Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

The Impact of Land Cover and Land Use Change on the Indian Monsoon Region Hydroclimate

Abstract: The Indian Monsoon Region (IMR) has undergone notable land cover and land use change (LCLUC) in response to a dramatic increase in population and economic growth. While the changes in characteristics of LCLUC have been documented in various studies, the realistic representation of these changes within a dynamic hydroclimatic framework remains a challenge to the scientific community. This chapter highlights that representing LCLUC changes and feedbacks should be an integral component of short, medium, and long-range predictions over the IMR. Realistic representation of the land conditions can enhance model predictions at different scales. Yet, the LCLUC information is not well represented in atmospheric models, principally due to lack of details in land surface datasets and also due to the limitations of the model parameterizations. The need for developing gridded, high-resolution datasets and models that can ascribe to the changes and build off the sensitivity to the land changes emerge as a focus area. This chapter presents the nature of land cover feedbacks and their influence on the prediction of hydroclimatological extremes over the IMR. It is not intended as a comprehensive review, but more as a synthesis of contemporary studies that have addressed the issue of land surface responses and their feedback as part of land–atmospheric interactions over the region. Current understanding from studies on the impact of LCLUC over different scales of environmental processes and associated principles is also summarized with a particular focus on improving the hydroclimatic predictions over the IMR by considering coupled land–atmosphere interactions.

Deep Convolutional Neural Network for Double-Identity Fingerprint Detection

Abstract: Automatic human recognition using ubiquitous fingerprint sensors is the most widely used modality in modern biometric based security systems. The double-identity fingerprint is a fake fingerprint created by aligning two fingerprints for maximum ridge similarity and then joining them along an estimated cutline such that relevant features of both fingerprints are present on either sides of the cutline. The fake fingerprint containing the features of the criminal and his innocuous accomplice can be enrolled with an electronic machine readable travel document and later used to cross the automated border gates by claiming identity of the accomplice. In this letter, we have developed a deep convolutional neural network (CNN)-based patch-learning approach to estimate the cutline by training the network to identify and learn the pattern around the region of the joint fingerprint. This is a recent, new fingerprint alteration technique, and due to the unavailability of any such public database, we have generated a new database of 450 double-identity fingerprints. Experimental results show that the deep learning based approach is able to predict the cutline with an equal error rate, which is the best when compared with many other popular handcrafted features for double-identity fingerprint detection.

Assessing Possible Applications of Waste Organic Solid Substances as Carbon Sources and Biofilm Substrates for Elimination of Nitrate Toxicity from Wastewater

Abstract: This work aimed to find inexpensive alternatives that could serve both as carbon source and biofilm carrier for heterotrophic denitrification in treating nitrate contaminated waters. To asc...