• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.220-223
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• 2011

Respiration signal of the vital signs is an important parameter in clinical parts. To extract the respiration signal from PPG signal for mobile healthcare system is difficult because the bands of the motion artifacts and respiration in the frequency domain are overlapped. This study to improve this problem suggested a respiration extraction method using the independent component analysis and evaluated its performances. In results of evaluation, the ICA method showed better performance than LPF suggested recently.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.505-507
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• 2002

본 논문에서는 그레이 블록 거리알고리즘(grey block algorithms, 이하 GBD)을 이용하여 독립성분분석(independent component analysis; 이하 ICA) 및 첨도(Kurtosis)에서의 영상간의 거리를 측정하여, 어느 정도 영상간의 상대적 식별을 용이하게 하여 영상 분류가 되는지 모의 실험을 통하여 확인하고자 한다. 모의 실험 결과로부터, ICA에서는 k는 8까지 상대적 식별이 되어 영상 분류가 되었고, 첨도에서는 영상간의 상대적 식별을 k가 4까지만 블록을 분할 할 수 있었다.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.17-23
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• 2007

Long-term potentiation (LTP) of synaptic transmission is the most widely studied model for learning and memory. However its mechanisms are not clearly elucidated and are a subject for intense investigation. Previous attempts to decipher cellular mechanisms and network properties involved a current-source density analysis (CSDA) of the LTP from small animal hippocampus measured with a limited number of microelectrodes (typically <3), only revealing limited nature of spatiotemporal dynamics. Recent advancement in multi-electrode array (MEA) technology allows continuous and simultaneous recordings of LTP with more than 60 electrodes. However CSDA via the standard Laplacian transform is still limited due to its relatively high sensitivity toward noise, inability of resolving overlapped current sources and sinks, and its requirement for tissue conductivity values. In this study, we propose a new methodology for improved CSDA. Independent component analysis and its joint use (i.e., Joint-ICA) are applied to extract spatiotemporal components of LTP. The results show that ICA and Joint-ICA are capable of extracting independent spatiotemporal components of LTP generators. The ICs of LTP indicate the reversing roles of current sources and sinks which are associated with LTP.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1679-1686
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• 2009

Odor sensing system that is electronic nose device and its signal processing technique has potential to become a critical service for the people who require tangibility of sense of smell in the multimedia communication. PCA(Principal Component Analysis) have been used for dimensionality reduction and visualization of multivariate measurement data. PCA is good for estimating importance value by variance of data but, have some limitation for getting meaningful representation from odor sensing system. This paper explain about how to analyze the data of odor sensing system by ICA(Independent Component Analysis). We show that ICA can give better result like sensor drift analysis, dimensionality reduction and data representation by improved discrimination.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.739-741
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• 2003

Electroencephalogram (EEG)-based brain computer interface (BCI) provides a new communication channel between human brain and computer. EEG is very noisy data and contains artifacts, thus the extraction of features that are robust to noise and artifacts is important. In this paper we present a method with employ both independent component analysis (ICA) and oriented principal component analysis (OPCA) for artifact-robust feature extraction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.131-135
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• 2005

This paper proposes an efficient face recognition using independent component analysis(ICA) derived from the fixed point(FP) algorithm based on secant method. The secant method can exclude the complex computation of differential process from the FP based on Newton method. The proposed ICA has been applied to recognize the 20 Yale face images of $324\times324$ pixels. The experimental results show that the proposed ICA is superior to PCA not only in the restoration performance of basis images but also in the recognition performance of the trained images and the test images. Then negative angle as similarity measures has better recognition ratio than city-block and Euclidean.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.24-33
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• 1998

Independent Component analysis (ICA) is a new statistical method for extracting statistically independent components from their linear instantaneous mixtures which are generated by an unknown linear generative model. The recognition model is learned in unsupervised manner so that the recovered signals by the recognition model become the possibly scaled estimates of original source signals. This paper addresses the neural learning approach to ICA. As recognition models a linear feedforward network and a linear feedback network are considered. Associated learning algorithms for both networks are derived from maximum likelihood and information-theoretic approaches, using natural Riemannian gradient [1]. Theoretical results are confirmed by extensive computer simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.687-692
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• 2006

In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.27-34
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• 2006

In this study, we propose this new algorithm that generates score function in ICA(Independent Component Analysis) using entropy theory. To generate score function, estimation of probability density function about original signals are certainly necessary and density function should be differentiated. Therefore, we used kernel density estimation method in order to derive differential equation of score function by original signal. After changing formula to convolution form to increase speed of density estimation, we used FFT algorithm that can calculate convolution faster. Proposed score function generation method reduces the errors, it is density difference of recovered signals and originals signals. In the result of computer simulation, we estimate density function more similar to original signals compared with Extended Infomax and Fixed Point ICA in blind source separation problem and get improved performance at the SNR(Signal to Noise Ratio) between recovered signals and original signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.515-520
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• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).