Sharif Digital Repository

Thermal transport characteristics of electroosmotic flow of power-law fluids in the presence of pressure gradient through a slit microchannel are studied in this paper. Considering a fully developed flow with a constant wall heat flux as the boundary condition, the governing equations are numerically solved by means of the finite difference method. A complete parametric study is done in order to investigate the effects of different flow parameters on the thermal behaviors of the flow. The results show that the non-Newtonian characteristic of the fluid can influence the thermal behaviors of the flow by affecting the rate of heat convection and viscous dissipation; however, its influence... 

The influence of variable fluid properties on mixed electroosmotic and pressure driven flow of non-Newtonian fluids is investigated in this paper. The non-linear coupled energy and momentum equations are solved by means of an iterative numerical approach. The results reveal that the temperature dependent effects only become significant at very high values of the Debye-Hückel parameter in case of combined electroosmotic and pressure driven flow and could safely be neglected in other cases. It is observed that the physical properties variation lead to a higher mean velocity in case of pressure assisted flow and a lower mean velocity in case of pressure opposed flow. Furthermore, the... 

Electroosmotic flow of power-law fluids in the presence of pressure gradient through a slit is analyzed. After numerically solving the Poisson-Boltzmann equation, the momentum equation with electroosmotic body force is solved through an iterative numerical procedure for both favorable and adverse pressure gradients. The results reveal that, in case of pressure assisted flow, shear-thinning fluids reach higher velocity magnitudes compared with shear-thickening fluids, whereas the opposite is true when an adverse pressure gradient is applied. The Poiseuille number is found to be an increasing function of the dimensionless Debye-Hückel parameter, the wall zeta potential, and the flow behavior... 

Electroosmosis has many applications in fluid delivery at microscale, sample collection, detection, mixing and separation of various biological and chemical species. In biological applications, most fluids are known to be non-Newtonian. Therefore, the study of thermal features of non-Newtonian electroosmotic flow is of great importance for scientific communities. In the present work, the fully developed electroosmotic flow of power-law fluids in a slit microchannel is investigated. The related equations are transformed into non-dimensional forms and necessary changes are made to adapt them for non-Newtonian fluids of power-law model. Results show that depending on different flow parameters... 

This paper proposes an image watermarking scheme for copyright protection based on Independent Component Analysis (ICA). In the suggested scheme, embedding is carried out in cumulative form in spatial domain and ICA is used for watermark extraction. For extraction there is no need to access the original image or the watermark, and extraction is carried out only with two watermarked images. Experimental results show that the new method has better quality than famous methods [1], [2], [3] in spatial or frequency domain and is robust against various attacks. Noise addition, resizing, low pass filtering, multiple marks, gray-scale reduction, rotation, JPEG compression, and cropping are some... 

In this paper, a property for sparse recovery algorithms, called invariancy, is introduced. The significance of invariancy is that the performance of the algorithms with this property is less affected when the sensing (i.e., the dictionary) is ill-conditioned. This is because for this kind of algorithms, there exists implicitly an equivalent well-conditioned problem, which is being solved. Some examples of sparse recovery algorithms will also be considered and it will be shown that some of them, such as SL0, Basis Pursuit (using interior point LP solver), FOCUSS, and hard thresholding algorithms, are invariant, and some others, like Matching Pursuit and SPGL1, are not. Then, as an... 

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  

The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification, particularly hypothesis testing. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is closely studied. Findings are then employed to develop a 2D compressive classifier (2D-CC) for sparse images. Finally, theoretical results are validated within a realistic... 

Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using k-nearest neighborhood (k-NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-known spectral technique, shortcomings of k-NN for construction of the adjacency graph are first illustrated, and then a new criterion, namely k/K-nearest neighborhood (k/K-NN) is introduced to overcome these drawbacks. The proposed criterion involves finding the sparsest representation of each sample in... 

