Remote Monitoring of Implantable Cardioverter Defibrillator

The rate of implantable cardioverter defibrillator (ICD) implantation has gone up as primary and secondary prevention trials have relatively consistently shown significant improvement in mortality and morbidity. Most patients with ICDs are followed routinely at intervals ranging from 3 to 6 months. Many patients require additional non-scheduled visits to investigate symptoms that may or may not relate to their cardiac disease or device. Appropriate and inappropriate therapies of implantable cardioverter defibrillators have a major impact on morbidity and quality of life in ICD recipients. Remote monitoring systems can substitute for routine follow-up visits and/ or deliver continuous diagnostic and device status information. Remote monitoring of ICDs can decrease the need for many patient visits and, thereby, probably reduce expense

Tumor and mutation suppressing plant extract

A compound derived from an extract of vegetative tissue of plants of the Genus Shortia demonstrates antitcarcinogenic potential by inhibiting the growth and development formation of tumors in living tissue in response to an organism known to cause the initiation of tumors in living tissue and by reducing mutation rates in living tissues exposed to a known mutagen

Experimental Implementation of Adaptive-Critic Based Infinite Time Optimal Neurocontrol for a Heat Diffusion System

Recently the synthesis methodology for the infinite time optimal neuro-controllers for PDE systems in the framework of adaptive-critic design has been developed. In this paper, first we model an experimental setup representing one dimensional heat diffusion problems. Then we synthesize and implement an adaptive-critic based neuro-controller for online temperature profile control of the experimental setup

Method and system for measuring sound velocity

A method and system for determining the speed of sound in a fluidic medium by determining the travel time of an acoustical signal a predetermined distance in a fluidic medium by generating a cyclical reference signal of a predetermined frequency and transmitting a portion of the reference signal through the medium. The transmitted portion of the reference signal is received after travelling a predetermined distance in the fluidic medium. The cycles of the cyclical reference signal are counted during the period of time between the transmitting and receiving of the portion of the reference signal wherein the travel time of the portion of the reference signal, is the number of cycle counts divided by the frequency. The speed of the acoustical signal through the fluidic medium is a function of the path length divided by the travel time

Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates

International audienceThe lack of an universal modelling approach for turbulence in Reynolds-Averaged Navier–Stokes simulations creates the need for quantifying the modelling error without additional validation data. Bayesian Model-Scenario Averaging (BMSA), which exploits the variability on model closure coefficients across several flow scenarios and multiple models, gives a stochastic, a posteriori estimate of a quantity of interest. The full BMSA requires the propagation of the posterior probability distribution of the closure coefficients through a CFD code, which makes the approach infeasible for industrial relevant flow cases. By using maximum a posteriori (MAP) estimates on the posterior distribution, we drastically reduce the computational costs. The approach is applied to turbulent flow in a pipe at Re= 44,000 over 2D periodic hills at Re=5600, and finally over a generic falcon jet test case (Industrial challenge IC-03 of the UMRIDA project)

Proper Orthogonal Decomposition Based Modeling and Experimental Implementation of a Neurocontroller for a Heat Diffusion System

Experimental implementation of a dual neural network based optimal controller for a heat diffusion system is presented. Using the technique of proper orthogonal decomposition (POD), a set of problem-oriented basis functions are designed taking the experimental data as snap shot solutions. Using these basis functions in Galerkin projection, a reduced-order analogous lumped parameter model of the distributed parameter system is developed. This model is then used in an analogous lumped parameter problem. A dual neural network structure called adaptive critics is used to obtain optimal neurocontrollers for this system. In this structure, one set of neural networks captures the relationship between the states and the control, whereas the other set captures the relationship between the states and the costates. The lumped parameter control is then mapped back to the spatial dimension, using the same basis functions, which results in a feedback control. The controllers are implemented at discrete actuator locations. Modeling aspects of the heat diffusion system from experimental data are discussed. Experimental results to reach desired final temperature profiles are presented

Modeling and Control of Re-Entry Heat Transfer Problem using Neural Networks

A nonlinear optimal re-entry temperature control problem is solved using single network adaptive critic (SNAC) technique. The nonlinear model developed and used accounts for conduction, convection and radiation at high temperature, represents the dynamics of heat transfer in a cooling fin for an object re-entering the earth\u27s atmosphere. Simulation results demonstrate that the control synthesis technique presented is very effective in obtaining a desired temperature profile over a wide envelope of initial temperature distribution