• Title/Summary/Keyword: Independent component analysis(ICA)

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A TWO-STAGE SOURCE EXTRACTION ALGORITHM FOR TEMPORALLY CORRELATED SIGNALS BASED ON ICA-R

  • Zhang, Hongjuan;Shi, Zhenwei;Guo, Chonghui;Feng, Enmin
    • Journal of applied mathematics & informatics
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    • v.26 no.5_6
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    • pp.1149-1159
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    • 2008
  • Blind source extraction (BSE) is a special class of blind source separation (BSS) methods, which only extracts one or a subset of the sources at a time. Based on the time delay of the desired signal, a simple but important extraction algorithm (simplified " BC algorithm")was presented by Barros and Cichocki. However, the performance of this method is not satisfying in some cases for which it only carries out the constrained minimization of the mean squared error. To overcome these drawbacks, ICA with reference (ICA-R) based approach, which considers the higher-order statistics of sources, is added as the second stage for further source extraction. Specifically, BC algorithm is exploited to roughly extract the desired signal. Then the extracted signal in the first stage, as the reference signal of ICA-R method, is further used to extract the desired sources as cleanly as possible. Simulations on synthetic data and real-world data show its validity and usefulness.

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Classification of General Sound with Non-negativity Constraints (비음수 제약을 통한 일반 소리 분류)

  • 조용춘;최승진;방승양
    • Journal of KIISE:Software and Applications
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    • v.31 no.10
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    • pp.1412-1417
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    • 2004
  • Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

Face Recognitions Using Centroid Shift and Independent Basis Images (중심이동과 독립기저영상을 이용한 얼굴인식)

  • Cho Yong-Hyun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.5
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    • pp.581-587
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    • 2005
  • This paper presents a hybrid face recognition method of both the first moment of image and the independent component analysis(ICA) of fixed point(FP) algorithm based on Newton method. First moment is a method for finding centroid of image, which is applied to exclude the needless backgrounds in the face recognitions by shifting to the centroid of face image. FP-ICA is also applied to find a set of independent basis images for the faces, which is a set of statistically independent facial features. The proposed method has been applied to the problem for recognizing the 48 face images(12 persons o 4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate) than conventional FP-ICA without preprocessing. The city-block has been relatively achieved more an accurate similarity than Euclidean or negative angle.

On Parameterizing of Human Expression Using ICA (독립 요소 분석을 이용한 얼굴 표정의 매개변수화)

  • Song, Ji-Hey;Shin, Hyun-Joon
    • Journal of the Korea Computer Graphics Society
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    • v.15 no.1
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    • pp.7-15
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    • 2009
  • In this paper, a novel framework that synthesizes and clones facial expression in parameter spaces is presented. To overcome the difficulties in manipulating face geometry models with high degrees of freedom, many parameterization methods have been introduced. In this paper, a data-driven parameterization method is proposed that represents a variety of expressions with a small set of fundamental independent movements based on the ICA technique. The face deformation due to the parameters is also learned from the data to capture the nonlinearity of facial movements. With this parameterization, one can control the expression of an animated character's face by the parameters. By separating the parameterization and the deformation learning process, we believe that we can adopt this framework for a variety applications including expression synthesis and cloning. The experimental result demonstrates the efficient production of realistic expressions using the proposed method.

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Overlapped Subband-Based Independent Vector Analysis

  • Jang, Gil-Jin;Lee, Te-Won
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.1E
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    • pp.30-34
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    • 2008
  • An improvement to the existing blind signal separation (BSS) method has been made in this paper. The proposed method models the inherent signal dependency observed in acoustic object to separate the real-world convolutive sound mixtures. The frequency domain approach requires solving the well known permutation problem, and the problem had been successfully solved by a vector representation of the sources whose multidimensional joint densities have a certain amount of dependency expressed by non-spherical distributions. Especially for speech signals, we observe strong dependencies across neighboring frequency bins and the decrease of those dependencies as the bins become far apart. The non-spherical joint density model proposed in this paper reflects this property of real-world speech signals. Experimental results show the improved performances over the spherical joint density representations.

Independent Feature Subspace Analysis for Gene Expression Data (유전자 발현 데이터의 독립 특징 부공간 해석)

  • Kim, Heijin;Park, Seungjin;Bang, Sung-Yang
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.10c
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    • pp.739-742
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    • 2002
  • This paper addresses a new statistical method, IFSAcycle, which is an unsupervised learning method of analyzing cell cycle-related gene expression data. The IFSAcycle is based on the independent feature subspace analysis (IFAS) [3], which generalizes the independent component analysis (ICA). Experimental results show the usefulness of IFAS: (1) the ability of assigning genes to multiple coexpression pattern groups; (2) the capability of clustering key genes that determine each critical point of cell cycle.

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Channel Estimation Scheme Using Modified ICA in MIMO-OFDM Systems (MIMO-OFDM 시스템에서 Modified ICA를 이용한 채널 추정 기법)

  • Kim Jong-Deuk;Byun Youn-Shik
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.5A
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    • pp.475-483
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    • 2006
  • If channel coefficients errors exist in MIMO-OFDM systems, the performance degradation of systems will occurs. In order to improve the performance of MIMO-OFDM systems, the technique of obtaining accurate channel estimation in multipath fading channel is necessary. In this paper, we introduce and propose new channel estimation-modified ICA algorithm. Simulation results shows from BER and SER curves which compare the proposed algorithm under time-varying Rayleigh fading with perfect known channel. The result of channel estimation by the proposed algorithm in this simulation, it shows that PDF(amplitude of channel) are close to the case with perfect known channel at the receiver with respect to uncoded QPSK/16-QAM/64-QAM modulation. Also, we can see that BER and SER curves are very close to the case with perfect known channel. Therefore, we see that the proposed algorithm have a good performance in MIMO-OFDM systems.

Fault Detection Method for Multivariate Process using ICA (독립성분분석을 이용한 다변량 공정에서의 고장탐지 방법)

  • Jung, Seunghwan;Kim, Minseok;Lee, Hansoo;Kim, Jonggeun;Kim, Sungshin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.2
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    • pp.192-197
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    • 2020
  • Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

A Study on Efficient Topography Classification of High Resolution Satelite Image (고해상도 위성영상의 효율적 지형분류기법 연구)

  • Lim, Hye-Young;Kim, Hwang-Soo;Choi, Joon-Seog;Song, Seung-Ho
    • Journal of Korean Society for Geospatial Information Science
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    • v.13 no.3 s.33
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    • pp.33-40
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    • 2005
  • The aim of remotely sensed data classification is to produce the best accuracy map of the earth surface assigning each pixel to its appropriate category of the real-world. The classification of satellite multi-spectral image data has become tool for generating ground cover map. Many classification methods exist. In this study, MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes classifier algorithms are compared using IKONOS image of the part of Dalsung Gun, Daegu area. Two preprocessing methods are performed-PCA(Principal component analysis), ICA(Independent Component Analysis). Boosting algorithms also performed. By the combination of appropriate feature selection pre-processing and classifier, the best results were obtained.

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Non-Contact Heart Rate Monitoring from Face Video Utilizing Color Intensity

  • Sahin, Sarker Md;Deng, Qikang;Castelo, Jose;Lee, DoHoon
    • Journal of Multimedia Information System
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    • v.8 no.1
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    • pp.1-10
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    • 2021
  • Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.