Recently, Linux has undergone a significant progress and since it provides many helpful features to companies and home users, it has become one of the most commonly used operating systems in IT industry. Considering the popularity of available Linux distributions, three Linux desktop distributions were selected for further evaluation. The idea of this paper is to evaluate performance and compare three different Linux distributions and their influence on processor, memory, graphics system and disk drive performance. Measurements were performed with a three different benchmarking tools specialized for a specific computer component. All Linux operating system distributions were installed on the same desktop computer and based on the achieved performance measurement results it was concluded that the best results were achieved by using Pop!_OS 20.04. Linux distribution, which is very surprising because it is a relatively new distribution on the market.

Contour detection is an important step in contour based object analysis in 2D images. We present a novel method for contour detection optimized for contour traversal tasks in 2D images. The method is based on morphological operations and detects a one-pixel wide contour around objects in binary images. Performance of the method is evaluated on ground truth images from our dataset created by manually tracing the contour in images. Proposed method achieves high DICE coefficient overlap to ground truth images and superior detection compared to other state-of-the-art methods.

Virtualization has experienced a dramatic expansion recently and today is ubiquitous in modern IT industry since it provides numerous benefits to companies and individual users. It increases efficiency, flexibility and scalability of IT equipment by enabling different software-based environments on a single physical hardware. Each virtual machine is a separate instance that is completely independent and separated from the computer hardware and it runs on emulated hardware. Emulated hardware is managed by virtualization tool that provides lower resources when compared to physical hardware. This paper presents a performance evaluation of three different virtual machines run by three recent versions of Windows operating system, namely Windows 7^TM Professional, Windows 8.1^TM Professional and Windows 10^TM Professional, on a host computer system run by Linux Ubuntu. Performance measurement results show that Window 7 is the most suitable virtual operating system since it obtains the best performance when run on a Linux host.

Fire protection is one of the activities that follow the development of technology in real-time and implements all the innovations of a detection system. This paper presents a unique solution for the development of an autonomous robot for the prevention, detection, and extinguishing of fires by studying the problem of choosing the optimal early-detection sensor in the infrared part of the spectrum, which characterizes the highest level of excitation in the state of prevention. The robot is equipped with several different sensors arranged in a hierarchical structure. Thermal detection has proven to be a significant investment that can be adapted to the different complexity of the objects to be protected, taking into account image processing and modular implementation of the required sensors. To this end, it is necessary to calibrate systems for different thermal cameras. The calibration procedure on seven cameras and two pyrometers resulted in data required for input-data correction and anomaly detection. The results of the analysis confirmed that devices of a higher price range have a lower deviation from the reference value compared to low-cost technical solutions. At the same time, results were observed indicating malfunction of more expensive devices, whose data exceed the specified nominal accuracy. Thanks to the performed calibration procedure and the obtained results, the observed problem is not an obstacle for implementation in an autonomous robotic system and can be used to correct the input data required for computer analysis.

The future of mobile systems relies on big data and fast information processing to improve efficiency, user experience, and system autonomy. With the massive growth of intelligent and mobile devices, along with the development of computational and communication technologies, a vast amount of data is being generated which needs to be processed and distributed as fast as possible, while reducing costs. Therefore, gathering and data processing, as well as efficient distribution of messages across the network, is the key problem of future mobile systems. In this paper, we are proposing a cost-effective method that relies on fog and edge computing principles called FOGO (FOG Optimization). The method proposes a three layer architecture that utilizes an upgraded existing infrastructure where the mobile nodes will create a fog service layer that could provide processing of medium-sized messages and the distribution of the results across the network. Furthermore, cloud computing is also considered but only in a case of necessity for a large-sized data processing. The proposed system architecture is described along with the method flowcharts, operating algorithms, and proposed message structure. Furthermore, the metrics for the system performance evaluation are proposed, as well as possible application domains.