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.

Oxygen vacancy defect engineering using atomic layer deposited HfAlOx in multi-layered gate stack

Abstract: This work evaluates the defects in high quality atomic layer deposited (ALD) HfAlOx with extremely low Al (<3% Al/(Al + Hf)) incorporation in the Hf based high-k dielectrics. The defect activation energy estimated by the high temperature current voltage measurement shows that the charged oxygen vacancies, V+/V2+, are the primary source of defects in these dielectrics. When Al is added in HfO2, the V+ type defects with a defect activation energy of Ea ∼ 0.2 eV modify to V2+ type to Ea ∼ 0.1 eV with reference to the Si conduction band. When devices were stressed in the gate injection mode for 1000 s, more V+ type defects are generated and Ea reverts back to ∼0.2 eV. Since Al has a less number of valence electrons than do Hf, the change in the co-ordination number due to Al incorporation seems to contribute to the defect level modifications. Additionally, the stress induced leakage current behavior observed at 20 °C and at 125 °C demonstrates that the addition of Al in HfO2 contributed to suppressed trap gen...

An Axis Based Mean Filter for Removing High-Intensity Salt and Pepper Noise

Abstract: In this work, we propose an Axis Based Mean Filtering (ABMF) method for removing high-intensity Salt and Pepper Noise from gray-scale images. The proposed method applies the concept of mean filter and uses only the terminal pixels within a window along a specific axis to predict the value of the central noisy pixel. The ABMF considers a fixed window size of 3×3. If the central pixel of the window is noisy, it tries to identify a straight line (referred to as axis) within the window passing through the central pixel such that the pixels on either side of the line are non-noisy. If such an axis is found, the noisy pixel is replaced by the mean of the pixels on either end of the axis. However, if such a line does not exist, the noisy pixel is replaced by the mean of all the non-noisy pixels within the window. Experimental results over a set of 34 images exhibit that the proposed ABMF outperforms the existing algorithms by 58% and 29% in terms of mean SSIM and mean PSNR respectively for noise-intensities ranging from 10%-90%.

Estimation of E-waste Generation—A Lifecycle-Based Approach

Abstract: The problem of e-waste disposal is a very well-known fact, and its generation is increasing exponentially every year In 2015, 54 million tons of e-waste was generated, whereas it has been predicted that around 50 million tons of e-waste will be generated worldwide by 2018, by the UN report Another source predicts that e-waste generation will be 72 million tons by 2017 This anomaly exists due to the different methodologies adopted in prediction of e-waste The most common method used so far to calculate the amount of e-waste generated is as follows The amount of EEE sold by manufacturers is collected first The average lifespan of an EEE is known Thus, applying the average lifespan of the EEE on the amount sold per year, the amount of e-waste is calculated However, this method is not free from flaws since a sizable portion of the EEE, once the average lifespan is over, does not directly become e-waste They land in the second-hand market and are resold, and are again used for more number of years Hence, the process of becoming e-waste for these recycled products is delayed Once an EEE leaves the Original Equipment Manufacturer (OEM), the lifecycle of an EEE begins After a certain time of use, the user may discard it for several reasons, which then becomes Used EEE (UEEE) One can exchange this UEEE for a newer and upgraded models (or cash) via authorized or unauthorized resellers, in which case also the UEEE lands up in the second-hand market The original user can also discard the product completely so that it lands up as e-waste From the e-waste, precious metals are recovered through recycling process and the discarded parts mostly end up as landfill In this paper, a model has been proposed based on the lifecycle of EEE Based on this model, an attempt has been made to predict the amount of e-waste generation in India Standard data available from the data bank of EU has been used for this purpose The work has been carried out using Vensim software The results have been compared with the real-life data

A Model for Distributed Processing and Analyses of NGS Data under Map-Reduce Paradigm

Abstract: Massively parallel sequencing technique, introduced by NGS technology, has resulted in an exponential growth of sequencing data, with greatly reduced cost and increased throughput. This huge explosion of data has introduced new challenges in regard to its storage, integration, processing, and analyses. In this paper, we have proposed a novel distributed model under Map-Reduce paradigm to address the NGS big data problem. The architecture of the model involves Map-Reduce based modularized approach involving three different phases that support various analytical pipelines. The first phase will generate detailed base level information of various individual genomes, by granulating the alignment data. The other two phases independently process this base level information in parallel. One of these two phases will provide an integrated DNA profile of multiple individuals, whereas the other phase will generate contigs with similar features in an individual. Each of these three phases will generate a repository of genomic information that will facilitate other analytical pipelines. A simulated and real experimental prototypes has been provided as results to show the effectiveness of the model and its superiority over a few existing popular models and tools. A detailed description of the scope of applications of this model is also included in this article.