Biomedical subjects

S Devasia

Publications and source records attributed to S Devasia.

4 recordsLinked to original sources

PubMed2001

High-speed solution switching using piezo-based micropositioning stages.

Motion-induced vibration is a critical limitation in high-speed micropositioning stages used to achieve solution switching. Controlled rapid solution switching is used to study the fast activation and deactivation kinetics of ligand-gated ion-channel populations isolated in excised membrane patches--such studies are needed to understand fundamental mechanisms that mediate synaptic excitation and inhibition in the central nervous system. However, as the solution-switching speed is increased, vibration induced in the piezo-based positioning stages can result in undesired, repeated, ligand application to the excised patch. The article describes a method to use knowledge of the piezo-stage's vibrational dynamics to compensate for and reduce these unwanted vibrations. The method was experimentally verified using an open-electrode technique, and fast solution switching (100 micros range) was achieved.

Models, Neurological↗

PubMed1998

Reduced-order modeling for hyperthermia: an extended balanced-realization-based approach.

Accurate thermal models are needed in hyperthermia cancer treatments for such tasks as actuator and sensor placement design, parameter estimation, and feedback temperature control. The complexity of the human body produces full-order models which are too large for effective execution of these tasks, making use of reduced-order models necessary. However, standard balanced-realization (SBR)-based model reduction techniques require a priori knowledge of the particular placement of actuators and sensors for model reduction. Since placement design is intractable (computationally) on the full-order models, SBR techniques must use ad hoc placements. To alleviate this problem, an extended balanced-realization (EBR)-based model-order reduction approach is presented. The new technique allows model order reduction to be performed over all possible placement designs and does not require ad hoc placement designs. It is shown that models obtained using the EBR method are more robust to intratreatment changes in the placement of the applied power field than those models obtained using the SBR method.

Algorithms↗

PubMedDate not supplied

A comparison of reduced-order modelling techniques for application in hyperthermia control and estimation.

Reduced-order modelling techniques can make important contributions in the control and state estimation of large systems. In hyperthermia, reduced-order modelling can provide a useful tool by which a large thermal model can be reduced to the most significant subset of its full-order modes, making real-time control and estimation possible. Two such reduction methods, one based on modal decomposition and the other on balanced realization, are compared in the context of simulated hyperthermia heat transfer problems. The results show that the modal decomposition reduction method has three significant advantages over that of balanced realization. First, modal decomposition reduced models result in less error, when compared to the full-order model, than balanced realization reduced models of similar order in problems with low or moderate advective heat transfer. Second, because the balanced realization based methods require a priori knowledge of the sensor and actuator placements, the reduced-order model is not robust to changes in sensor or actuator locations, a limitation not present in modal decomposition. Third, the modal decomposition transformation is less demanding computationally. On the other hand, in thermal problems dominated by advective heat transfer, numerical instabilities make modal decomposition based reduction problematic. Modal decomposition methods are therefore recommended for reduction of models in which advection is not dominant and research continues into methods to render balanced realization based reduction more suitable for real-time clinical hyperthermia control and estimation.

Algorithms↗

PubMedDate not supplied

Optimal actuator placement for large scale systems: a reduced-order modelling approach.

A recently developed, extended balanced realization, reduced order modelling technique for large-scale distributed systems is applied to the problem of optimal actuator placement for hyperthermia treatments. Extended balanced realization develops low-order models whose state reconstructions are robust to actuator and sensor placement changes, and hence can be effectively used to find optimal placements in a computationally efficient manner. This optimization approach has been tested on simulations of a scanned focused, ultrasound hyperthermia system and found to be robust and accurate over a wide range of models, and the savings in computational costs were found to be significant.

Equipment Design↗