Electrophoretic deposition of carbon nanotubes on silicon substrates

This dissertation research describes the feasibility study and investigation of Electrophoretic Deposition (EPD) of carbon nanotubes (CNTs) for applications in semiconductor research. In recent years, the EPD technique has been considered as an economical, room temperature, solution based wet coating technique for thin and thick CNT films on arbitrary substrates. In this study, fabrication of uniform coatings of acid-treated CNTs has been pursued on bare silicon substrates by EPD from aqueous and organic suspensions. Research endeavors are extended to examine EPD of CNTs on silicon substrates with various surface coatings such as metal (aluminum), insulator layers (silicon dioxide and silicon nitride) and self-assembled polar organosilane (APTES) molecules. Microstructural imaging, spectroscopic analysis and characterization of the morphology of the CNT films have also been reviewed in relation to the deposition parameters such as inter-electrode electric field, deposition duration and APTES concentration. For research and development involving advanced spectroscopic analysis, Surface Enhanced Raman Spectroscopy (SERS) studies have been conducted on horizontally aligned EPD fabricated porous CNT networks coated with silver nanoparticles (AgNPs). The acquired Raman spectra of AgNP-CNT hybrid nanostructures display significant enhancement in the Raman intensity values of Rhodamine6G (R6G) analyte by several orders of magnitude with respect to the reference sample. Improvement in the Raman signals has pushed the detection limit to as low as 1 × 10^-12 M. The experimental results, reported in this dissertation, thus establish the novelty of EPD in the fabrication of the AgNP coated porous CNT substrate for routine SERS analysis of different target analytes

Trust Management Model for Cloud Computing Environment

Software as a service or (SaaS) is a new software development and deployment paradigm over the cloud and offers Information Technology services dynamically as "on-demand" basis over the internet. Trust is one of the fundamental security concepts on storing and delivering such services. In general, trust factors are integrated into such existent security frameworks in order to add a security level to entities collaborations through the trust relationship. However, deploying trust factor in the secured cloud environment are more complex engineering task due to the existence of heterogeneous types of service providers and consumers. In this paper, a formal trust management model has been introduced to manage the trust and its properties for SaaS in cloud computing environment. The model is capable to represent the direct trust, recommended trust, reputation etc. formally. For the analysis of the trust properties in the cloud environment, the proposed approach estimates the trust value and uncertainty of each peer by computing decay function, number of positive interactions, reputation factor and satisfaction level for the collected information.Comment: 5 Pages, 2 Figures, Conferenc

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide better visual explanations of CNN model predictions, in terms of better object localization as well as explaining occurrences of multiple object instances in a single image, when compared to state-of-the-art. We provide a mathematical derivation for the proposed method, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the corresponding class label. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ provides promising human-interpretable visual explanations for a given CNN architecture across multiple tasks including classification, image caption generation and 3D action recognition; as well as in new settings such as knowledge distillation.Comment: 17 Pages, 15 Figures, 11 Tables. Accepted in the proceedings of IEEE Winter Conf. on Applications of Computer Vision (WACV2018). Extended version is under review at IEEE Transactions on Pattern Analysis and Machine Intelligenc

A Reflection on the Scope of Feminist Pedagogy in Indian Tertiary Education

The Indian National Education Policy seek to restructure and standardize the higher education institution (HEI) curriculum and look forward to a futuristic, meritocratic, equitable, and multidisciplinary pedagogy. Present work critically analyses the scope of Feminist Pedagogy in the Indian higher education scenario, in this regard. It would try to offer an active participatory teaching-learning strategy to dismantle the existing gender hierarchy and oppression in Indian HEIs

Enhanced Regularizers for Attributional Robustness

Deep neural networks are the default choice of learning models for computer vision tasks. Extensive work has been carried out in recent years on explaining deep models for vision tasks such as classification. However, recent work has shown that it is possible for these models to produce substantially different attribution maps even when two very similar images are given to the network, raising serious questions about trustworthiness. To address this issue, we propose a robust attribution training strategy to improve attributional robustness of deep neural networks. Our method carefully analyzes the requirements for attributional robustness and introduces two new regularizers that preserve a model's attribution map during attacks. Our method surpasses state-of-the-art attributional robustness methods by a margin of approximately 3% to 9% in terms of attribution robustness measures on several datasets including MNIST, FMNIST, Flower and GTSRB.Comment: 15 pages, 18 figures, Accepted at AAAI 202

A comparative study between dexmedetomidine and propofol for maintaining depth of anesthesia in elective craniotomy: a prospective randomized double blind study

Background: The objective of present study was to assess the efficacy of dexmedetomidine over propofol in maintaining depth of anesthesia in patients undergoing elective craniotomy.Methods: Ninety patients of American Society of Anaesthesiologists (ASA) physical status 1 or 2, of either sex, with Glasgow Coma Score (GCS) 14 or 15, scheduled for elective craniotomy, were allocated in two groups, Group D and Group P. Each group consisted of 45 patients. Anesthesia was induced with propofol and maintained with nitrous oxide in oxygen, atracurium and intermittent fentanyl. Patients in Group D received continuous infusion of dexmedetomidine 0.4 µg/kg/hour which was started after induction and stopped after closure of dura in and patients in Group P received continuous infusion of propofol 100 µg/kg/min in same manner. Heart Rate (HR), mean arterial pressure (MAP), and bispectral index (BIS) were recorded and compared at specific time points which are known to have hemodynamic alterations throughout the intraoperative period.Results: Dexmedetomidine was comparable and even better (after intubation p 0.02, head pin fixation p 0.00, opening of dura p <0.00) than propofol in maintaining depth of anesthesia. It also attenuated HR and MAP at intubation, head pin fixation, skin incision, making of burr hole, opening of dura and at extubation (p 0.00). But Ramsay sedation score of patients after extubation in both groups did not differ significantly (p 0.36). No patient had recall.Conclusions: Dexmedetomidine is comparable with propofol in maintaining depth of anesthesia during elective craniotomy. It can be used as a sole anesthetic agent during craniotomy.