Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

Dissolution and Delignification of Bamboo Biomass Using Amino Acid-Based Ionic Liquid

Abstract: In the present work, the dissolution of bamboo biomass was tested using a number of ionic liquids synthesized in laboratory. It was observed that one of the synthesized amino acid-based ionic liquids, namely 1-ethyl-3-methylimidazolium glycinate, was capable of dissolving the biomass completely. The dissolved biomass was then regenerated using a reconstitute solvent (acetone/water) and was characterized using Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. The results were compared to preconditioned bamboo biomass. The regenerated biomass was found to have a more homogenous macrostructure, which indicates that the crystalline form and structure of its cellulose has changed from type Ι to type ΙΙ during the dissolution and regeneration process.

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

Abstract: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP $$_{16 {\text{-}} \mathrm{ri}}$$ vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.