Publikacyje i wystōmpiynia

Excessive speed remains one of the main factors contributing to road‑traffic fatalities, while current Intelligent Speed Assistance (ISA) and map‑ or vision‑based systems often fail because of environmental limitations, outdated data, or high infrastructure costs. This paper proposes a cloud‑based Unified Driver Speed‑Assistance System (UDSAS), inspired by the European Train Control System (ETCS). The aim of the concept is to create a centralised and authoritative digital source of speed‑limit information and to enable real‑time computation of vehicle‑specific braking curves. The system integrates existing in‑vehicle components, including navigation, GNSS positioning, and speed‑limiter interfaces, to ensure low implementation cost and high scalability. The driver remains in the loop, while the system provides continuously updated speed guidance along the planned route. Optional extensions include offline operation, integration with vision‑based recognition, digital signage, Vehicle‑to‑vehicle (V2V) communication, and support for autonomous vehicles. The proposed solution addresses the current lack of a unified and trustworthy speed‑limit database and provides a simple, cost‑effective, and evolvable framework that can enhance road‑traffic safety, environmental performance, and driver comfort.

High-impedance ion-selective electrodes are susceptible to electromagnetic interference (EMI), which degrades potentiometric sodium and pH monitoring in automation. This study presented a disturbance-aware reconstruction pipeline for dual-channel recordings sampled at 1 kHz under laboratory conditions. A windowed sinusoidal regression was estimated and the 60 Hz interference component was subtracted using overlapping 0.5 s windows. Disturbances were detected per channel using robust amplitude and derivative scores with hysteresis, minimum duration rules, and mask dilation. Masked samples were replaced by an interpolated quiet-state baseline estimated from locally weighted scatterplot smoothing (LOWESS) using unmasked data only. Disturbed datasets contained events that masked up to 33% of the sodium record, which were reconstructed without changing the time axis. Two outputs were generated, including a drift-preserving trace and a drift-removed trace anchored to an initial reference level. Six smoothing kernels and a zero-phase second-order sections (SOS) Butterworth low-pass filter were benchmarked using quiet-only metrics. The SOS low-pass filter outperformed six alternative kernels in 22 of 24 experimental test cases, including one tie. Under the best quiet segment, robust variability decreased by approximately 50 times after reconstruction and low-pass filtering overall. Power spectral density values near -200 dB reflected a numerical reporting floor set by the safeguard, not floating-point quantisation.

Air quality measurements conducted using unmanned aerial vehicles offer researchers opportunities that were previously difficult to achieve. An increasing number of publications on this topic are appearing in the literature. However, the popularity of low-cost sensors is often associated with their limited accuracy and sensitivity to environmental changes. This work demonstrates that CO2 sensors can accurately indicate the actual concentration of this gas in the air, provided that the influence of temperature is considered.

Due to additional parameters and similar structure with PID, fraction-order (FO) PID controllers offer tuning flexibility for better performance. More degree of freedom poses challenges in applying simple tuning rules for FO-PID controllers. The dilemma of practical realization of the FO controllers further limits their benefits. This work proposes simple tuning rules for the parameters of the FO-PI controllers by following the frequency-domain methods. We apply three different approximation approaches to realize the controller implementation and validate its performance on a simulation example. Comparison with a FO-PID controller evidences the effectiveness of the proposed approach

Electromyography (EMG) signals, crucial for neuromuscular assessment, are frequently corrupted by noise, impairing signal fidelity and subsequent analysis across diverse applications. Conventional filters often inadequately address non-stationary noise or introduce signal distortion. This paper introduces an advanced deep learning framework for EMG denoising, centred on a U-Net-inspired convolutional autoencoder with integrated residual blocks and skip connections. Training utilised synthetic EMG data, closely emulating physiological frequency bands and burst dynamics, subsequently corrupted by a comprehensive noise model encompassing electrode, crosstalk, electronic, drift, and contact artefacts. Training was guided by a custom loss function that combined weighted mean squared error (MSE) with signal-to-noise ratio (SNR). The proposed autoencoder achieved substantial improvements, SNR increased from -0.95 dB (noisy) to 14.64 dB (denoised), and MSE was drastically reduced from 0.001493 V2 to 0.000041 V2 on the test dataset. Qualitative analysis confirmed effective noise suppression while retaining crucial EMG burst characteristics. This advanced framework offers a promising solution for robust restoration of EMG signals in practical settings.

