Vaibhav Kulkarni

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We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space.(More)
Location-based services today, exceedingly depend on user mobility prediction, in order to push context aware services ahead of time. Existing location forecasting techniques are driven by large volumes of data to train the prediction models in a centralised server. This amounts to considerably long waiting times before the model kicks in. Disclosing highly(More)
Mobility prediction is becoming one of the key elements of location-based services. In the near future, it will also facilitate tasks such as resource management, logistics administration and urban planning. To predict human mobility, many techniques have been proposed. However, existing techniques are usually driven by large volumes of data to train user(More)
In recent years, we have witnessed a proliferation of wireless technologies and devices operating in the unlicensed bands. The resulting escalation of wireless demand has put enormous pressure on available spectrum. This raises a unique set of communication challenges, notably co-existence, Cross Technology Interference (CTI), and fairness amidst high(More)
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