On optimizing edge connectivity of product graphs

This work studies the super edge connectivity and super restricted edge connectivity of direct product graphs, Cartesian product graphs, strong product graphs and lexicographic product graphs. As a result, sufficient conditions for optimizing the edge connectivity and restricted edge connectivity of these graphs are presented.     

基于人类视觉系统的立体图像质量评价方法

随着立体图像的大规模发展,很多应用场合需要能够迅速有效地完成对立体图像的质量评价工作,以便于后续应用,而对其进行主观质量评价在效率上很难满足要求。因此,提出了一种感知质量评价算法,并结合了一些人类视觉系统的特性。首先需要得到视差图,然后通过边界图和显著图来对视差图进行加权调整。接着使用Minkowski融合方法将加权后的视差图整合成感知分数。最后,使用多尺度分析来得到最终的感知质量分数。通过使用EPFL立体质量评价数据库来验证文中的立体图像感知质量评价算法。实验显示算法最后得到的客观分数和EPFL数据库中的主观分数具有高度的一致性和单调性,证明了文中的立体图像感知质量评价算法是有效的。     

Tracking a Screen and Detecting Its Rate of Change in 3-D Video Scenes of Multipurpose Halls

An automatic approach is presented to track a wide screen in a multipurpose hall video scene. Once the screen is located, this system also generates the temporal rate of change by using the edge detection based method. Our approach adopts a scene segmentation algorithm that explores visual features(texture) and depth information to perform efficient screen localization. The cropped region which refers to the wide screen undergoes salient visual cues extraction to retrieve the emphasized changes required in rate-ofchange computation. In addition to video document indexing and retrieval, this work can improve the machine vision capability in the behavior analysis and pattern recognition.     

Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization

Brain Magnetic Resonance (MR) images often suffer from the inhomogeneous intensities caused by the bias field and heavy noise. The most widely used image segmentation algorithms, which typically rely on the homogeneity of image intensities in different regions, often fail to provide accurate segmentation results due to the existence of bias field and heavy noise. This paper proposes a novel variational approach for brain image segmentation with simultaneous bias correction. We define an energy functional with a local data fitting term and a nonlocal spatial regularization term. The local data fitting term is based on the idea of local Gaussian mixture model (LGMM), which locally models the distribution of each tissue by a linear combination of Gaussian function. By the LGMM, the bias field function in an additive form is embedded to the energy functional, which is helpful for eliminating the influence of the intensity inhomogeneity. For reducing the influence of noise and getting a smooth segmentation, the nonlocal spatial regularization is drawn upon, which is good at preserving fine structures in brain images. Experiments performed on simulated as well as real MR brain data and comparisons with other related methods are given to demonstrate the effectiveness of the proposed method.     

Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images

This paper proposes a new anisotropic diffusion approach to remove the impulse noise and retain the fine details. The proposed approach contains two stages, the first stage detects the impulse noise, and the second stage removes the noisy pixel and retains the fine details of the original image. The Laplacian operator is used to fine-tune the image quality of the restored image in the anisotropic diffusion filter. The proposed approach is tested with PSNR, IEF, correlation factor, and NSER for different test images and the results are compared against existing algorithms. The simulation results show that the proposed approach gives better results than the existing denoising algorithms.     

Structure preserving model order reduction of shallow water equations

In this paper, we present two different approaches for constructing reduced‐order models (ROMs) for the two‐dimensional shallow water equation (SWE). The first one is based on the noncanonical Hamiltonian/Poisson form of the SWE. After integration in time by the fully implicit average vector field method, ROMs are constructed with proper orthogonal decomposition(POD)/discrete empirical interpolation method that preserves the Hamiltonian structure. In the second approach, the SWE as a partial differential equation with quadratic nonlinearity is integrated in time by the linearly implicit Kahan's method, and ROMs are constructed with the tensorial POD that preserves the linear‐quadratic structure of the SWE. We show that in both approaches, the invariants of the SWE such as the energy, enstrophy, mass and circulation are preserved over a long period of time, leading to stable solutions. We conclude by demonstrating the accuracy and the computational efficiency of the reduced solutions by a numerical test problem.     

The Applicability of Essential Oils in Different Stages of Production of Animal-Based Foods

Essential oils (EOs) have been used for centuries, and interest in these compounds has been revived in recent years. Due to their unique chemical composition as well as antimicrobial, immunostimulatory, anti-inflammatory and antioxidant properties, EOs are used in pharmacology, cosmetology and, increasingly, in animal breeding and rearing, and processing of animal raw materials. Essential oils have become a natural alternative to preservatives, taste enhancers and, most importantly, antibiotics, because the European Union banned the use of antibiotics in metaphylaxis in animal husbandry in 2006. In the animal production chain, EOs are used mainly as feed additives to improve feed palatability and increase feed intake, improve animal resistance and health status, and to prevent and treat diseases. Recent research indicates that EOs can also be applied to sanitize poultry houses, and they can be used as biopesticides in organic farming. Essential oils effectively preserve meat and milk and, consequently, improve the safety, hygiene and quality of animal-based foods. Novel technologies such as encapsulation may increase the bioavailability of EOs and their application in the production of food and feed additives.     

Advanced Materials in Cultural Heritage Conservation

Cultural Heritage is a crucial socioeconomic resource; yet, recurring degradation processes endanger its preservation. Serendipitous approaches in restoration practice need to be replaced by systematically addressing conservation issues through the development of advanced materials for the preservation of the artifacts. In the last few decades, materials and colloid science have provided valid solutions to counteract degradation, and we report here the main highlights in the formulation and application of materials and methodologies for the cleaning, protection and consolidation of works of art. Several types of artifacts are addressed, from murals to canvas paintings, metal objects, and paper artworks, comprising both classic and modern/contemporary art. Systems, such as nanoparticles, gels, nanostructured cleaning fluids, composites, and other functional materials, are reviewed. Future perspectives are also commented, outlining open issues and trends in this challenging and exciting field.     

A recursive color image edge detection method using Green's function approach

In this paper, an extended version of image edge detector using Green's function approach is proposed for detection of edges in the color vector space field. In the proposed method, the relationship between the Red, Green and Blue components is considered to design a differential operator for detection of edges in color images. By using the proposed operator, partial derivatives of all components of color image can simultaneously affect on the edge detection process. Therefore the proposed method can preserve the vector nature of color images during the edge processing stages. Also, the proposed method is compared both quantitatively and qualitatively with other color edge detectors. Experimental results show that the proposed method can efficiently preserve the edges even when the color images corrupted with different levels of noise.     

Effects of pressure and temperature on the survival rate of adherent A-172 cells

Preservation of cells under high pressure is an important alternative to cryopreservation. We studied the effect of temperature (4, 25, 37°C) and pressure (0.1–350 MPa) on the survival rate of A-172 glioblastoma cells. The survival rate was not changed by brief (10 min) pressurization of up to 150 MPa, but the survival rate began to decrease from 150 MPa, and most of the A-172 cells died when treated with over 200 MPa. Lengthy pressurization (4 days) at lower pressure (upto 20.1 MPa) without medium exchange showed complex results. The survival rate of cells preserved at 25°C showed two maxima at 1.6 and 20.1 MPa. After preservation, cells adhered and proliferated in the same way as normal cells when cultured at 37°C in a CO₂ incubator. The other two temperatures, 4° and 37°C, showed no maximum survival rate. Therefore, a high survival rate can be maintained with high pressure treatment.