This paper describes the design and evaluation of the PCA (Plastic Cell Architecture) cell, which implements a novel space allocation method. PCA is a dynamically reconfigurable architecture which exceeds the FPGA (Field Programmable Gate Array) in flexibility and generality. Circuit dynamically reconfiguration is achieved as administrators manage the heap areas. But, because objects operate an...

Principal Components Analysis (PCA) has been one of the most applied methods for face verification using only 2D information, in fact, PCA is practically the method of choice for applications of face verification in real-world. An alternative method to reduce the problem dimension is working with low resolution images. In our experiments three classifiers have been considered to compare the res...

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker’s PCA transformation matrix to reduce the correlation among the el...

The Active Appearance Model (AAM) is a widely used method for model based vision showing excellent results. But one major drawback is that the method is not robust against occlusions. Thus, if parts of the image are occluded the method converges to local minima and the obtained results are unreliable. To overcome this problem we propose a robust AAM fitting strategy. The main idea is to apply a...

Predicting business failure of listed companies is a hot topic because of the emergency of financial crisis in developed countries recently. The two classical statistical methods of multivariate discriminant analysis (MDA) and logistic regression (logit) have taken a key role in the area of business failure prediction (BFP). However, they are frequently criticized for the relative low predictiv...

This paper proposes a novel approach using two-dimensional principal component analysis (2D-PCA) and local direction descriptor for face recognition. The proposed method utilizes the transformed image obtained from local direction descriptor as the direct input image of 2D-PCA algorithms. The performance comparison was performed using principal component analysis (PCA) and Gabor-wavelets based ...

This work proposes a dynamic PCA modeling method for dynamical non-linear processes. This method uses fault free data to construct data matrix used to compute the correlation matrix and faulty system data in order to fix the dynamic PCA model parameters (the time-lag and the number of principal components). It is shown that the sensitivity of dynamic PCA-based fault detection depends on the par...

this paper presents a deterministic approach for performance assessment of different province’s road safety level at iran. a data envelopment analysis (dea) model considering undesirable input and output indices and a multivariate statistical method, principle component analysis (pca) are used in this paper, while previous studies do not use composite pca-dea method and undesirable input and ou...

Face recognition is an important research hotspot. More and more new methods have been proposed in recent years. In this paper, we propose a novel face recognition method which is based on PCA and logistic regression. PCA is one of the most important methods in pattern recognition. Therefore, in our method, PCA is used to extract feature and reduce the dimensions of process data. Afterwards, we...

Overview Purpose: To examine the use of targeted principal component analysis (PCA) with HPLC-charged aerosol detector data as a means of determining adulteration of olive oils. Method: Oil samples were analyzed as both untreated and hydrolyzed, using a C30 HPLC method and charged aerosol detection. PCA results were determined using peak area percent values of the acylglycerides and free fatty ...