This paper presents a new transform domain to evaluate the goodness of fit of natural image data to the common Benford's Law. The evaluation is made by three statistical fitness criteria including Pearson's chi-square test statistic, normalized cross correlation and a distance measure based on symmetrized Kullback-Leibler divergence. It is shown that the serial combination of variance filtering and block 2-D discrete cosine transform reveals the best goodness of fit for the first significant digit. We also show that the proposed transform domain brings reasonable fit for the second, third and fourth significant digits. As an application, the proposed transform domain is utilized to detect... 

This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

The current double JPEG compression detection techniques identify whether or not an JPEG image file has undergone the compression twice, by knowing its embedded quantization table. This paper addresses another forensic scenario in which the quantization table of a JPEG file is not explicitly or reliably known, which may compel the forensic analyst to blindly reveal the recompression clues. To do this, we first statistically analyze the theory behind quantized alternating current (AC) modes in JPEG compression and show that the number of quantized AC modes required to detect double compression is a function of both the image’s block texture and the compression’s quality level in a fresh... 

This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

Recompression is a prevalent form of multimedia content manipulation. Different approaches have been developed to detect this kind of alteration for digital images of well-known JPEG format. However, they are either limited in performance or complex. These problems may arise from different quality level options of JPEG compression standard and their combinations after recompression. Inspired from semantic and perceptual analyses, in this paper, we suggest a part-level middle-out learning strategy to detect double compression via an architecturally efficient classifier. We first demonstrate that singly and doubly compressed data with different JPEG coder settings lie in a feature space... 

In this paper, an algorithm for image coding based on a sparse 3-dimensional Discrete Cosine Transform (3D DCT) is studied. The algorithm is essentially a method for achieving a sufficiently sparse representation using 3D DCT. The experimental results obtained by the algorithm are compared to the 2D DCT (used in JPEG standard) and wavelet db9/7 (used in JPEG2000 standard). It is experimentally shown that the algorithm, that only uses DCT but in 3 dimensions, outperforms the DCT used in JPEG standard and achieves comparable results (but still less than) the wavelet transform. © Springer-Verlag Berlin Heidelberg 2009  

In this paper a new geometrical approach for separating speech signals is presented. This approach can be directly applied to separate more than two speech signals. It is based on clustering the observation points, and then fitting a line (hyper-plane) onto each cluster. The algorithm quality is shown to be improved by using DCT coefficients of speech signals, as opposed to using speech samples. © Springer-Verlag 2004  

Based on a modified secant equation proposed by Li and Fukushima, we derive a stepsize for the Barzilai-Borwein gradient method. Then, using the newly proposed stepsize and another effective stepsize proposed by Dai et al. in an adaptive scheme that is based on the objective function convexity, we suggest a modified two-point stepsize gradient algorithm. We also show that the limit point of the sequence generated by our algorithm is first-order critical. Finally, our numerical comparisons done on a set of unconstrained optimization test problems from the CUTEr collection are presented. At first, we compare the performance of our algorithm with two other two-point stepsize gradient algorithms... 

This paper is a survey of semi-blind source separation approaches. Since Gaussian iid signals are not separable, simplest priors suggest to assume non Gaussian iid signals, or Gaussian non iid signals. Other priors can also been used, for instance discrete or bounded sources, positivity, etc. Although providing a generic framework for semi-blind source separation, Sparse Component Analysis and Bayesian ICA will just sketched in this paper, since two other survey papers develop in depth these approaches. © 2006 i6doc.com publication. All rights reserved  

In this paper, a nonparametric "gradient" of the mutual information is first introduced. It is used for showing that mutual information has no local minima. Using the introduced "gradient", two general gradient based approaches for minimizing mutual information in a parametric model are then presented. These approaches are quite general, and principally they can be used in any mutual information minimization problem. In blind source separation, these approaches provide powerful tools for separating any complicated (yet separable) mixing model. In this paper, they are used to develop algorithms for separating four separable mixing models: linear instantaneous, linear convolutive, post...