József Homlok

Security issues of service installation on a multi application NFC environment

NFC is one of the latest wireless communication technologies, making possible variety of business opportunities. At Budapest University of Technology and Economics (BME) a generalized approach for NFC application development has been elaborated, where the Mobile Network Operators (MNOs), as well as service providers can make their own application and run it in the proposed common environment. We have developed the prototype of an application framework based on the generalized approach that provides the possibility of rapid implementation of any kind of the business logic and the user interface as well as the opportunity to create a new skin for the already existing application. Further benefit of the application framework is that the technological details are hidden from the service application developers thus they can focus only on the proper implementation of the business logic and the user interface. One of the main units of the developed framework is a script application execution environment (the so called scripting host) running on mobile equipments. The scripting host allows the user to dynamically manage and access NFC based services using the underlying mobile equipment. The current paper presents the security consideration of the script installation and execution procedures.

Altered blood glucose dynamics during and after anhepatic phase of liver transplantation: A model-based approach

During liver transplantation (LT) the glucose metabolism is effected by a crucial disturbance. The blood glucose level is extremely hard to control by conventional clinical protocols during this phase. Model based approach can enhance the blood glucose control during the anhepatic phase (absence of liver) and post-anhepatic phase. The physiological constants of validated clinical metabolic model were slightly modified based on previous studies. The model fitting errors and the sufficient capture of the blood glucose (BG) dynamic evinced the applicability of the model. However the sufficient per-patient estimation of endogenous production could more enhance the performance of the model based BG prediction.

Model-based analysis of traffic flow in signalized intersections

Traffic systems are man-made systems in which the human component as a complex factor plays a significant part. The aim of the research was to offer a method which is applicable for setting the real traffic situations at a signalized intersection against the simulated ones, using a traffic model with similar statistical features. The simplest technical apparatus has been used to monitor the real traffic situations. Only one video camera is needed for data collection and it is located in the observable area. Hence time-dependent parameters have been collected which are available for the analysis of the traffic situations. In the case of both traffic models the analysis is based on a behavior model of inhomogeneous driver-populations and an estimation method. The behavior model of inhomogeneous driver-populations handles the time-dependent parameters of driver behavior as the most important predictors, describes the movement and the behavior of the inhomogeneous driver-populations approaching a signalized intersection, passing the amber signal light. The estimation method developed using the model parameters opens the door to estimating the composition of drivers and measuring the capacity of an intersection on a more sensitive scale.

Specific validation analysis of stochastic ICING model based estimation of insulin sensitivity profile using clinical data

In this paper the performance of an insulin sensitivity profile estimation method is analysed from the the aspects of clinical applicability. In our previous studies it has been demonstrated that the grey box variant of the ICING (Intensive Control Insulin-Nutrition-Glucose) model may be successfully applied to improve the estimation accuracy of the insulin sensitivity profile of intensive care patients. This may enable the application of the model in the clinical treatment, especially in tight glycaemic control. The sensitivity estimation accuracy itself and the accuracy improvement compared to previous methods are highly variable, it strongly depends on the range of blood glucose concentration. In the present study the insulin sensitivity estimation is analysed from the clinically relevant aspects. Modelling error represented by the difference of measured and computed the blood glucose concentration was considered on the total glucose concentration range (measured in mmol/l) - 0 < cG < 20 - of the cohort data set as well as on its four subregions, namely hypoglycaemic (0 < cG ≤ 4), normoglycaemic (4 < cG ≤ 6.5), mild-hyperglycaemic (6.5 < cG ≤ 10), and severe-hyperglycaemic (10 < cG ≤ 20). The results of the computations indicate that the SDE model was significantly better in the normoglycaemic and mild-hyperglycaemic ranges, somewhat better in the hypoglycaemic range and nearly the same in the severe-hyperglycaemic range. The 97 % of all of the concentration values were in the normoglycaemic and the mild-hyperglycaemic range (5841 values), which amplifies our statement in these ranges, but further study is necessary the ensure the verdict for the hypoglycaemic and severe-hyperglycaemic ranges.

Estimation of the insulin sensitivity profile for the stochastic variant of the ICING model

In this paper the insulin sensitivity profile and the diffusion term as stepwise functions were determined for the grey box variant of the ICING (Intensive Control Insulin-Nutrition-Glucose) model used for virtual trial methodology. The suggested technique can separate system noise, which was earlier lumped into the insulin sensitivity profile itself. In this way one can get smaller residual for the model fitting as well as more precise value for the insulin sensitivity. This fact can improve the prediction process estimating insulin sensitivity value for the future time span and therefore it can ensure better clinical treatment. Maximum likelihood method has been employed to compute the values of the insulin sensitivity profile and the diffusion term for every half hour intervals. The global optimization problem was solved using genetic algorithm, simulated annealing method and Nelder-Mead procedure employing parallel computation. The application of the grey box model was able to reduce the errors sometimes with even more than 50%. According to the likelihood ratio test, the stochastic variant of the ICING model yielded significant improvement comparing to the white box one.

Model-based estimation of physiological parameters in the reperfusion phase of liver transplantation

In liver transplantation (LT) well defined increase of blood glucose (BG) level can be observed after the revas-cularization of allografted liver, which is followed by a highly variable BG level period. Managing the BG level in the normo-glycemic range would cause a positive effect on the outcomes of the surgery. Model-based approach may lead us to design appropriate glycemic control (GC) method. In this paper we suggest to modify the so called ICING model to describe the dynamics of the BG level during LT. The identified physiological parameters allow the model to follow the characteristic blood glucose dynamics during and after the reperfusion phase of LT as well. The possibility of the measurements of physiological parameters are restricted due to the surgery conditions. The suggested model also let us estimate the rate of the endogenous glucose production and the insulin independent glucose uptake.

Blood glucose model for liver transplantation: Alteration of physiological parameters

During liver-transplantation (LT) a tight glycemic control protocol can improve the clinical outcomes. The ICING (Intensive-Control-Insulin-Nutrition-Glucose) model is validated for medical intensive care unit (MICU) application. Previous studies have showed that changes in the model parameters are necessary to use it in LT environments. This study analyses the optimal range of physiological parameters and the differences between the MICU and LT cases, as well the time dependency of the parameters after the recirculation of the new liver. There can be found a clear optimal physiological parameter range for the phase before the recirculation of the new liver, the parameter values are different from the MICU patients with higher endogenous glucose production and lower glucose dependent glucose utilization values. After the recirculation there is an approximately 9 hours long time interval where the optimal parameter set cannot be clearly determined. During this transition time the differences gradually disappear between the MICU and LT patients.

Model based analysis of cerebrovascular oscillation using the system Circle of Willis

Low frequency fluctuation (oscillation) can be observed in the cerebral blood flow under the following condition. The physiological phenomenons frequency range is about 4-12 cpm (0; 06 - 0; 2 Hz). Independent from location, in both of the hemispheres the observed phenomenon shows high level of correlation, although previously its local enhancement was proved. The concrete physiological mechanism, controlling this oscillation has not been identified. One potential natural reason for the synchronous appearance of the oscillation is the structure of arteries, on the base of the brain, the so called Circle of Willis (CoW). In order to analyze the potential causes, physiological phenomenons controlling mechanism, the CoW model has been modified in order to reflect the experimental setup. In this paper we demonstrate the evaluation of the measurement, the modified model and present the simulation results.