Search for: principal-component-analysis
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    Government interventions and the size of the informal economy. The case of Iran (1971-2007)

    , Article Journal of Economic Policy Reform ; Vol. 17, issue. 1 , 2014 , pp. 71-90 ; ISSN: 17487870 Khandan, A ; Nili, M ; Sharif University of Technology
    Literature on the informal economy can mainly be divided into two different contrasting theories. According to the dual labor market theory (DLM), which considers the informal economy as a spare sector providing jobs for formally unemployed resources, unpleasant economic situations force people to act informally. Legalists, on the other hand, blame government interventions such as minimum wages or price control policies for pushing rent-seeking firms toward the shadows. This study using an Error-correction Multi-Indicators Multi-Causes (EMIMIC) model, a systematic method consisting of structural and measurement equations, shows that these two theories are complementary rather than... 

    Voice conversion using nonlinear principal component analysis

    , Article 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, Honolulu, HI, 1 April 2007 through 5 April 2007 ; 2007 , Pages 336-339 ; 1424407079 (ISBN); 9781424407071 (ISBN) Makki, B ; Seyed salehi, S. A ; Sadati, N ; Noori Hosseini, M ; Sharif University of Technology
    In the last decades, much attention has been paid to the design of multi-speaker voice conversion. In this work, a new method for voice conversion (VC) using nonlinear principal component analysis (NLPCA) is presented. The principal components are extracted and transformed by a feed-forward neural network which is trained by combination of Genetic Algorithm (GA) and Back-Propagation (BP). Common pre- and post-processing approaches are applied to increase the quality of the synthesized speech. The results indicate that the proposed method can be considered as a step towards multi-speaker Voice conversion. © 2007 IEEE  

    Unaligned training for voice conversion based on a local nonlinear principal component analysis approach

    , Article Neural Computing and Applications ; Volume 19, Issue 3 , 2010 , Pages 437-444 ; 09410643 (ISSN) Makki, B ; Noori Hosseini, M ; Seyyedsalehi, S. A ; Sadati, N ; Sharif University of Technology
    During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at first, performance of the network is described by means of an artificial dataset and then, the developed method is applied to perform VC  

    Statistical feature embedding for heart sound classification

    , Article Journal of Electrical Engineering ; Volume 70, Issue 4 , 2019 , Pages 259-272 ; 13353632 (ISSN) Adiban, M ; Babaali, B ; Shehnepoor, S ; Sharif University of Technology
    De Gruyter Open Ltd  2019
    Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed length feature vector; called i-vector; is extracted from each heart sound using Mel Frequency Cepstral Coefficient (MFCC) features. Afterwards, Principal Component Analysis (PCA) transform and Variational Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced... 

    An efficient PCA-based color transfer method

    , Article Journal of Visual Communication and Image Representation ; Volume 18, Issue 1 , 2007 , Pages 15-34 ; 10473203 (ISSN) Abadpour, A ; Kasaei, S ; Sharif University of Technology
    Color information of natural images can be considered as a highly correlated vector space. Many different color spaces have been proposed in the literature with different motivations toward modeling and analysis of this stochastic field. Recently, color transfer among different images has been under investigation. Color transferring consists of two major categories: colorizing grayscale images and recoloring colored images. The literature contains a few color transfer methods that rely on some standard color spaces. In this paper, taking advantages of the principal component analysis (PCA), we propose a unifying framework for both mentioned problems. The experimental results show the... 

    Method as a preprocessing stage for tracking sperms progressive motility

    , Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013 ; 2013 , Pages 170-174 Monfared, S. S. M. S ; Lashgari, E ; Aghdam, A. A ; Khalaj, B. H ; Sharif University of Technology
    IEEE Computer Society  2013
    Methods of human semen assessment are quite wide ranging. In this paper, we use background subtraction methods in order to detect progressive sperms whose quality of movement strongly influence fertility. Robust Principal Component Analysis (RPCA) is a powerful algorithm which has been used recently for background subtraction purposes. Sperm tracking problem can also be defined as a background subtraction problem. In RPCA algorithm, data is represented by a low rank plus sparse matrix. In our approach, the foreground data is recovered through such matrix decomposition. We compare the RPCA approach with four other background subtraction methods in order to check accuracy of algorithm as a... 

