Volume 61, Nº 5 (2018)

Anovel point-to-point symbol detection algorithm in multiple input multiple output (MIMO) system is proposed. This algorithm is an augmentation of two popular algorithms, namely, vertical Bell laboratories layered space-time (VBLAST) and maximum a posteriori probability (MAP). Here, layers are distinguished or ordered based on the a posteriori probabilities of output symbols and not on signal-to-noise ratio (SNR). For each layer, a set of a posteriori probabilities is computed for all output symbols using all possible signal constellations. The layer corresponding to the output symbol having minimum a posteriori probability is selected first from the set of a posteriori probabilities for detection by doing the comprehensive search over all the possible signal constellations. Then, the remaining layers are detected by the conventional VBLAST MAP technique. The relationship of MIMO symbol error rate (MIMO SER) versus MIMO symbol SNR is presented using simulations for 16×16 MIMO systems and 16-QAM constellation. The results show that the proposed algorithm outperforms conventional VBLAST MAP and improved VBLAST algorithms.

Automated Osborn wave detection system featuring the sensitivity of 94.63% and classification accuracy of 94.58% for the notch and slur types of waves in the cardiac signal has been developed. The quasi-matched wavelet filtering method and the method of principal components were applied for extraction and formation of feature vectors representing the input data of classifier. The error feedforward neural network with topology of a multilayer perceptron was used as a classifier. Signal samplings built on information from the PhysioNet open database of medical signals were used for training, testing and validation of neural network. This study involved the use of 12-lead electrocardiograms of 60 healthy patients aged 17–87. These electrocardiograms formed the basis for creating a database of 14832 signals (9888 with Osborn wave signals of two types and 4944 signals without pathological findings). The proposed approach ensured the classification accuracy exceeding the accuracy of existing techniques.

In this paper it is proposed an original method for verification of statistical hypotheses about mean values of random quantities. This method is based on Kunchenko stochastic polynomials tool and probabilistic description on a basis of higher order statistics (moments and/or cumulants). There are represented analytical expressions allowing to optimize decision rules using certain qualitive criterion and calculate decision-making error. It is shown polynomial decision rule in case of polynomial power S = 1 corresponds to classic linear decision rule which is used for comparative analysis. By means of multiple statistical experiments (Monte–Carlo method) obtained results of Neumann–Pierson criterion show proposed polynomial decision rules are characterized by increased accuracy (decrease of the 2nd genus errors probability) in compare to linear processing. The method efficiency increases with increase of stochastic polynomial order increase of degree of random quantities distribution difference from Gaussian probabilities distribution law.