Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation (MAD) scale estimator has been used to detect outliers, but it is only efficient with symmetrically distributed observations. Experimental analysis has shown that time-series RSS observations can have a symmetric or asymmetric distribution depending on the nature of the environment in which the measurement was taken. Hence, the use of the Z-score method with the MAD scale estimator will not be efficient. In this paper, the $S_n$ scale estimator is proposed as an alternative to MAD to be used with the Z-score method in detecting outliers in time-series RSS observations. Performance comparison using an online RSS dataset shows that the Z-score with MAD and $S_n$ as scale estimators falsely detected about 50% and 13%, respectively, of the RSS observations as outliers. Furthermore, the average absolute RSS median deviations between raw and outlier-free observations are 3 dB and 0.25 dB, respectively, for the MAD and $S_n$ scale estimators, corresponding to a range error of about 2 m and 0.5 m.

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system’s localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user’s instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers’ suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

Visegrad Group is a group of four countries in Central Europe, namely the Czech Republic, Slovakia, Poland, and Hungary. These countries share not only a similar history, but also similar economic development (measured for example by Gross Domestic Product (GDP)) and geo-political ideas. Nowadays, the economic development of every country and its competitiveness on the world market is supported by the creation of innovation (knowledge-based economy), especially from an Industry 4.0 point of view. The aim of this article is to compare the Visegrad Four (V4) from different perspectives. Firstly, the comparison of GPD development is done, next the analysis of foreign trade. The article presents the results of a comparative analysis of changes in innovativeness and competitiveness of the V4 economies over a period of 5 years. The Global Innovation Index (GII) shows the level of innovation of most countries in the world. Reports publishing GII were established thanks to the cooperation of Cornwall University with INSEAD (fr. Institut européen d'administration des affaires) Business School and World Intellectual Property Organization. The Summary Innovation Index (SII) was used in the European Innovation Scoreboard, as well as the Global Competitiveness Report and Global Competitiveness Index (GCI). The analysis shows that all members of V4 are so called moderate innovators. The Czech Republic begins to diverge from other member states in terms of SII, GII and it has been increasing its GCI as well. Poland occupies one of the last positions in the V4 innovation ranking, where Hungary was the weakest in terms of competitiveness in 2016. However, the mutual connection between GDP and above mentioned indexes shows relatively surprising results.

Kalman filters are a set of algorithms based on the idea of a filter described by Rudolf Emil Kalman in 1960. Kalman filters are used in various application domains, including localization, object tracking, and navigation. The text provides an overview and discussion of the possibilities of using Kalman filters in indoor localization. The problems of static localization and localization of dynamically moving objects are investigated, and corresponding stochastic models are created. Three algorithms for static localization and one algorithm for dynamic localization are described and demonstrated. All algorithms are implemented in the MATLAB software, and then their performance is tested on Bluetooth Low Energy data from a real indoor environment. The results show that by using Kalman filters, the mean localization error of two meters can be achieved, which is one meter less than in the case of using the standard fingerprinting technique. In general, the presented principles of Kalman filters are applicable in connection with various technologies and data of various nature.

The aim of this article is to discuss the effect of learning a non-native language on the enhancement of cognitive performance in healthy native Czech elderly. In addition, special emphasis is put on the qualitative assessment. To do this, 42 Czech cognitively unimpaired seniors were enrolled into the study. These were then divided into an experimental group (i.e., 20 healthy elderly studied English as a non-native language for three months) and a passive control group (22 healthy elderly, who did not undergo any non-native language intervention). The main outcome measures included the Montreal Cognitive Assessment, statistical processing of the data, and a qualitative content analysis. The results indicate that the cognitive performance of the intervention group did not differ from the control group. Therefore, no cognitive enhancement through non-native language learning was achieved. However, the findings of the qualitative analysis show that such non-native language learning with the peers of the same age is especially beneficial for the overall well-being of healthy seniors, especially as far as their social networks are concerned. Furthermore, participant’s subjective feelings from their self-reports indicate that foreign language learning also contributes to acquiring new English words and phrases. However, as there are very few empirical studies on this research topic, further research is needed in order to confirm or refute the present research findings on the enhancement of cognitive performance through non-native language learning in healthy seniors.

Digital storytelling is one of the teaching methods that aims to improve motivation of students, critical thinking and learning outcomes. The results of previous research show the successful use of this method in education, but its use in some subjects is still questionable. The aim of the pedagogical experiment, which was conducted in 2021 at six business academies in the Czech Republic, was to discover whether or not digital storytelling contributes to better study results in business subjects taught at high school. A total number of 856 students were randomly divided into two independent groups. In the experimental group, the digital storytelling method was used in the process of teaching. The students in the control group were taught with the standard teaching method—the teacher’s explanation with the support of the presentation. By comparing the results from the pre-tests and post-tests of the experimental and control groups, the findings reveal that the students from the experimental group reached higher mean values in the post-test than the students from the control group did.