Electrical capacitance tomography (ECT) is one of non-invasive visualization techniques which can be used for industrial process monitoring. However, acquiring images trough 3D ECT often requires performing time consuming complex computations on large size matrices. Therefore, a new parallel approach for 3D ECT image reconstruction is proposed, which is based on application of multi-GPU, multi-node algorithms in heterogeneous distributed system. This solution allows to speed up the required data processing. Distributed measurement system with a new framework for parallel computing and a special plugin dedicated to ECT are presented in the paper. Computing system architecture and its main features are described. Both data distribution as well as transmission between the computing nodes are discussed. System performance was measured using LBP and the Landweber’s reconstruction algorithms which were implemented as a part of the ECT plugin. Application of the framework with a new network communication layer reduced data transfer times significantly and improved the overall system efficiency.

Wireless Sensor Networks (WSNs) are a component of various applications, including environmental monitoring and the Internet of Things (IoT). Energy efficiency is one of the significant issues in WSNs, since sensor nodes are usually battery-powered and have limited energy resources. The Enhanced Distributed Energy-Efficient Clustering (DEEC) protocol is one of the most common methods for improving energy efficiency and network lifespan by selecting cluster heads according to residual energy. Nevertheless, standard DEEC methods are limited in dynamic environments because of their fixed nature. This paper presents a novel modular implementation of the DEEC protocol for Wireless Sensor Networks, addressing the limitations of the standard DEEC in dynamic and heterogeneous environments. Unlike the typical DEEC protocol, the proposed approach incorporates realistic energy models, supports heterogeneous nodes, implements load balancing, and enables dynamic cluster head selection Numerical simulations in MATLAB^® demonstrate that the improved DEEC protocol achieves a 133% longer stability period (first node death at 1166 rounds vs. 472 rounds), nearly doubles the network lifetime (4000 rounds vs. 2111 rounds), and significantly enhances energy efficiency compared to the standard DEEC. These results verify the effectiveness of the proposed enhancements, making the protocol a robust solution for modern WSN and IoT applications.

Electrical Capacitance Tomography is a non-invasive imaging technique, which allows visualization of the industrial processes interior and can be applied to many branches of the industry. Image reconstruction process, especially in case of 3D images, is a very time consuming task (when using classic processors and algorithms), which in turn leads to an unacceptable waiting time and currently limits the use of 3D Electrical Capacitance Tomography. Reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem must be able to transform capacitance data into images in a fraction of a second. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms, time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for further optimizations of previously developed algorithms.

The article presents a new approach that combines two separate fields of stock exchange analysis. The aim of proposed solution is to support investors in their decisions and recommend to buy the assets which provide the greatest profits. To achieve this goal, decisive algorithms have been developed using artificial neural networks and technical analysis, which were used along with statistics that refer to the occurrence of single words in the fundamental analysis. Based on this, a model was prepared that in response gives a recommendation for future increases. The system consists of two algorithms. The first of them uses the LSTM (Long Short-Term Memory) artificial neural network. As inputs, information about the current closing price as well as technical analysis indicators along with the value of the current volume were used. The output has been specified as the closing price on the following day. In order to improve the response from the ANN (Artificial Neural Network), statistics of the occurrence of words in publications from last week were used. Subsequent signals gained much more importance if the volume of all transactions was much larger than the moving average of the last 15 periods and if the words that appeared in the last publication caused earlier increases. Additional information for the system are also data that come from Google Trends. This allows to verify the trend of interest and whether the published messages are important.

The word hacking very often evokes pejorative connotations. In IT to hack is to break security measures, that were introduced by the developer or the manufacturer. However to hack can also mean to make the application or device do more, that it was originally intended, to extend its capabilities. In this article we will show that by hacking (or rooting) a Samsung Smart TV it is possible to build a system that will allow remote management and monitoring of such device. Parental control is often very limited in functionality, usually just a PIN lock. By achieving arbitrary code execution we were able to extend the functionality drastically. This system is independent of manufacturer and can work and be extended even on older TV sets, that do not receive updates.