The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

In this paper, the application of spline -models for some imaging tasks is proposed. Using traditional methods in the case of front-side-looking radar can be very complicated. To calculate surface image antenna pattern and some sounding scheme features should be known. This task is incorrectly formulated inverse problem. The problem is solved in the class of two-dimensional spline-function for regularization. In the paper the problem is modeled for certain conditions, such as antenna angle 45 degrees. For the given conditions, different received and reconstructed signals are modeled.

Meteorology plays an important role in aviation, as it enables to predict weather conditions and detect flight dangerous meteorological phenomena. Meteorological radar is used to detect the intensity and possible location of precipitation and dangerous zones in them. Doppler radar systems are able to measure the speed of scatteres that constitute meteorological formations and phenomena. The tasks of measurement accuracy increasing and reliability rise of hazardous meteorological phenomena detection become much more relevant after establishing new flight control system CNS ATM adopted by ICAO - the International Civil Aviation Organization.© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

In this work the fractal signal constructed on the base of linear and cubic Hermite splines is considered. Spline shape at a large scale and shape of its fragments are the same. Small number of parameters allows to obtain a complex waveform. The methods of constructing of such signals and their evaluation at the presence of noise are considered.