Hrishikesh Deshpande

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Application level multicast schemes have traditionallybeen evaluated with respect to the eeciency penalties incurred in migrating the multicast functionality from the network layer to the application layer. We argue that the current performance measures, and therefore design strategies, are incomplete as they do not consider transience of peers. The routers(More)
Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output(More)
The manual delineation of Multiple Sclerosis (MS) lesions is a challenging task pertaining to the requirement of neurological experts and high intra-and inter-observer variability. It is also time consuming because large number of Magnetic Resonance (MR) image slices are needed to obtain 3-D information. Over the last years, various models combined with(More)
BACKGROUND The development of arterial spin labeling methods has allowed measuring regional cerebral blood flow (rCBF) quantitatively and to show the pattern of cerebral activity associated with any state such as a sustained pain state or changes due to a neurotropic drug. METHODS The authors studied the differential effects of three pain conditions in 10(More)
PURPOSE In healthy control subjects certain brain regions of interest demonstrate increased regional cerebral blood flow in response to painful stimuli. We examined the effect of bladder distension on arterial spin label functional magnetic resonance imaging measures of regional cerebral blood flow in regions of interest in subjects with interstitial(More)
— Today, internet and web services have become an inseparable part of our lives. Hence, ensuring continuous availability of service has become imperative to the success of any organization. But these services are often hampered by constant threats from myriad types of attacks. One such attack is called distributed denial of service attack that results in(More)
This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and(More)
Sparse representations allow modeling data using a few basis elements of an over-complete dictionary and have been used in many image processing applications. We propose to use the sparse representation and dictionary learning paradigm to automatically segment Multiple Sclerosis (MS) lesions from longitudinal MR data. The dictionaries are learned for the(More)