This paper, we present a performance evaluation of a multifaceted electronic voting framework. The electronic voting model that was developed on the framework is capable of handling electronic ballots with multiple scopes simultaneously via three different electronic voting means. The model catered for probity of an election process in terms of generic and functional requirements. The performance evaluation detailed the degree to which the framework meets the generic and functional requirements of electronic voting systems using a five-point psychometric scale. The results of the quantitative analysis showed that the framework is capable of ensuring voters’ privacy and authenticity while the integrity, accuracy and verifiability of ballots casted are guaranteed.

The paper offers an overview of signal processing approaches that are suitable for monitoring of persons by means of ultrawideband sensors. The “Monitoring of persons” is related to their detection, localization, and tracking and in the case of static persons, also to the monitoring of their vital signs such as the breathing frequency and the heart rate. This paper discusses basic principles in the monitoring of persons who can be moving, static, or changing the character of their movement. In the case of static persons, the paper describes signal processing methods suitable for contactless estimation of their breathing frequency and the heart rate. Monitoring of persons using UWB sensors is attractive in many application scenarios such as emergency, rescue, medical, etc. Therefore, we believe that this comprehensive survey brings a lot of interesting information to researchers and specialists working in the field of contactless monitoring of persons.

This paper presents a direct vector control (DVC) strategy for the doubly fed induction generator (DFIG)based wind turbine systems. The major disadvantages that are usually associated with DVC control scheme are the electromagnetic torque, reactive power and active power ripples. To overcome these disadvantages an advanced five-level space vector modulation (5L-SVM) strategy based on neural networks (NNs) controller is proposed. The proposed controller is shown to be able to reduce the reactive and active powers ripples and to improve the performances of the DVC control. Simulation results are shown by using Matlab/Simulink..

The Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the Internet. Given the character of IoT devices, the most popular way of connecting them is using wireless technologies. However, unlike traditional (legacy) networks, the IoT has unique traffic characteristics with specific demands on the used communication protocols. Together with the constrained nature of IoT devices and the diversity in IoT application domains, there is no one network technology that meets all requirements. IoT solutions are based on a variety of protocols that yield concerns about interoperability and mutual compatibility. This article provides a high-level overview of the most popular IoT network technologies and compares them in one unified protocol stack consisting of five layers. The proposed protocol stack is intended to highlight the similarities and differences among IoT network technologies and to provide their compatibility possibilities at different layers.

Impact of rapid annealing on the soft magnetic properties of the Fe80Nb3Cu1Si6B10 alloy has been investigated. Parent as-quenched ribbons were prepared by planar flow casting method. Rapid thermal treatments (7-30s) has been conducted at 500°C using the preheated Cu blocks to ensure elevated heating rate of more than 100 K/s. Reference samples were isothermally annealed in the vacuum furnace for 1 h at the same temperature. X-Ray diffraction unveiled formation of nanocrystalline structure of bcc α-Fe(Si) grains, embedded in the residual amorphous matrix in all processed samples, irrespective of the annealing technique. Evolution of coercivity and saturation magnetization values, obtained from the measured hysteresis loops, showed improved magnetic softness of the rapidly annealed samples, compared to the as-quenched and conventionally processed ones. Significant embrittlement of the samples after nanocrystallization has been observed regardless of annealing time and thermal treatment technique used.

In this work we proposed a backstepping controller adapted by a fuzzy inference for the control of the electric vehicle with two motor wheel drives. This proposed combine controller has significantly improved control performance compared to conventional backstepping. The different speeds of the wheels are ensured by the electronic differential, this driving process makes it possible to direct each driving wheel to any curve separately. Modeling and simulation are performed using the Matlab / Simulink tool to study the performance of the proposed controller.

Digital data are all around us and occurs in various forms as videos, pictures or texts. Digital documents represent the vast majority of such data. It can be e-news, social media contributions and so on. They can contain useful information, but due to their amount, it is time-consuming to find relevant information for the concrete company or persons. For that reason, there is a need for their automatic analysis. One of the areas which dealt with textual data analysis is topic modeling. It showed us a new way of how to automatically browse, search and summarize data in the organization. Topic modeling can be useful for time-based analysis of crises, elections, news feeds, launching of new products on the market, and other tasks which led to decision support tasks. In this paper, we aim to survey and compare topic modeling methods and propose web application to visualize extracted topics using topic modeling method called Latent Dirichlet Allocation (LDA). The comparison of selected standard topic modeling methods was experimentally tested on two selected textual datasets (20Newsgroup and Reuters) using standard evaluation metric. The proposed web application was implemented to use LDA and can extract topic models from textual documents datasets, visualize them and show their evolution over time.

This article aims to present the issue of the company´s bankruptcy and defines which financial indicators affects and can accurately detect the financial health of the company and thus better predict the emergence of potential bankruptcy. Currently, these methods include mainly modern techniques from the data mining area. For the practical application of this approach to predict the future state of the company, were used the financial indicators of Polish companies. We used the most suitable algorithms for predicting bankruptcies – decision trees that provide simple results interpretation. The analytical process is managed by the CRIPS-DM methodology, which offers a description of the important steps needed to solve this task. Part of the article constitutes an analysis of the current state, which presents solutions to this problem by other authors. Analysing available data we found that the most effective financial indicators are Attr27[profit on operating activities / financial expenses], Attr34 [operating expense/total liabilities] and Attr41 (total liabilities/[(profit on operating activities + depreciation)*(12*365)]). The model that best-predicted bankruptcy was the C5.0 decision tree algorithm.