Effect of polyethylenimine addition and washing on stability and electrophoretic deposition of Co3O4 nanoparticles

In this work, the parameters of cobalt oxide suspension such as conductivity, zeta potential, particle size, stability, and finally the electrophoretic behavior of particles in the absence and presence of polyethylenimine (PEI) in acetone medium were investigated. Also, the effects of washing on the stability and electrophoretic deposition of Co₃O₄ were studied. Characterization of the obtained layer by optical microscopy revealed that there was no deposition in the suspension without PEI, while a uniform layer was formed in the presence of PEI additive. Scanning electron microscopy (SEM) results confirmed the uniformity of layer obtained in acetone using PEI additive. Moreover, SEM results demonstrated that more porous microstructures were obtained at longer deposition durations. The difference in the porosity of the layers, as indicated by the SEM micrographs, is attributed to increase in the deposition time.     

Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

A joint correlation noise estimation and decoding algorithm for distributed video coding

Multimedia Tools and Applications - Distributed video coding is relatively a novel video coding paradigm that enables a lower complex video encoding compared to conventional video coding schemes,...     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

Landsat ETM+ imaging for mineral potential mapping: application to Avaj area,Qazvin, Iran

The Avaj area is located in the northwestern part of Iran, on the boundary between the Orumieh-Dokhtar and Sanandaj-Sirjan geological zones. It corresponds to two different geological sub-zones:Abe-Garm, to the north, which is related to the volcanic belt of Orumieh-Dokhtar; and the Razan sub-zone, to the south, which is related to the Sanandaj-Sirjan zone. Lithological aspects of different rock units indicate the presence of four distinct volcanic groups of andesite, basalt, tuff, and dacite. The present research utilizes various Landsat Enhanced Thematic Mapper Plus (ETM+) image-processing techniques, including false colour composite images, colour composite ratio images, and standard principal component (PC) image analysis on six bands and colour composite selective PC images, filtering and supervised classification. Nonetheless, colour composite selective images may be the most reliable method for enhancement of areas with hydrothermal alteration. All the techniques used clearly show the pervasive alteration of kaolinite-argillite in the area. These alterations are mainly related to the andesite, and tuffs have the same trend as fault directions.     

A Unified Approach to Similarity Measures Between Intuitionistic Fuzzy Sets

Two general classes of similarity measures between intuitionistic fuzzy sets (hereafter IFSs) are introduced. Some properties of these measures are investigated with respect to some well‐known reasonable axioms. It is shown that several similarity measures between IFSs introduced elsewhere can be expressed by these definitions as special cases. Some illustrative and practical examples from the areas of pattern recognition, fuzzy clustering analysis, and decision making will be used to investigate the performance of the proposed measures with respect to some common measures. The results indicate that these measures can provide a useful way for measuring the degree of similarity between IFSs and that the proposed approach performs well in pattern recognition, fuzzy clustering, and decision making.     

Sparse representation based facial image compression via multiple dictionaries and separated ROI

Multimedia Tools and Applications - Constant increasing of visual information necessitates most efficient image compression schemes for saving storage space or reducing required transmission...     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

A blur-robust descriptor with applications to face recognition

Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition by a fusion of image-formation models and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then, under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a prespecified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard data sets.     

Towards quantum reversible ternary coded decimal adder

Quantum ternary logic is a promising emerging technology for the future quantum computing. Ternary reversible logic circuit design has potential advantages over the binary ones like its logarithmic reduction in the number of qudits. In reversible logic all computations are done in an invertible fashion. In this paper, we propose a new quantum reversible ternary half adder with quantum cost of only 7 and a new quantum ternary full adder with a quantum cost of only 14. We termed it QTFA. Then we propose 3-qutrit parallel adders. Two different structures are suggested: with and without input carry. Next, we propose quantum ternary coded decimal (TCD) detector circuits. Two different approaches are investigated: based on invalid numbers and based on valid numbers. Finally, we propose the quantum realization of TCD adder circuits. Also here, two approaches are discussed. Overall, the proposed reversible ternary full adder is the best between its counterparts comparing the figures of merits. The proposed 3-qutrit parallel adders are compared with the existing designs and the improvements are reported. On the other hand, this paper suggested the quantum reversible TCD adder designs for the first time. All the proposed designs are realized using macro-level ternary Toffoli gates which are built on the top of the ion-trap realizable ternary 1-qutrit gates and 2-qutrit Muthukrishnan–Stroud gates.