    The integration of principal component analysis and cepstral mean subtraction in parallel model combination for robust speech recognition

    , Article Digital Signal Processing: A Review Journal ; Volume 21, Issue 1 , 2011 , Pages 36-53 ; 10512004 (ISSN) Veisi, H ; Sameti, H ; Sharif University of Technology
    This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In... 

    Sparse ICA via cluster-wise PCA

    , Article Neurocomputing ; Volume 69, Issue 13-15 , 2006 , Pages 1458-1466 ; 09252312 (ISSN) Babaie Zadeh, M ; Jutten, C ; Mansour, A ; Sharif University of Technology
    In this paper, it is shown that independent component analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise principal component analysis (PCA). Consequently, Sparse ICA may be done by a combination of a clustering algorithm and PCA. For the clustering part, we use, in this paper, an algorithm inspired from K-means. The final algorithm is easy to implement for any number of sources. Experimental results points out the good performance of the method, whose the main restriction is to request an exponential growing of the sample number as the number of sources increases. © 2006 Elsevier B.V. All rights reserved  

    An innovative chemometric approach for simultaneous determination of polycyclic aromatic hydrocarbons in oil-contaminated waters based on dispersive micro-solid phase extraction followed by gas chromatography

    , Article Microchemical Journal ; Volume 159 , 2020 Saburouh, N ; Jabbari, A ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2020
    In the present study, an analytical strategy was developed using reduced graphene oxide (rGO) as an effective sorbent in dispersive micro-solid phase extraction (DMSPE) for simultaneous determination of seven polycyclic aromatic hydrocarbons (PAHs) combined with gas chromatography (GC-FID). rGO was synthesized using modified Hummer's method and characterized using scanning electron microscope (SEM), atomic force microscope (AFM) and Raman spectroscopy. A rotatable central composite design (CCD) combined with multiple linear regression (MLR) and analysis of variance (ANOVA) was used for designing, modelling and optimization of the extraction procedure. In this regard, principal component... 

    Failure threshold determination of rolling element bearings using vibration fluctuation and failure modes

    , Article Applied Sciences (Switzerland) ; Volume 11, Issue 1 , 2021 , Pages 1-18 ; 20763417 (ISSN) Behzad, M ; Feizhoseini, S ; Addin Arghand, H ; Davoodabadi, A ; Mba, D ; Sharif University of Technology
    MDPI AG  2021
    One of the challenges in predicting the remaining useful life (RUL) of rolling element bearings (REBs) is determining a proper failure threshold (FT). In the literature, the FT is usually assumed to be a constant value of an extracted feature from the vibration signals. In this study, a degradation indicator was extracted to describe damage to REBs by applying principal component analysis (PCA) to their run-to-failure data. The relationship between this degradation indicator and the vibration peak was represented through a joint probability distribution using statistical copula models. The FT was proposed as a probability distribution based on the fluctuation increase in the vibration trend.... 

    Mixed qualitative/quantitative dynamic simulation of processing systems

    , Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 24, Issue 1 , 2005 , Pages 53-67 ; 10219986 (ISSN) Yadegar, S ; Pishvaie, M. R ; Sharif University of Technology
    In this article the methodology proposed by Li and Wang for mixed qualitative and quantitative modeling and simulation of temporal behavior of processing unit is reexamined and extended to more complex case. The main issue of their approach considers the multivariate statistics of principal component analysis (PCA), along with clustered fuzzy digraphs and reasoning. The PCA and fuzzy clustering provide tools to categorize the quantitative dynamic trends, describing the temporal behavior of joint human-process interactions qualitatively, and through the proposed neuro-fuzzy reasoning the system responses can be obtained when the system is exposed to uncertain disturbances. First, the method... 

