Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

Cardiotocography is the main method used for fetal assessment in every day clinical practice for the last 30 years. Many attempts have been made to increase the effectiveness of the evaluation of cardiotocographic recordings and minimize the variations of their interpretation utilizing technological advances. This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis. The core of the proposed method is the introduction of a support vector machine to "foresee" undesirable and risky situations for the fetus, based on features extracted from the fetal heart rate signal at the time and frequency domains along with some morphological features. This method has been tested successfully on a data set of intrapartum recordings, achieving better and balanced overall performance compared to other classification methods, constituting, therefore, a promising new automatic methodology for the prediction of metabolic acidosis.     

Performance aspects of data broadcast in wireless networks with user retrials

The user retrial phenomenon and its significant impact on network performance in unicast wireless systems are known and relatively well studied in the literature. However, there have been no previous studies on the impact of the user retrial phenomenon on other types of wireless networks. The objective of this paper is to extend the analysis of the user retrial phenomenon to wireless systems which, in addition to unicast service, also support a data broadcast service. This objective is realized by defining several performance measures appropriate for the analysis of hybrid unicast-broadcast systems in the presence of users' retrials. Subsequently, we derive the exact mathematical expression for each of the measures. Based on these expressions, we prove the existence of a single broadcast scheduling scheme, which ensures optimal system performance, with respect to the given set of proposed measures, the system's throughput, and the grade and quality of service. We also take a closer look at the class of hybrid unicast-broadcast systems with autonomous estimation of data item popularities, and we elaborate on the major challenges associated with such systems. Finally, we evaluate our theoretical expressions through simulation, and we discuss their robustness with respect to moderate deviations in the underlying model.     

A Simulation Tool to Predict the Impact of Soil Topologies on Coupling Between a Light Rail System and Buried Third-Party Infrastructure

The production of stray currents by DC light rail systems leads to the corrosion of the supporting and third-party infrastructure in close proximity to the rail system. This paper simulates two parallel tracks that are occupied by two trains: one on each track. This type of modeling constitutes a case study that is utilized to investigate the effect of soil topologies on the corrosion performance of a floating DC light rail system focusing on the supporting and third-party infrastructure. The modeling technique used involves the accurate computation of the shunt and series parameters for use in a resistive-type model using a commercially available software package. The results demonstrate the importance that soil resistivity has on the corrosion risk to traction system and third-party infrastructure. Such information could ultimately be used to vary the level of stray current protection across a light rail system to ensure a consistent lifetime across the whole system.     

An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps

The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.     

Two iterative image restoration algorithms with applications to nuclear medicine

Two methods for recovering an image that has been degraded while being processed are presented. The restoration problem is formulated as a constrained optimization problem in which a measure of smoothness based on the second derivatives of the restored image is maximized subject to the constraint that noise energy is equal to the energy in the difference between the distorted and blurred images. The approach is based on the Lagrange multiplier method. The first algorithm reduces the problem to the computation of few discrete Fourier transforms and allows control of the degree of sharpness and smoothness of the restored image. The second algorithm with weight matrices included allows the handling of edges and flat regions in the image in a pleasing manner for the human visual system. In this case the iterative conjugate gradient method is used in conjunction with the discrete Fourier transform to minimize the Lagrangian function. The application of these algorithms to nuclear medicine images is presented.