Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...

Facial Expression Recognition is one of the active research area in the field of Human Machine Interaction (HMI) because of its several applications such as human emotion analysis, stress level and lie detection. In this paper, an algorithm for facial expression recognition has been proposed which integrate the Local Binary Patterns (LBP), Gabor filter and Principal Component Analysis (PCA). Th...

In the light of recent developments in optimal real L1-norm principal-component analysis (PCA), we provide the first algorithm in the literature to carry out L1-PCA of complexvalued data. Then, we use this algorithm to develop a novel subspace-based direction-of-arrival (DoA) estimation method that is resistant to faulty measurements or jamming. As demonstrated by numerical experiments, the pro...

We propose shiny analysis framework accompanied with makeup deterioration using normalized facial images (MaVIC and the corresponding makeup-deteriorated data sets). These images are analyzed and reconstructed based on principal component analysis (PCA) and then the differential ones between the reconstruction images with different numbers of PCA components can be generated. The shiny Eigen-fac...

In this paper, in order to categorize ORL database face pictures, principle Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) methods by using Elman neural network and Support Vector Machine (SVM) categorization methods are used. Elman network as a recurrent neural network is proposed for modeling storage systems and also it is used for reviewing the effect of using PCA nu...

This paper uses Multi-Layer Perceptron Neural Network (MLPNN) for comparing the linear dimensionality reduction techniques (DRTs) for fault diagnosis in rolling element bearing (REB).The vibration signals from normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired under different radial loads and ...

Principal Component Analysis (PCA) is one of the most valuable results oriented techniques of applied linear algebra. The minimum effort of PCA provides a roadmap for reducing a complex data set to a lower dimension to reveal the sometimes hidden, simplified structure that often underlie it. Bioelectrical signals express the electrical functionality of different organs in the human body. The El...

A careful comparison between the Principal Component Analysis (PCA) and the Hankel Total Least Squares (HTLS) based methods for estimating the resonances in sets of MR spectra is presented. After a description of the methods and their relationships, we compare their performance on simulated data sets of Magnetic Resonance Spectroscopy (MRS) signals and discuss their advantages and limitations. ...

Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...