Control ofa Three Phase Four Wire PWM Inverter AbolfazlJalilvand Dep... more Page 1. Observer-Based Fuzzy Control ofa Three Phase Four Wire PWM Inverter AbolfazlJalilvand Department of Electrical Engineering Zanjan University Zanjan, Iran ajalilvand@znu. ac.ir Majid Kiamini Department of Electrical Engineering Zanjan University Zanjan, Iran ...

Research paper thumbnail of A high_performance steganographic method using JPEG and PSO algorithm

2008 IEEE International Multitopic Conference, 2008

In this paper, we present a novel method to embed secret message in the cover-image so that the i... more In this paper, we present a novel method to embed secret message in the cover-image so that the interceptors will not notice about the existence of the hidden data. The basic concept of the proposed method is by simple Least Significant Bit (LSB) substitution. In order to improve the quality of stego-image and to increase the secret message capacity and security level, we inspire by the work of Li and Wang [1] which splits the cover-image into n blocks of 8 X 8 pixels and the secret message into n partitions. Then we apply Particle Swarm Optimization (PSO) algorithm to search approximate optimal solutions and to find an optimal substitution matrix for transforming the secret message in each block, instead of finding only one optimal substitution matrix for the whole cover-image as in . The quality of the resulting stegoimage, the secret message capacity and the security level of the proposed method are calculated and compared to other methods. Experimental results show that the proposed method outperforms the JPEG and Quantization Table Modification (JQTM) method and the Li and Wang's work in image quality, embedding capacity and security level.

Research paper thumbnail of A wavelet based algorithm for Ocular Artifact detection In the EEG signals

2008 IEEE International Multitopic Conference, 2008

Artifacts are noises introduced to an electroencephalogram (EEG) signal by patient's movements. E... more Artifacts are noises introduced to an electroencephalogram (EEG) signal by patient's movements. Eye-blinks and movements of the eyeballs produce electrical signals that are collectively known as Ocular Artifacts (OA) and these are 10-100 times stronger than EEG signal which is being recorded. Removing artifacts from EEG signal may aid the work of doctors, because artifacts disturb their attention. This paper present a new method to automatically identify the position of Ocular Artifacts zones in contaminated EEG signal. Then only removing it's to obtain clean EEG based on the stationary wavelet transform (SWT). In this case, correlation coefficient between wavelet coefficients of the Ocular Artifacts zones in contaminated EEG signal and same zones in electrooculogram (EOG) will be used to generate the wavelet coefficients for artifact-free EEG signal.

Research paper thumbnail of A high_performance steganographic method using JPEG and PSO algorithm

2008 IEEE International Multitopic Conference, 2008

In this paper, we present a novel method to embed secret message in the cover-image so that the i... more In this paper, we present a novel method to embed secret message in the cover-image so that the interceptors will not notice about the existence of the hidden data. The basic concept of the proposed method is by simple Least Significant Bit (LSB) substitution. In order to improve the quality of stego-image and to increase the secret message capacity and security level, we inspire by the work of Li and Wang [1] which splits the cover-image into n blocks of 8 X 8 pixels and the secret message into n partitions. Then we apply Particle Swarm Optimization (PSO) algorithm to search approximate optimal solutions and to find an optimal substitution matrix for transforming the secret message in each block, instead of finding only one optimal substitution matrix for the whole cover-image as in [1]. The quality of the resulting stegoimage, the secret message capacity and the security level of the proposed method are calculated and compared to other methods. Experimental results show that the proposed method outperforms the JPEG and Quantization Table Modification (JQTM) method and the Li and Wang's work in image quality, embedding capacity and security level.

Research paper thumbnail of Feature Conditioning Based on DWT Sub-Bands Selection on Proposed Channels in BCI Speller

Journal of Biomedical Science and Engineering, 2017

In this paper, we present a novel and efficient scheme for detection of P300 component of the eve... more In this paper, we present a novel and efficient scheme for detection of P300 component of the event-related potential in the Brain Computer Interface (BCI) speller paradigm that needs significantly less EEG channels and uses a minimal subset of effective features. Removing unnecessary channels and reducing the feature dimension resulted in lower cost and shorter time and thus improved the BCI implementation. The idea was to employ a proper method to optimize the number of channels and feature vectors while keeping high accuracy in classification performance. Optimal channel selection was based on both discriminative criteria and forward-backward investigation. Besides, we obtained a minimal subset of effective features by choosing the discriminant coefficients of wavelet decomposition. Our algorithm was tested on dataset II of the BCI competition 2005. We achieved 92% accuracy using a simple LDA classifier, as compared with the second best result in BCI 2005 with an accuracy of 90.5% using SVM for classification which required more computation, and against the highest accuracy of 96.5% in BCI 2005 that used SVM and much more channels requiring excessive calculations. We also applied our proposed scheme on Hoffmann's dataset to evaluate the effectiveness of channel reduction and achieved acceptable results.