    Dimension reduction of optical remote sensing images via minimum change rate deviation method

    , Article IEEE Transactions on Geoscience and Remote Sensing ; Volume 48, Issue 1 , 2010 , Pages 198-206 ; 01962892 (ISSN) Dianat, R ; Kasaei, S ; Sharif University of Technology
    This paper introduces a new dimension reduction (DR) method, called minimum change rate deviation (MCRD), which is applicable to the DR of remote sensing images. As the main shortcoming of the well-known principal component analysis (PCA) method is that it does not consider the spatial relation among image points, our proposed approach takes into account the spatial relation among neighboring image pixels while preserving all useful properties of PCA. These include uncorrelatedness property in resulted components and the decrease of error with the increasing of the number of selected components. Our proposed method can be considered as a generalization of PCA and, under certain conditions,... 

    Ictal EEG signal denoising by combination of a semi-blind source separation method and multiscale PCA

    , Article 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016, 23 November 2016 through 25 November 2016 ; 2017 , Pages 226-231 ; 9781509034529 (ISBN) Pouranbarani, E ; Hajipour Sardoubie, S ; Shamsollahi, M. B ; Sharif University of Technology
    Contamination of ictal Electroencephalogram (EEG) signals by muscle artifacts is one of the critical issues related to clinically diagnosing seizure. Over the past decade, several methods have been proposed in time, frequency and time-frequency domain to accurately isolate ictal EEG activities from artifacts. Among denoising approaches Canonical Correlation Analysis (CCA) and Independent Component Analysis (ICA) are widely used. Denoising based on Generalized EigenValue Decomposition (GEVD) is one of the Semi-Blind Source Separation (SBSS) methods which has been recently proposed. In the GEVD-based method, a couple of time-frequency covariance matrices are used. These time-frequency (TF)... 

    Intensity estimation of spontaneous facial action units based on their sparsity properties

    , Article IEEE Transactions on Cybernetics ; Volume 46, Issue 3 , 2016 , Pages 817-826 ; 21682267 (ISSN) Mohammadi, M. R ; Fatemizadeh, E ; Mahoor, M. H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2016
    Automatic measurement of spontaneous facial action units (AUs) defined by the facial action coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different facial activities and expressions. In spontaneous facial expressions, a subset of AUs are often occurred or activated at a time. Given this fact that AUs occurred sparsely over time, we propose a novel method to detect the absence and presence of AUs and estimate their intensity levels via sparse representation (SR). We use the robust principal component analysis to decompose expression from facial identity and then estimate the intensity of multiple AUs jointly using a regression model... 

    A sensitivity study of FILTERSIM algorithm when applied to DFN modeling

    , Article Journal of Petroleum Exploration and Production Technology ; Vol. 4, issue. 2 , June , 2014 , p. 153-174 ; ISSN: 21900558 Ahmadi, R ; Masihi, M ; Rasaei, M. R ; Eskandaridalvand, K ; Shahalipour, R ; Sharif University of Technology
    Realistic description of fractured reservoirs demands primarily for a comprehensive understanding of fracture networks and their geometry including various individual fracture parameters as well as network connectivities. Newly developed multiple-point geostatistical simulation methods like SIMPAT and FILTERSIM are able to model connectivity and complexity of fracture networks more effectively than traditional variogrambased methods. This approach is therefore adopted to be used in this paper. Among the multiple-point statistics algorithms, FILTERSIM has the priority of less computational effort than does SIMPAT by applying filters and modern dimensionality reduction techniques to the... 

