An Adaptive Liquid Level Controller Using Multi Sensor Data Fusion

Multi-sensor data fusion using Bayesian programming : an automotive application

Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique

Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...

Multi-sensor Fusion through Adaptive Bayesian Networks

Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-...

Safe Multi-controller Adaptive Switching Using Closed-loop Data

Consider an interconnection of an unknown or partially known plant and a known stabilizing controller, and assume that some knowledge of the closed-loop system is available. Suppose, on the basis of that knowledge, the use of a new controller appears attractive. We present analysis and novel tests using a limited amount of experimental and possibly noisy data obtained from the existing closed-l...

Nonlinear 3-D Motion Simulation Using Multi-Sensor Data Fusion

It is a interesting topic of tracking a 3D object and estimating its 3-D motion in a multi-sensor observation environment. The motion with constant translation velocity and constant rotation rate is assumed and tracked by two separate cameras. Hence the problem considered here also involves a track-to-track fusion system in which twosensors are tracking the same target. Due to the nonlinear nat...