Grain Refinement in Aluminum Alloys by Acoustic Cavitation Phenomena

In this article, ultrasonic method of transmitting forced vibrations to solidifying aluminum-alloy melts is presented. In the presence of well developed cavitation situations, a fine and homogeneous microstructure has been observed throughout the irradiated ingots. The effects produced when high-intensity sonic or ultrasonic waves are propagated through molten metals can be listed under three main categories: grain refinement, dispersive effects, and degassing resulting in reduced porosity. It has been found that vibrations of a mechanical origin are effective in increasing fluidity by as much as a factor of three and consequently, favorably influence the mold-filling ability of aluminum alloys. There appear to be two distinct views regarding the mechanism, which may be explained by the cavitation effects and the influence of the fluid-flow phenomena

QoS Preserving Topology Advertising Reduction for OLSR Routing Protocol for Mobile Ad Hoc Networks

http://citi.insa-lyon.fr/wons2006/index.htmlMobile ad hoc networks (MANET) are formed by mobile nodes with a limited communication range. Routing protocols use a best effort strategy to select the path between a source and a destination. Recently, mobile ad hoc networks are facing a new challenge, quality of service (QoS) routing. QoS is concerned with choosing paths that provide the required performances, specified mainly in terms of the bandwidth and the delay. In this paper we propose a QoS routing protocol. Each node forwards messages to their destination based on the information received during periodically broadcasts. It uses two different sets of neighbors: one to forward QoS compliant application messages and another to disseminate local information about the network. The former is built based on 2-hop information knowledge about the metric imposed by the QoS. The latter is selected in order to minimize the number of sent broadcasts. We provide simulation results to compare the performances with similar QoS protocols

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes

Texture spectrum coupled with entropy and homogeneity image features for myocardium muscle characterization

People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease. Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. Texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The noise sensitivity of the fuzzy c–means classifier is overcome by using the image features. The proposed method is tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies. The results established that the entropy-based features provided superior clustering results compared to homogeneity

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature

Viscosity of eutectic silumin alloy in ultrasonic field and estimation of melting temperature

733-737In this paper, the viscous flow phenomenon in molten eutectic silumin alloy and the viscosity in the vicinity of its melting temperature was analyzed. The second derivative of the activation energy for viscous flow with respect to temperature was found to show discontinuity in the vicinity of the melting temperature. In order to highlight the effect of the ultrasonic waves on the viscosity and the activation energy for viscous flow, the experiments were carried out under similar conditions both with and without ultrasound. Our estimations indicate that the break of the second derivative versus temperature could be observed corresponding to 863 K in the presence of ultrasonic field and 893 K without ultrasound

The Romanian Modern University in the Frame of the Academic Profession and Governance

AbstractTo develop education in a new economic context calls for new knowledge and new modalities of political governing. This paper explores how the academic profession in Romania perceives, interprets and interacts with changes in the socio-economic environment and in the organisational structure of higher education systems and higher education institutions. The analysis is based on data gathered to accomplish a cross study on the influence of the European governance in the countries involved in the EuroHESC project EUROAC – The Academic Profession in Europe: responses to societal challenges (Germany, Austria, Switzerland, Ireland, Romania, and Croatia as Principal Investigators and Finland and Poland as Associated Partners). In our analysis we focused on the themes covering hierarchy, external decision maker (stakeholder) and management. The data are based on the results from an online questionnaire which was conducted in Romania between November 2009 and June 2010