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Charged particles are produced in huge amount in high energy and astro-particle physics experiments such as LHC experiments at CERN, Geneva and in proposed India Based Neutrino Observatory (INO). The identification and measurement of these charged particle set the physics achievable for these experiments. Resistive plate chambers (RPCs), the gaseous detectors which work on the principle of ionization...
In this work, we investigate the spatial diversity gain offered by on-body channels in indoor environments. Narrowband on-body channel measurements are conducted in a dual-link topology, to cover various scenarios with different link geometries and body movements. Cooperative dual-link communications with equal-power allocation scheme are evaluated based on the channel measurements. The simulated...
The reliability of electronic systems, used in nuclear power plants, is traditionally estimated with empirical databases such as MIL-HDBK-217, PRISM etc. These methods assign a constant failure rate to electronic devices, during their useful life. Currently, electronic reliability prediction is moving towards applying the Physics of Failure approach which considers information on process, technology,...
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance. Constructing high quality classifiers for such imbalanced training data sets is one of the major challenges in machine learning, since...
Discovering structures in streaming data is an important data mining task and has motivated design of several well known algorithms. However, in some applications, a higher level of analysis is desirable to reveal the set of dimensions which contribute heavily to the structures. In this paper, we propose an algorithm ISID (identifying structures with informative dimensions), which operates in the...
Recently ensemble classification has attracted serious attention of machine learning community as a solution for improving classification accuracy. The effect of the strategies for generating the members, combining the predictions and the size of the ensemble on the accuracy of the ensemble are of utmost interest to the researchers. In this paper, we propose and empirically evaluate a novel method...
Data in databases is the outcome of an underlying data generation process (dgp), which is a complex function of unknown number of parameters. A few of these parameters may be known and understood by the domain expert or the end-user. The trends hidden in the evolving databases evolve with time and are manifestations of the underlying dgp. We believe that one of the objectives of the KDD technology...
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