Research paper thumbnail of Optimizing feature vectors and removal unnecessary channels in BCI speller application

Journal of Biomedical Science and Engineering, 2013

In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) syst... more In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) system in speller application based on single-trial classification of electroencephalogram (EEG) signal. The idea is to employ proper methods for reducing the number of channels and optimizing feature vectors. Removal unnecessary channels and reducing feature dimension result in cost decrement, time saving and improve the BCI implementation eventually. Optimal channels will be gotten after two stages sifting. In the first stage, the channels reduced up to 30% based on channels of the important event related potential (ERP) components and in the next stage, optimal channels were extracted by backward forward selection (BFS) algorithm. Also we will show that suitable single-trial analysis requires applying proper feature vector that was constructed by recognizing important ERP components, so as to propose an algorithm to distinguish less important features in feature vectors. F-Score criteria used to recognize effective features which created more discrimination between different classes and feature vectors were reconstructed based on effective features. Our algorithm has tested on dataset II of BCI competition III. The results show that we achieve accuracy up to 31% in single-trial, which is better than the performance of winner who is in this competition (about 25.5%). Also we use simple classifier and few channels to compute output performances while more complicated classifier and all channels are used by them.

Research paper thumbnail of Elimination of Ocular Artifacts from EEG signals using the wavelet transform and empirical mode decomposition

2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009

Abstract Electroencephalogram (EEG) is a biological signal that represents the electrical activit... more Abstract Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. EEG signal may be highly distorted by the eye-blinks and movements of the eyeballs that are collectively known as ocular artifacts (OA). OA severely limit the utility ...

Research paper thumbnail of Observer-based fuzzy control of a three phase four wire PWM inverter

2008 IEEE International Multitopic Conference, 2008

Page 1. Observer-Based Fuzzy Control ofa Three Phase Four Wire PWM Inverter AbolfazlJalilvand Dep... more Page 1. Observer-Based Fuzzy Control ofa Three Phase Four Wire PWM Inverter AbolfazlJalilvand Department of Electrical Engineering Zanjan University Zanjan, Iran ajalilvand@znu. ac.ir Majid Kiamini Department of Electrical Engineering Zanjan University Zanjan, Iran ...

Research paper thumbnail of A high_performance steganographic method using JPEG and PSO algorithm

2008 IEEE International Multitopic Conference, 2008

In this paper, we present a novel method to embed secret message in the cover-image so that the i... more In this paper, we present a novel method to embed secret message in the cover-image so that the interceptors will not notice about the existence of the hidden data. The basic concept of the proposed method is by simple Least Significant Bit (LSB) substitution. In order to improve the quality of stego-image and to increase the secret message capacity and security level, we inspire by the work of Li and Wang [1] which splits the cover-image into n blocks of 8 X 8 pixels and the secret message into n partitions. Then we apply Particle Swarm Optimization (PSO) algorithm to search approximate optimal solutions and to find an optimal substitution matrix for transforming the secret message in each block, instead of finding only one optimal substitution matrix for the whole cover-image as in . The quality of the resulting stegoimage, the secret message capacity and the security level of the proposed method are calculated and compared to other methods. Experimental results show that the proposed method outperforms the JPEG and Quantization Table Modification (JQTM) method and the Li and Wang's work in image quality, embedding capacity and security level.

Research paper thumbnail of A wavelet based algorithm for Ocular Artifact detection In the EEG signals

2008 IEEE International Multitopic Conference, 2008

Artifacts are noises introduced to an electroencephalogram (EEG) signal by patient's movements. E... more Artifacts are noises introduced to an electroencephalogram (EEG) signal by patient's movements. Eye-blinks and movements of the eyeballs produce electrical signals that are collectively known as Ocular Artifacts (OA) and these are 10-100 times stronger than EEG signal which is being recorded. Removing artifacts from EEG signal may aid the work of doctors, because artifacts disturb their attention. This paper present a new method to automatically identify the position of Ocular Artifacts zones in contaminated EEG signal. Then only removing it's to obtain clean EEG based on the stationary wavelet transform (SWT). In this case, correlation coefficient between wavelet coefficients of the Ocular Artifacts zones in contaminated EEG signal and same zones in electrooculogram (EOG) will be used to generate the wavelet coefficients for artifact-free EEG signal.