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This paper focuses on various possible applications of the GNU General Public License free software functionality. During the lockdown, end users are granted the freedom of studying at home using this license. Our efforts are focused on the use of GNU Octave. This software is a high-level programming language, primarily intended for numerical computations. Octave is suitable for illustrating basic linear and nonlinear problems that occur in telecommunications and are studied on the “Signals and Systems” course. Octave is a good alternative of the most used commercial software in this area, like MA TLAB and Mathematika. Specific features have been considered in the design of exercises. The methodology of exercises for students is described in this paper

This article describes the design and implementation of an Embedded Interactive Timing System. Such systems are used in the therapy of children who have learning problems. The matching of different beats can help the brain work more efficiently. A methodology and architecture of an interactive timing is proposed. Open-source software environment and microcontroller is used for the practical realization of the system. Some results of the program functioning are shown.

Individuals with Mild Cognitive Impairment (MCI) currently have few treatment options against memory loss. Solutions for caring for the elderly both efficacious and cost-effective are given by Ambient Assisted Living (AAL) architecture, promising the improvement of the Quality of Life (QoL) of patients. QoL factors that are important for the MCI patients include mood, pleasant engagements, physical mobility and health, and the ability to perform activities of daily living. In this paper, we propose a daily activity reasoner that monitors, measures and analyses in real time several everyday events for building habits diary and detecting abnormal behavior of the user, part of an effective AAL system. The proposed solution is based on a combination of mean shift clustering algorithm. The reasoner offers two primary functionalities: habits building and duration and frequency of events. The reasoner can predict the behavior and detect (slow or fast) changes that might indicate modification in the health status of the user.

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%.

Capturing the type of physical activity a person is performing thorough his daily life, can inspire the development of new and innovative applications. Examples include monitoring patients' health and physical activity performance, reasoning upon the observed activity to recommend better training strategy, new therapeutic programs, etc. In this work we propose an algorithm for Human Activity Recognition based on the application of a geometrically motivated feature selection method. We test the algorithm on a standard data set and validate its performance by comparing it with the existing results of other known algorithms.

A biometric system is essentially a pattern recognition system. This system measures and analyses human body physiological characteristics, such as face and facial features, fingerprints, eye, retinas, irises, voice patterns or behavioral characteristic for enrollment, verification or identification (Bolle & Pankanti, 1998). Uni-modal biometric systems have poor performance and accuracy, and over last few decades the multi-modal biometric systems have become very popular. The main objective of multi biometrics is to reduce one or more false accept rate, false reject rate and failure to enroll rate. Face Recognition (FR) is still considered as one of the most challenging problems in pattern recognition. The FR systems try to recognize the human face in video sequences as 3D object (Chang et al., 2003; 2005), in unconstrained conditions, in comparison to the early attempts of 2D frontal faces in controlled conditions. Despite the effort spent on research today there is not a single, clearly defined, solution to the problem of Face Recognition, leaving it an open question. One of the key aspects of FR is its application, which also acts as the major driving force for research in that area. The applications range from law enforcement to human-computer interactions (HCI). The systems used in these applications fall into two major categories: systems for identification and systems for verification (Abate et al., 2007). The first group attempts to identify the person in a database of faces, and extract personal information. These systems are widely used, for instance, in police departments for identifying people in criminal records. The second group finds its main application in security, for example to gain access to a building, where face is used as more convenient biometric. The more general HCI systems include not only identification or verification, but also tracking of a human in a complex environment, interpretation of human behavior and understanding of human emotions. Another biometric modality that we use in our approach is the electrocardiogram (ECG). The modern concept for ECG personal identification is to extract the signal features using transform methods, rather than parameters in time domain (amplitudes, slopes, time intervals). The proper recognition of the extracted features and the problem of combining different biometric modalities in intelligent video surveillance systems are the novel steps that we introduce in this work. 4

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.