In this paper an approach for personal biometric identification is presented based on extraction of ECG features and classification with RBFNN. We perform denoising and segmentation on the input signal, after which we realize dimensionality reduction and feature extraction based on PCA transform. The separability of the selected features is improved by applying LDA. The final stage of the proposed approach is classification and recognition of the extracted features with classifier score fusion.

In this article is presented an overview of the existing solutions for smart grids, and an analysis of the majority cases of cybersecurity threats. The purpose of this article is to define the risks of compromising the confidentiality of the transferred and stored data, and the ability to install a new device into the network. The main hazards coming from unauthorized access to the smart grid devices are analyzed and systematized and recommendations are given to improve security. The proposed smart grid design aims at making the system as safe as possible from the viewpoint of information protection and communication using intelligent communication and control devices.

This paper presents an approach for automated estimation of human emotions from electroencephalogram data. The used features are principally the Hjorth parameters calculated for theta, alpha, beta and gamma bands taken from certain channels. The classification stage is support vector machine. Since the human emotions are modelled as combinations from physiological elements such as arousal, valence, dominance, liking, etc., these quantities are the classifier's outputs. The best achieved correct classification performance is about 80%.

The paper describes combined approach to face detection for grayscale images using the combined cascade of neural network classifiers which consists of Haar-like features' cascade of weak classifiers and convolutional neural network. The combined cascade with proposed face candidates' verification method allows achieving one of the best detection rates on CMU test set and a high processing speed suitable for a video flow processing. In the last stage of our algorithm we provide extraction of salient facial features for pose estimation and recognition.