Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Influence of oxygen partial pressure on optical and structural properties of RF sputtered ZnO thin films

Abstract: In this paper we report a detailed investigation of ZnO thin film properties deposited on Si substrate at 400°C using RF sputtering. To reduce oxygen induced vacancies and interstitial defects in samples, variable oxygen flow rate during deposition followed by post growth annealing in oxygen ambient were carried out. Four samples were deposited under constant temperature condition but with variable oxygen partial pressure of 0%, 20%, 50% and 80% in Argon and Oxygen mixture, namely sample S1, S2 , S3 and S4 respectively. Deposited films were further annealed at 700, 800, 900 and 1000°C in oxygen ambient for 10s. Photoluminescence (PL) measurements carried at low temperature (18K) demonstrated near band edge emission peak of ZnO at 3.37eV. Increment in PL intensity was observed with increasing annealing temperature and a particular sample S4 annealed at 900 measured narrowest full width half maxima (FWHM) of ~0.1272eV. Defects peaks observed at lower energies were suppressed with increasing oxygen flow and post growth annealing, indicating improvement in film quality. From HRXRD measurement it was observed S4 sample annealed at 900°C has the highest peak intensity and narrowest FWHM compared to other samples, demonstrating the best crystalline property of annealed film at 900°C. Highest XRD peak intensity measured at 34.53° corresponds to (002) crystal orientation reveals that the films were highly caxis oriented. AFM results show increase in grain size with increasing oxygen flow and annealing temperature which ensures improvement in morphological properties of the film.

A novel SFLA based method for gene expression biclustering

Abstract: Form the time of its invention, microarray technology is continuously growing and has been taking major role in biological research. This technology generates huge amount of gene expression data for biological analysis. Parallel computation methods are required to find functional associations from this large amount of biological data. An unsupervised machine learning technique, clustering algorithm groups similar genes based on entire conditions. But normal clustering methods cannot find different cellular processes from gene expression data because a biological activity can start functioning in the presence of some specific conditions. So, biclustering techniques are used instead of normal clustering. Biclustering basically identifies a set of genes that are co-expressed for some specific experimental conditions. Here we introduce an improved shuffled frog leaping algorithm(SFLA) based approach to find biclusters. SFLA is a hybrid of evolutionary memetic algorithm and collective intelligence based particle swarm optimization algorithm. Also It has faster convergence speed. By applying the proposed algorithm on yeast (Saccharomyces cerevisiae) cell cycle dataset, large number of biologically significant biclusters are obtained, which are verified by gene ontology database, compared to other existing algorithms. Also the biclusters have small MSR value and large size.