Heritage Institute of Technology

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

An End-to-End Deep Learning Framework for Super-Resolution Based Inpainting

Abstract: Image inpainting is an extremely challenging and open problem for the computer vision community. Motivated by the recent advancement in deep learning algorithms for computer vision applications, we propose a new end-to-end deep learning based framework for image inpainting. Firstly, the images are down-sampled as it reduces the targeted area of inpainting therefore enabling better filling of the target region. A down-sampled image is inpainted using a trained deep convolutional auto-encoder (CAE). A coupled deep convolutional auto-encoder (CDCA) is also trained for natural image super resolution. The pre-trained weights from both of these networks serve as initial weights to an end-to-end framework during the fine tuning phase. Hence, the network is jointly optimized for both the aforementioned tasks while maintaining the local structure/information. We tested this proposed framework with various existing image inpainting datasets and it outperforms existing natural image blind inpainting algorithms. Our proposed framework also works well to get noise resilient super-resolution after fine-tuning on noise-free super-resolution dataset. It provides more visually plausible and better resultant image in comparison of other conventional and state-of-the-art noise-resilient super-resolution algorithms.

Role of Metabolic Risk Factors, Family History, and Genetic Polymorphisms (PPARγ and TCF7L2) on Type 2 Diabetes Mellitus Risk in an Asian Indian Population.

Abstract: Introduction Women with family history of diabetes (FHD) are at significantly increased risk of developing gestational diabetes mellitus which may eventually lead to type 2 diabetes mellitus (T2DM) in later life. Objective This study investigates the role of FHD on metabolic markers and gene polymorphisms and hence on T2DM susceptibility in nondiabetic pregnant women and the subsequent risks in their newborns. Materials and methods The present study was conducted on 200 healthy (nondiabetic and normotensive) adult Asian Indian women, including 100 with and 100 without FHD, living in and around Kolkata, India. During the gestational period, they were studied twice and followed up till delivery. During delivery, both mothers' venous blood and cord blood were collected to estimate serum CRP, glucose, and lipid profiles of the respective mothers and their newborns. Genotyping of PPARγ and TCF7L2 polymorphisms was done from these blood samples. Results A comparison of the metabolic variables among the subjects with and without FHD revealed significant differences among them. We also found close relationship between mothers and their newborn babies in terms of both PPARγ (rs1801282) C/G and TCF7L2 (rs7903146) C/T polymorphisms. More specifically, genotyping results for mothers with FHD and their newborn babies showed high concordance in inheritance of alleles: (i) for PPARγ via the risk allele G (74.0%) which is carried over to the newborn babies (64.5%) and (ii) for TCF7L2 via the risk allele T (73.0%) which is carried over to the newborn babies (68.5%). Conclusion This study leads to the conclusion that Asian Indian women population based in Kolkata, India, are ethnically and genetically predisposed to the risk factors of diabetes through FHD, which is reflected in their gestational phase, and it has a significant implication on their birth outcomes.

Influence of GaN layer to AlGaN layer width Ratio on Analog Performance of an Underlapped DG AlGaN/GaN based MOS-HEMT

Abstract: This paper aims at exploring the changes in analog performance of a symmetric Underlapped Double-Gate hetero- junction based AlGaN/GaN MOS-HEMT device, with Hafnium oxide as the high-k dielectric Gate material, on varying the GaN layer to AlGaN layer width ratio. The variations have been studied for different ratios, where the ratios have been varied by individually varying one of the GaN and AlGaN layer widths at a time, while keeping the other constant. The analog performance variations have been studied on Drain current (I d ), Transconductance (g m ), and Transconductance Generation Factor (g m / I d ). Studies show that the GaN layer to AlGaN layer width ratio greatly determines the performance of a given device, manifested in significant variations in threshold voltage of device, on-state currents and efficient methods of performance enhancement.

A novel locally guided genome reassembling technique using an artificial ant system

Abstract: DNA reassembling is an NP-hard problem (Brun, Theor Comput Sci 395:31---46, 2008; Medvedev et al 2007; Ma and Lombardi 2008). The present article presents a locally guided global learning system to solve the problem of genome reassembling. We have used a reference DNA sequence which is 99 % similar to an unknown DNA sequence. Two different sequences from the same organism generally have around 99 % similarity (Wei et al 2007). We have considered different DNA sequences from NCBI website (http://www.ncbi.nlm.nih.gov). Then we have simulated the tasks of cloning the sequence, followed by shearing the clones to a number of short reads. In our algorithm, we have introduced a new concept in the task of DNA reassembling using Ant Colony Optimization, where pheromone concentration is proportional to the score of assembled DNA fragments with some known reference sequences within the same organism. Unlike local overlapping, we have used here local alignment score of short reads with some known local reference region as the heuristic information. The result shows that our algorithm is capable of reassembling at par with the state-of-the-art. DNA reassembling techniques may need a massive parallel computation and huge memory space (Kurniawan et al 2008) because of size ~109bp of DNA sequences of mammals (Miller et al, Genomics 95:315---327, 2010; Blazewicz et al, Comput Biol Chem 33:224---230, 2009; Butler et al, Genome Res 18:810---820, 2008; Joshi et al 2011; Stupar et al, Arch Oncol 19:3---4, 2011; Quail et al, BMC Genomics 13:1471---2164, 2012), and ACO is inherently concurrent in nature (Dorigo and Stutzle 2004). Due to lack of appropriate computational resources, we had to confine ourselves to deal with the sequences of length up to ~105bp. We have considered 22 sequences of different organism, including Homo sapiens BRCA1 (127429bp) gene. For large sequences, we have applied hierarchical BAC-by-BAC sequencing (Fig. 2) (Myers, Comput Sci Eng 1:33---43, 1999), to stitch the individual segments to retrieve the original DNA sequence.