A new method to assess the velocity vector field in a beating heart
The estimation of left ventricle motion and deformation from a series of images has been an area of attention in medical image analysis, and still remains an open and challenging research problem. The proper motion tracking of the left ventricle wall can contribute to isolating the location and extent of ischemic or infarcted myocardium. We present a completely new methodology to automatically estimate velocity vector fields for a beating heart based on the study of the variation in frequency content in a time series of non-stationary images. A synthetic phantom validation and results obtained from cardiac SPECT images are also presented.
Research on wind turbine Operations & Maintenance (O&M) procedures is critical to the expansion of Wind Energy Conversion systems (WEC). In order to reduce O&M costs and increase the lifespan of the turbine, we study the application of Set-Valued Observers (SVO) to the problem of Fault Detection and Isolation (FDI) of wind turbines, by taking advantage of the recent advances in SVO theory for model invalidation. A simple wind turbine model is presented along with possible faulty scenarios. The SVO algorithm is built upon these dynamics, taking into account process disturbances, model uncertainty, and measurement noise. The FDI algorithm is assessed within a publicly available benchmark model, using Monte-Carlo simulation runs.