variations in biochemical features are extensive among cells. identification of marker that is specific for each cell is essential for following the differentiation of stem cell and metastatic growing. fourier transform infrared spectroscopy (ftir) as a biochemical analysis more focused on diagnosis of cancerous cells. in this study, commercially obtained cell lines such as human ovarian carcin...

Thresholding operators have been used successfully for denoising signals, mostly in the wavelet domain. These operators transform a noisy coefficient into a denoised coefficient with a mapping that depends on signal statistics and the value of the noisy coefficient itself. This paper demonstrates that a polynomial threshold mapping can be used for enhanced denoising of Principal Component Analy...

The visible image-based approach has long been considered the most powerful approach to facial emotion estimation. However it is illumination dependency. Under uncontrolled operating conditions, estimation accuracy degrades significantly. In this paper, we focus on integrating visible images with thermal image sequences for facial emotion estimation. First, to address limitations of thermal inf...

In this paper, we introduce a new method for steganalysis of grey-scale images. First, we analyzed the effect of various steganographic processes on the statistical properties of the image. So we extracted the optimal features from the images, which have high ability in make differentiated between two groups of normal and stego images. In this method, high order statistics in discrete wavelet t...

We consider the dimensionality-reduction problem for a contaminated data set in a very high dimensional space, i.e., the problem of finding a subspace approximation of observed data, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some outlying observations. We propose a High-dimension Robust Principal Component...

Principal Component Analysis (PCA) is the most widely used unsupervised dimensionality reduction approach. In recent research, several robust PCA algorithms were presented to enhance the robustness of PCA model. However, the existing robust PCA methods incorrectly center the data using the `2-norm distance to calculate the mean, which actually is not the optimal mean due to the `1-norm used in ...

In this paper we discuss some problem of Eigenfaces which is ignores in the previous work that is the question of which features are important for classification, and which are not. Eigenfaces seeks to answer this by using principal component analysis of the images of the faces. This analysis reduces the dimensionality of the training set, leaving only those features that are critical for face ...

Safeguarding the energy security is an important energy policy goal of every country. Assuring sufficient and reliable resources of energy at affordable prices is the main objective of energy security. Due to such reasons as special geopolitical position, terrorist attacks and other unrest in the Middle East, securing Iran’s energy demand and increasing her natural gas exports have turned into ...