Publikacje i wystąpienia

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Intelligent Speed Assistance (ISA) and map‑ or vision‑based systems often fail because of environmental limitations, outdated data, or high infrastructure costs.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.

Reliable ion-selective electrode (ISE) monitoring in industrial matrices is often compromised by electromagnetic interference (EMI). Traditional uncertainty models assume independent Gaussian noise; however, ISE data sampled at 1 kHz reveals temporal instabilities and cross-channel dependencies that violate these frameworks. This study presents a two-phase metrological evaluation of ISE responses (EH-01 and ENa-01). First, statistical characterization reveals extreme non-Gaussian behavior, with excess kurtosis reaching κ=20, negative skewness (γ = -3.40), and a Pearson correlation shift from r ≈ 0.26 to r ≈ 0.69 during impulsive activity. In the second phase, we evaluate uncertainty propagation using the Guide to the Expression of Uncertainty in Measurement (GUM) and validate results via Monte Carlo simulation (MCs). Our findings demonstrate that GUM underestimates measurement risk for EH-01 by 15.0% due to heavy-tailed noise, while overestimating ENa-01 uncertainty by 22.0% due to extreme central peak density. These results invalidate the second-moment-only GUM approximation in high-interference settings, necessitating multivariate, non-Gaussian uncertainty evaluation. These findings motivate the development of ML-based filtering approaches to mitigate EMI-induced transients and potentially restore GUM validity in real-time industrial process control.

Pomiary jakości powietrza wykonywane z użyciem bezzałogowych statków powietrznych stwarzają dla badaczy możliwości do tej pory trudne do osiągnięcia. W literaturze pojawia się coraz więcej publikacji na ten temat. Popularność niedrogich czujników wiąże się jednak z ich niską dokładnością i wrażliwością na zmianę warunków otoczenia. W tej pracy wykazano, że czujniki CO2 dość dobrze wskazują faktyczne stężenia tego gazu w powietrzu pod warunkiem uwzględnienia wpływu temperatury.

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.