This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. A single-input (propofol) single-output (bispectral index (BIS)) model of the patient has been assumed for prediction. The performance of our previous strategy in drug dosing control has been improved to tackle inter-patient variability. A set of 12 patient models was studied and, in order to ensure the applicability of the proposed controller, gain adaptation in the controller is proposed. Preliminary studies have shown that due to static nonlinearity in the sigmoid curve of the patient model, feedback control is not feasible in the first part of the induction phase. Therefore, the control strategy applied in this study consists of controlling the effect site concentration (C e ) during the first phase and controlling BIS once the relation BIS/C e has been identified. The policy of switching and adapting the control strategies shows a good performance during the induction phase in simulation studies. Clinical tests have been scheduled at the Ghent University Hospital for the coming months.

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 × 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

No abstract

This paper is focused on the model identification of a Micro Air Vehicle (MAV) in straight steady flight condition. The identification is based on input-output data collected from flight tests using both frequency and time domain techniques. The vehicle is an in-house 40 cm wingspan airplane. Because of the complex coupled, multivariable and nonlinear dynamics of the aircraft, linear SISO structures for both the lateral and longitudinal models around a reference state were derived. The aim of the identification is to provide models that can be used in future development of control techniques for the MAV.