Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.

It is very hard to implement the emotion recognition system based on spoken text. Computer applications have a huge problem with understanding non-literal meaning of statements as well as irony or a situational joke. The article describes how to represent emotional speech in the form of scalograms which are the result of speech signal processing by Discrete Wavelet Transform (DTW). The method of processing scalograms in order to extract input data for natural language processing algorithms in order to recognise the emotional state is also presented. The following emotional states were considered during the research: joy, anger, boredom, sadness, fear and neutral state. The developed method has been tested on databases containing recordings of emotional speech in the following languages: Polish, English, German and Danish. Depending on the language and classifier used, obtained results ranged from over 62% to over 94%. The use of fuzzy classifiers greatly improves the time and efficiency of classification.

One of the greatest problems in the field of cultural heritage is the process of material destruction of monuments. In most cases, the cause is operational or random wear. Such processes can be slowed down or eliminated thanks to the maintenance process. The most dangerous processes of monument wear and tear are those in which historical objects are exposed to environmental impact. Cycles of weather changes have a particularly negative impact on objects of this type. These types of objects are significantly damaged and are the most difficult to scan. The research work included the scanning of such an object and attempts were made to digitise the point cloud. The monument selected for the model laser scanning was a rock petroglyph located on the slope of the Chatkal mountain near the town of Tashkent in Uzbekistan. The article describes the process of digitally recording a historic object with the use of the Artec Eva scanner. The process of data acquisition from points in space and the processes of highlighting petroglyphs are described. The highlighted model was obtained using proprietary software created for this purpose by the authors of the article. As a result, data were saved which would be difficult to save and read using the previously used 3D scanning techniques. The obtained data can be made available to recipients in the form of exhibits in virtual museums.

Word recognition of Slavic languages is not an easy task due to the complicated declension of words and a variety of diacritical signs. Polish is a representative of West Slavic languages, which are written in Latin characters. Automatic handwritten word recognition in Slavic languages is not easy, due to the poor recognition rate of letters with diacritical signs and lack of good handwritten text corpora for languages with declension. The main aim of the research is to investigate the possibility of correcting typos made in the final phase of recognizing Polish. The method developed is based on letter recognition by means of convolutional neural networks (CNNs) and text matching algorithms for resulting words. At the first stage, we use a designed convolutional neural network for character recognition. At the second stage, after combining letters into words we apply a post-processing error correction method, which improves the efficiency of recognition of the misspelled words. We checked the efficiency of word matching for a few measures of similarity of words, i.e: edit distance (Damerau-Levenshtein), string matching (Sorensen-Dice) and list of candidates. In addition, we examine how word length and the number of misplaced letters affect the behaviour of the algorithms used. The analysis is carried out for bigram and trigram methods. By combining different methods to assess the similarity of words, better selection of lists of proposed words has been achieved. The article proposes an innovative method for correcting post-processing errors in recognizing Polish words with the efficiency of correct word matching ranging from 76% to 99%, depending on the measure and word length used.

The article presents the compact system for predicting the blood pressure with the use of PPG (photoplethysmogram) curves. The algorithm for data measurement and analysis was proposed. The prototype system and initial experimental results were presented. In the system the filters were applied to the input signal for improving the peak detection algorithm. The measurement device allows the user to record the data and transmit to a remote location. In more compact form in future it can act as an Internet of Things wearable agent.