    PCA-based dictionary building for accurate facial expression recognition via sparse representation

    , Article Journal of Visual Communication and Image Representation ; Vol. 25, issue. 5 , July , 2014 , pp. 1082-1092 ; ISSN: 10473203 Mohammadi, M. R ; Fatemizadeh, E ; Mahoor, M. H ; Sharif University of Technology
    Sparse representation is a new approach that has received significant attention for image classification and recognition. This paper presents a PCA-based dictionary building for sparse representation and classification of universal facial expressions. In our method, expressive facials images of each subject are subtracted from a neutral facial image of the same subject. Then the PCA is applied to these difference images to model the variations within each class of facial expressions. The learned principal components are used as the atoms of the dictionary. In the classification step, a given test image is sparsely represented as a linear combination of the principal components of six basic... 

    Metabolic load comparison between the quarters of a game in elite male basketball players using sport metabolomics

    , Article European Journal of Sport Science ; 2020 Khoramipour, K ; Gaeini, A. A ; Shirzad, E ; Gilany, K ; Chashniam, S ; Sandbakk, Ø ; Sharif University of Technology
    Taylor and Francis Ltd  2020
    Purpose: A basketball match is characterized by intermittent high-intensity activities, thereby relying extensively on both aerobic and anaerobic metabolic pathways. Here, we aimed to compare the metabolic fluctuations between the four 10-min quarters of high-level basketball games using metabolomics analyses. Methods: 70 male basketball players with at least 3 years of experience in the Iran national top-league participated. Before and after each quarter, saliva samples were taken for subsequent untargeted metabolomics analyses, where Principal component analysis (PCA) and Partial least squares-discriminant analysis (PLS-DA) were employed for statistical analysis. Results: Quarters 1 and 3... 

    Multiple sclerosis diagnosis based on analysis of subbands of 2-D wavelet transform applied on MR-images

    , Article 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007, Amman, 13 May 2007 through 16 May 2007 ; 2007 , Pages 717-721 ; 1424410312 (ISBN); 9781424410316 (ISBN) Torabi, M ; Moradzadeh, H ; Vaziri, R ; Dehestani Ardekani, R ; Fatemizadeh, E ; Sharif University of Technology
    In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by Multiple Sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the Principal Component Analysis (PCA) and have been pushed to an Artificial Neural Network (ANN) that is a feed-forward type. According to changing in position of defected parts of... 

    Principal component analysis-ranking as a variable selection method for the simulation of 13C nuclear magnetic resonance spectra of xanthones using artificial neural networks

    , Article QSAR and Combinatorial Science ; Volume 26, Issue 6 , 2007 , Pages 764-772 ; 1611020X (ISSN) Jalali Heravi, M ; Shahbazikhah, P ; Zekavat, B ; Ardejani, M. S ; Sharif University of Technology
    A Quantitative Structure-Property Relationship (QSPR) relating atom-based calculated descriptors to 13C NMR chemical shifts was developed to accurately simulate 13C NMR spectra of polyhydroxy and methoxy substituted dibenzo pyrons (xanthones). A dataset consisting of 35 xanthones was employed for the present analysis. A set of 132 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated for each of 497 unique carbon atoms in the dataset. Principal Component Analysis (PCA)-ranking and Artificial Neural Networks (ANNs) were used as descriptor selection and model building methods, respectively. Analyses of the results revealed a... 

    A principal-components approach to assign confidence intervals in steady-state simulation

    , Article IIE Transactions (Institute of Industrial Engineers) ; Volume 38, Issue 2 , 2006 , Pages 117-126 ; 0740817X (ISSN) Akhavan Niaki, S. T ; Iskander, W. H ; Sharif University of Technology
    This paper presents an approach to the assignment of a confidence interval to the mean of a stream of autocorrelated output data from a steady-state simulation run. Based on the principal-components analysis method, the approach is to derive a linear transformation of the data that yields approximate independence of the transformed data. To aid convergence to normality (on which the confidence interval is based) and to keep the dimension of the transformation reasonable, the original output data are batched prior to performing the transformation. The approach is fairly simple to understand and to implement, and experimental results indicate that it may perform better than the non-overlapping...