Sainyam Galhotra

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Three patients with intractable epilepsy, two with brain tumors, and one with encephalitis were imaged with magnetic resonance (MR) and positron emission tomography (PET). MR data were used to construct a three-dimensional (3D) computer model of the brain surface depicting the precentral (movement), postcentral (sensation), left inferior frontal (speech),(More)
Image processing and volume rendering have been applied to magnetic resonance (MR) images to produce three-dimensional views of the surface of the brain. Four healthy volunteers and 17 patients with a variety of disorders underwent MR imaging of the head, and these images were processed and subjected to volume rendering. The resulting three-dimensional(More)
We have developed volumetric-rendering methods for visualizing multimodality, multivariable medical imaging dam This paper describes an integrated 3-D display of data from multiple cross-sectional imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), and reports a novel approach for displaying vascular(More)
Data from single 10-minute magnetic resonance scans were used to create three-dimensional (3-D) views of the surfaces of the brain and skin of 12 patients. In each case, these views were used to make a preoperative assessment of the relationship of lesions to brain surface structures associated with movement, sensation, hearing, and speech. Interactive(More)
We investigate the condition on transmission radius needed to achieve connectivity in duty-cycled wireless sensor networks (briefly, DC-WSNs). First, we settle a conjecture of Das et al. [2012] and prove that the connectivity condition on random geometric graphs (RGGs), given by Gupta and Kumar [1989], can be used to derive a weakly sufficient condition to(More)
The steady growth of graph data from social networks has resulted in widespread research in finding solutions to the influence maximization problem. Although, TIM [4] is one of the fastest existing algorithms, it cannot be deemed scal-able owing to its exorbitantly high memory footprint. In this paper, we address the scalability aspect – memory consumption(More)
The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI(More)
Monitoring the formation and evolution of communities in large online social networks such as Twitter is an important problem that has generated considerable interest in both industry and academia. Fundamentally, the problem can be cast as studying evolving sub-graphs (each subgraph corresponding to a topical community) on an underlying social graph – with(More)
The steady growth of data from social networks has resulted in wide-spread research in a host of application areas including transportation, health-care, customer-care and many more. Owing to the ubiquity and popularity of transportation (more recently) the growth in the number of problems reported by the masses has no bounds. With the advent of social(More)