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.

The purpose of this research was to analyze the applicability of infrared thermography in assessing the condition of an e-bike battery. The main challenge was to interpret the surface radiation detected by a thermal imaging camera. In addition to basic challenges of thermography, such as determining the emissivity, the angle of image acquisition, the problem of low resolution and thermal inertia, it was necessary to understand the physical behavior of the elements inside the battery and battery pack that respectively determine the dynamics and shape of the thermal pattern. E-bike batteries are mainly composed of 18650 format Li-ion cells, and the topology of the battery cell is related to the energy profile of the system. With the aim of establishing a link between the surface radiation patterns and the parameters of the individual 18650 cells that dictate the internal thermal behavior, a detailed analysis of the battery pack interior, upon its lifetime, was performed. Based on the results of the capacity analysis of the individual cells and determination of the exact position of the cells within the structure of the battery, a visualization and comparison with the thermographic records were performed to determine whether infrared thermography is able to diversify faulty cells. The conclusion of the study is that it is not possible to make a judgment about the thermal pattern expression or the pattern shape based on the estimated capacity values; however, based on the pattern, it is possible to draw a conclusion about the homogeneity of the capacity of the individual battery cells.

Infrared thermography as contactless method for determining the temperature distribution on the surface is used for analyzing the impact of intense pulsed light hair removal device (IPL) on the skin. Depth of light penetration depending of wavelength is described as well as absorption curves and IPL impulse shapes. Energy balance and IPL impulse influence on the skin is analyzed. Melanin temperature rise by different fluence operation and temperature distribution in the modeled hair is used in order to determine overall skin temperature rise. Estimated energy balance provided by mathematical model has been confirmed with experimental results. Performed measurements, beside determination of the right emissivity, required the identification of most significant parameter in the process which proved to be the skin reference temperature and real temperature rise. Practical IPL application with detailed body temperature analysis is comprehensively described and thermal imaging interpretation problem and determination of the temperature rise is observed.

Transformers are the most important elements in the power system. Due to their mass and complexity, they require constant monitoring and maintenance. Maintenance of power transformers increases the availability of the power system. The large number of substations and the specifics of their locations make condition-based maintenance (CBM) useful as part of the system’s on-demand response. Unlike other system responses, the transformer contains a large amount of uncertain information, both qualitative and numerical. A large amount of information is necessary to implement CBM, but due to the often incomplete information, an analysis tool is essential. In this paper, a multi-level condition assessment framework based on evidential reasoning is proposed. A model for condition-based maintenance of a power transformer and procedures for the aggregation process based on evidential reasoning are presented. The implementation of the decomposition model with appropriate weights of a baseline and general attributes was made. Based on the decomposition model, the data and ratings of baseline attributes were collected. By carrying out the aggregation process, the ratings of the baseline attributes, as well as the ratings of the condition of the individual elements and the overall rating of the system condition as a whole, for several points in time, were obtained. The scientific contribution of the work is the proposal of an analysis that provides an insight into the condition of a complex technical system based on a single numerical value, thus determining its priority in the maintenance process.

This paper presents the development of a modular and low-cost IoT (Internet of Things) system for remote monitoring of essential parameters during wine fermentation, designed for small and medium-sized wineries—Winnie. The system combines distributed embedded sensing units with centralized colorimetric analysis and real-time data transmission to a remote server. Barrel-mounted devices measure wine and cellar parameters (temperature, humidity, and CO₂ concentration), while a central hub performs colorimetric SO₂ analysis using an RGB color sensor and automated fluid handling. Communication between the Barrel and Hub device relies on the RS-485 protocol, providing robustness in harsh winery conditions. All measurements are securely transferred via Wi-Fi. A hash-based integrity check ensures continuous and reliable data collection. The modular design, simple installation, and user-friendly web interface make the system accessible to winemakers. This technology provides a scalable method for digitalizing conventional winemaking processes by reducing the cost and complexity of wine quality monitoring.