Recent review articles highlight an exponential rise in publications on Digital Twin (DT) technology. Despite its recognised potential, DTs have yet to achieve widespread practical use. Following a presentation of the state of the art and an original conceptual diagram illustrating the technology, this work presents the key factors contributing to the gap between conception and implementation. Using experimental data from a laboratory water system and a reliability-based perspective, this analysis examines the practical limitations of DT application. The findings indicate that broader use of DTs depends on the technological maturity and reliability of all system components, which still require further development.

This study presents the application of an Internet of Things (IoT)-based system for environmental data acquisition in a scientific research setting. The system comprises a network of 12 sensor nodes and a central server. Each node is built around a D1 Mini module, which collects data from an attached sensor and transmits the measurements to the server via web requests. A server-side script processes these requests and stores the data in structured text files. The collected data can be analysed either in real time during the experiment or retrospectively. To ensure durability and reliability in outdoor conditions, all sensor nodes are enclosed in protective housings. This work highlights the practicality, cost-effectiveness, and efficiency of a custom-designed, application-specific IoT measurement system, demonstrating its suitability for rapid deployment in environmental monitoring applications.

Train on-board system calculates its speed based on values provided by several sensors. This work deals with a hypothetical case where the speed is estimated throughout 60 seconds during braking. Information from five sensors characterized by different error models is fused in three ways: mean, median, and the weighted method. Although the weighted method is the most complex, it exhibits the best fit to the reference value.

Understanding dataset characterisation is fundamental to achieving accurate statis- tical modelling, particularly in the context of Global Navigation Satellite System (GNSS) data analysis. GNSS data exhibit heavy-tailed and skewed distributions, prompting this study to eval- uate non-Gaussian models (Cauchy, Student’s t, lognormal, skew-normal) in modelling GNSS data collected from a stationary receiver. This study uses maximum likelihood estimation for parameter estimation with a confidence i nterval. I t evaluates t he m odel’s p erformance using log-likelihood analysis, the Akaike Information Criterion, the Bayesian Information Criterion, and the Root Mean Squared Error. The comparative assessment of these models highlights that lognormal and skew-normal outperform in capturing extreme deviations and provide a better fit than the normal distribution. These findings underscore the importance of selecting appropriate statistical models to enhance uncertainty quantification in GNSS-based measurements.

The paper deals with the problem of ion activity determination for a mixture by means of ion-selective electrodes. Mathematical model of the analysed phenomenon is described by the Nicolsky-Eisenman equation, which relates activities of ions and ion-selective electrode potentials. The equation is strongly nonlinear and, especially in the case of multi-compound assays, the calculation of ion activities becomes a complex task. Application of multilayer perceptron artificial neural networks, which are known as universal approximators, can help to solve this problem. A new proposition of such network has been presented in the paper. The main difference in comparison with the previously proposed networks consists in the input set, which includes not only electrode potentials but also electrode parameters. The good network performance obtained during training has been confirmed by additional tests using measurement results and finally compared with the original as well as the simplified analytical model.

Determination of pH using a typical glass electrode requires prior calibration in order to determine the electrode parameters. Knowledge about uncertainties of the parameters is insufficient to calculate the uncertainty of measured pH because of existing correlation. In the paper, an example illustrating the problem is presented. Two ways of proper uncertainty assessment are suggested: (1) analytical with removing the correlated variables and (2) numerical using Monte Carlo simulations. The second one seems to be much less time-consuming and allows easier investigations of the uncertainty properties.