Rajendu Mitra

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Nakagami-m distribution is well known for its ability to model a number of probability density functions, be it symmetric or asymmetric. Many Maximum Likelihood parameter estimation techniques for this distribution have been proposed that use estimated higher order moments of the data. However, the required large amount of data may not always be available.(More)
The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work shows that voltage time series measurements collected from customer smart meters exhibit correlations that(More)
The connectivity model of a power distribution network can easily become outdated due to system changes. Maintaining and sustaining an accurate connectivity model is a key challenge for most distribution utilities today. This work presents novel analytics techniques that can infer the connectivity model from measurements already available from a(More)
  • Rajendu Mitray, Prashanth L Ay, Shalabh Bhatnagary, Prasad H Ly, Rajendu Mitra
  • 2011
We study the problem of sleep-wake scheduling in a wireless sensor network for quickest intrusion detection. We develop a Q-learning based algorithm with function approximation to solve this problem. Our algorithm incorporates feature based representations and includes a novel feature selection scheme that balances the conflicting objectives of maximizing(More)
The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work focuses on inferring customer to phase connectivity using machine learning techniques. Using voltage time(More)
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