Dynamic Data-Driven Application Systems for Empty Houses, Contaminat Tracking, and Wildland Fireline Prediction

Towards policies for data insertion in dynamic data driven application systems: a case study sudden changes in wildland fire

We have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wildfire propagation. Our goal is to build a system that dynamically adapts to sudden changes in environmental conditions. For this purpose, we are building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. This dat...

University of Colorado at Denver and Health Sciences Center Wildland Fire Dynamic Data-Driven Application System

A Dynamic Data Driven Wildland Fire Model

We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather prediction, on-site data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth.

Design of robust carrier tracking systems in high dynamic and high noise conditions, with emphasis on neuro-fuzzy controller

The robust carrier tracking is defined as the ability of a receiver to determine the phase and frequency of the input carrier signal in unusual conditions such as signal loss, input signal fading, high receiver dynamic, or other destructive effects of propagation. An implementation of tight tracking can be understood in terms of adopting a very narrow loop bandwidth that contradict with the req...

Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction

This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction proces...