Application of Doehlert matrix to determine the optimal conditions of electrochemical treatment of tannery effluents

The oxidation of organic and inorganic pollutants present in tannery effluents has been realised by electrochemical way. The influence of the electrochemical reactor parameters was carried out by the use of Doehlert matrix. The obtained results have shown that the current intensity and the electrolysis time were the main influent parameters on the removal ratio of chemical oxygen demand (COD), total organic carbon (TOC), electrochemical oxidation of trivalent chromium and sulphite ions.     

Four‐neighborhood clique kernel: A general framework for Bayesian and variational techniques of noise reduction in magnetic resonance images of the brain

Several algorithms have been proposed in the literature for image denoising but none exhibit optimal performance for all range and types of noise and for all image acquisition modes. We describe a new general framework, built from four‐neighborhood clique system, for denoising medical images. The kernel quantifies smoothness energy of spatially continuous anatomical structures. Scalar and vector valued quantification of smoothness energy configures images for Bayesian and variational denoising modes, respectively. Within variational mode, the choice of norm adapts images for either total variation or Tikhonov technique. Our proposal has three significant contributions. First, it demonstrates that the four‐neighborhood clique kernel is a basic filter, in same class as Gaussian and wavelet filters, from which state‐of‐the‐art denoising algorithms are derived. Second, we formulate theoretical analysis, which connects and integrates Bayesian and variational techniques into a two‐layer structured denoising system. Third, our proposal reveals that the first layer of the new denoising system is a hitherto unknown form of Markov random field model referred to as single‐layer Markov random field (SLMRF). The new model denoises a specific type of medical image by minimizing energy subject to knowledge of mathematical model that describes relationship between the image smoothness energy and noise level but without reference to a classical prior model. SLMRF was applied to and evaluated on two real brain magnetic resonance imaging datasets acquired with different protocols. Comparative performance evaluation shows that our proposal is comparable to state‐of‐the‐art algorithms. SLMRF is simple and computationally efficient because it does not incorporate a regularization parameter. Furthermore, it preserves edges and its output is devoid of blurring and ringing artifacts associated with Gaussian‐based and wavelet‐based algorithms. The denoising system is potentially applicable to speckle reduction in ultrasound images and extendable to three‐layer structure that account for texture features in medical images. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 224–238, 2014     

Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications

Pattern Analysis and Applications - Thanks to the significant developments in healthcare industries, various types of medical data are generated. Analysing such valuable resources aid healthcare...     

Multi-orientation geometric medical volumes segmentation using 3D multiresolution analysis

Multimedia Tools and Applications - Medical images have a very significant impact in the diagnosing and treating process of patient ailments and radiology applications. For many reasons, processing...     

Unexploited Acacia cyanophylla seeds: potential food sources of ω6 fatty acids and antioxidants?

BACKGROUND: In order to investigate new sources of dietary phytochemicals, recent studies have focused on underexploited seeds. In this study the total lipid contents, fatty acid profiles and levels of soluble proteins, minerals and antioxidants in seeds from 12 Acacia cyanophylla ecotypes commonly grown in Tunisia were determined. RESULTS: Total lipids averaged 101.7 g kg^?1 on a dry weight basis. Linoleic (61.11–65.45% of total fatty acid content), oleic (19.67–22.85%) and palmitic (9.18–9.98%) acids were the principal fatty acids. Smaller proportions of stearic (1.49–1.82%), vaccenic (1.13–2.05%) and palmitoleic (0.34–0.58%) acids were also quantified. Proteins (by Kjeldahl assay) averaged 107.2 g kg^?1 on a dry weight basis. Total phenolics averaged 1.91 g gallic acid equivalent kg^?1 dry weight (DW) and total flavonoids averaged 0.40 g rutin equivalent kg^?1 DW. The free radical‐scavenging activity determined by 2,2‐diphenyl‐1‐picrylhydrazyl assay averaged 0.59 mmol L^?1 Trolox equivalent antioxidant capacity (TEAC), while that determined by 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) assay averaged 0.28 mmol L^?1 TEAC. CONCLUSION: The findings of this study confirm the presence of ω6 fatty acids at high levels in A. cyanophylla seeds. These metabolites could be used as such and/or extracted for the formulation of supplements and/or ingredients to provide a ratio close to the ideal for the ω3/ω6 balance. Copyright © 2012 Society of Chemical Industry     

Generalized Gaussian mixture models as a nonparametric Bayesian approach for clustering using class-specific visual features

Recently, there has been a growing interest in the problem of learning mixture models from data. The reasons and motivations behind this interest are clear, since finite mixture models offer a formal approach to the important problems of clustering and data modeling. In this paper, we address the problem of modeling non-Gaussian data which are largely present, and occur naturally, in several computer vision and image processing applications via the learning of a generative infinite generalized Gaussian mixture model. The proposed model, which can be viewed as a Dirichlet process mixture of generalized Gaussian distributions, takes into account the feature selection problem, also, by determining a set of relevant features for each data cluster which provides better interpretability and generalization capabilities. We propose then an efficient algorithm to learn this infinite model parameters by estimating its posterior distributions using Markov Chain Monte Carlo (MCMC) simulations. We show how the model can be used, while comparing it with other models popular in the literature, in several challenging applications involving photographic and painting images categorization, image and video segmentation, and infrared facial expression recognition.     

Iterative technique for analysis and design of circular leaky‐wave antenna for the 2.45‐GHz radio‐frequency identification applications

This paper suggests a new miniaturized antenna for radio‐frequency identification (RFID) applications of 2.45 GHz. Our structure consists of a circular microstrip patch antenna which incorporates two concentric annular slots is printed on a multilayer substrates. The bandwidth, one of the most important characteristics of antenna, can be significantly improved by using a multilayer dielectric configuration. we aim by this study to show that the effect of radiating structure loaded by annular rings for the patch size reduction as well as the advantage of this structure being to create a circular polarisation in the direction of maximal radiation pattern. The wave concept iterative procedure is used to analyse this new antenna. Using the proposed procedure, less computing time and memory are needed to calculate the electromagnetic parameters of our design. The validation of the results of our developed model was verified with known commercial software called “CST Microwave Studio Software” followed by an experimental test. According to the reached results, we can judge that our new design antenna is suitable for RFID applications in the 2.45 GHz band.