The paper presents the use of applying circular polarization in a vision system. It describes how reflections and glare are reduced when illuminating a reflecting surface with circularly polarized light and collecting scattered light through the same circular polarizer. Even if usage of linear polarizers in vision systems is common, the use of circularly polarized light offers advantages due to the simplicity of the setup, which is composed of a single polarizing plate, without any need for accurate angular alignment, enhancing the contrast without significant loss of illumination due to the absence of dark field regions. Reduction of glare reduces image saturation, therefore allowing for a better contrast in images acquired from highly reflective surfaces.
Detection of broken rotor bars in induction motors based on sideband components near the fundamental stator current frequency often yields unreliable results under variable operating conditions due to masking effects and the dominant amplitude of the fundamental component. This study proposes a robust fault detection method which uses higher-order current harmonics, which demonstrate significantly reduced susceptibility to load variations and other masking phenomena. As principal contribution this study demonstrates that a convolutional neural network trained exclusively on data derived from a single operating condition achieves strong generalisation across a wide spectrum of unsteady operating regimes, spanning substantially different supply frequencies and load levels, with consistently high classification accuracy. Signal processing involves bandpass filtering around the fifth and seventh harmonic frequencies, envelope extraction via the Hilbert transform, continuous wavelet transform to generate time-frequency scalograms, and data augmentation through vertical shifts to simulate variations in defect-related frequency components under changing slip conditions. Scalograms corresponding to the fifth and seventh harmonics are treated as independent training samples, effectively expanding the dataset while leveraging physically redundant fault information. This single-point training paradigm directly addresses a critical industrial limitation, thereby enabling deployment of data-driven condition monitoring systems even under severe training data constraints.
The evaluation of measurement uncertainty is a task in metrology, and remains challenging when measurement models show nonlinearity, or when input quantities follow non-Gaussian probability distributions. Although the ISO/IEC Guide 98-3 (GUM) is widely applied in calibration practice, studies have highlighted situations in which its underlying assumptions may no longer be satisfied. In response to these developments, this paper presents a simulation-based comparative analysis of the GUM framework and the Monte Carlo method recommended in GUM Supplement 1, with a specific focus on temperature sensor calibration. We analysed a set of calibration scenarios, covering linear and nonlinear models, symmetric and asymmetric input distributions, and limited sample sizes. The comparison addresses uncertainty estimates, coverage behaviour, and computational aspects, allowing the practical consequences of each method to be assessed under controlled conditions. The results indicate that while the GUM approach remains suitable for near-linear models with approximately Gaussian inputs, Monte Carlo simulation provides a more faithful representation of the output distribution when these conditions are not met. For readers and practitioners, this study offers methodological insight and practical guidance for selecting an appropriate uncertainty evaluation approach in contemporary temperature sensor calibration, consistent with recent advances reported in the metrological literature.
This study aims to present the results obtained from the profilometric and reflectographic analyses of the painting on panel L’Ebbrezza di Noè, attributed to Antonio Bazzi (known as Il Sodoma), preserved at the Galleria Nazionale di Arte Antica in Palazzo Barberini (Rome, Italy). Utilizing an advanced integrated scanning system, we employed a high-resolution laser profilometer with a 405 nm laser line, a lateral resolution of 12.5 µm and a height resolution of 1.6 µm, along with a SWIR uncooled camera equipped with an InGaAs detector with a resolution of 320 × 256 pixels, operating within a spectral range from 900 to 1700 nm, to scan and analyze the artwork. By selecting significant details, we highlighted the potential of a multi-instrument approach in accurately interpreting the complex information revealed through diagnostic imaging of artworks. Specifically, profilometric images were compared with infrared and false-color images to determine whether hidden features beneath the paint layers can be detected through surface topography.
This research investigates the radon exhalation rate, natural radionuclide content, and associated radiological hazard to human health linked to Viterbo tuff stone, a material of notable historical and architectural relevance. Particular attention was given to radon exhalation behavior by examining both untreated and accelerated aged (through a salt crystallization test) samples. Radon exhalation rates were quantified using the Closed Chamber Method (CCM) in combination with a Durridge RAD7 real-time continuous radon detector. The measured exhalation rates were subsequently employed in the Markkanen room model to estimate potential indoor radon concentrations. In addition, the activity concentrations of 226Ra, 232Th and 40K were determined by means of high-purity germanium (HPGe) gamma-ray spectrometry. Radiological hazard was evaluated using several indices, including the absorbed gamma dose rate (D), the annual effective dose equivalent (AEDE), the activity concentration index (I), and the alpha index (Iα). Noteworthy, the findings of this study provide a valuable basis for future research aimed at developing a systematic framework for assessing radon release from building materials. Such an approach is essential for supporting decision-making processes, as it enables the identification of buildings that may be particularly susceptible to radiological risks, and therefore require targeted mitigation and protection measures.
