Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

Novel V 2 O 5 -CeO 2 -TiO 2 -SO 4 2− nanostructured aerogel catalyst for the low temperature selective catalytic reduction of NO by NH 3 in excess O 2

Abstract: New ceria and sulfate co-modified V2O5-TiO2 aerogel catalysts were developed, using the one-step sol gel method associated with the supercritical drying process, for Diesel DeNOx technology. N2 adsorption-desorption, XRD, H2-TPR, NH3-TPD, Raman and DRUV-Vis spectroscopy were employed to probe the physico-chemical properties of TiO2, V2O5-TiO2, V2O5-CeO2-TiO2 and V2O5-CeO2-TiO2-SO42− aerogel materials. XPS was used to obtain further information about the oxidation states of the active sites on the surface of the novel V2O5-CeO2-TiO2-SO42− aerogel catalyst. The characterization results showed the successful synthesis of a new generation of well nanostructured aerogel catalysts with high surface area, large porosity and good thermal stability. V, Ce and SO42− actives species were found highly dispersed on TiO2 surface and their presence strongly influenced the surface acidity and the redox properties of the aerogel catalysts. Sulfate anions created strong acid sites and most probably contributed to the stabilization of V and Ce surface species at their 4 + and 3 + oxidation state, respectively. In the SCR-NO by NH3 under oxygen rich conditions, V2O5-TiO2 aerogel catalyst exhibited low NO conversions in 150–500 °C temperature range. The addition of cerium significantly increased the NO conversion at low temperature (220–400 °C). However, the simultaneous incorporation of cerium and sulfate has led to a novel V2O5-CeO2-TiO2-SO42− nanostructured aerogel catalyst with superior catalytic performances, at high temperature (450–500 °C), with respect to V2O5-WO3/TiO2 commercial one (EUROCAT).

Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain

Abstract: Recently, deep learning has been shown effectiveness in multimodal image fusion. In this paper, we propose a fusion method for CT and MR medical images based on convolutional neural network (CNN) in the shearlet domain. We initialize the Siamese fully convolutional neural network with a pre-trained architecture learned from natural data; then, we train it with medical images in a transfer learning fashion. Training dataset is made of positive and negative patch pair of shearlet coefficients. Examples are fed in two-stream deep CNN to extract features maps; then, a similarity metric learning based on cross-correlation is performed aiming to learn mapping between features. The minimization of the logistic loss objective function is applied with stochastic gradient descent. Consequently, the fusion process flow starts by decomposing source CT and MR images by the non-subsampled shearlet transform into several subimages. High-frequency subbands are fused based on weighted normalized cross-correlation between feature maps given by the extraction part of the CNN, while low-frequency coefficients are combined using local energy. Training and test datasets include pairs of pre-registered CT and MRI taken from the Harvard Medical School database. Visual analysis and objective assessment proved that the proposed deep architecture provides state-of-the-art performance in terms of subjective and objective assessment. The potential of the proposed CNN for multi-focus image fusion is exhibited in the experiments.