The aim of this paper is to propose a new approach to probability density function (PDF) estimation which is based on the fuzzy transform (F-transform) introduced by Perfilieva in \cite{Perfilieva:FSS06}. Firstly, a smoothing filter based on the combination of the discrete direct and continuous inverse F-transform is introduced and some of the basic properties are investigated. Next, an alternative approach to PDF estimation based on the proposed smoothing filter is established and compared with the most used method of Parzen windows. Such an approach can be of a great value mainly when dealing with financial data, i. e. large samples of observations.
The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.
The paper technically describes the principles of incorporation of the biofeedback system into the system of a driving simulator. After a brief introduction of the basic features of EEG biofeedback, the most important scenarios where such simulator enhancement can be successfully used are described. The system is introduced with the use of an analysis of the major technical and construction aspects, such as the software design, hardware realization and its incorporation into the driving simulator system. Finally, the paper sketches pilot experiments which were performed using EEG biofeedback incorporated into the driving simulator.