The journal Measurement Science Review has been published online since 2001. PUBLISHER: De Gruyter Brill, Paradigm Publishing Services https://reference-global.com/journal/MSR ARCHIVE from 2001 to 2024 is available at https://www.measurement.sk/ If you want to submit your article to Measurement Science Review you can use this site. Measurement Science Review is a scientific peer-reviewed journal of the Institute of Measurement Science, Slovak Academy of Sciences. The aim of the journal Measurement Science Review is to publish papers from scientific disciplines covering Measurement Science with an orientation toward the theory of measurement, measurement of physical quantities and measurement in biomedicine. The journal Measurement Science Review is published since 2001. Since 07 Mar 2008 the Journal has been published by: De Gruyter Poland Sp. z o.o., Sciendo; As of 01 Oct 2025, the Sciendo publishing platform operates under a new name — Paradigm — available at https://reference-global.com/journal/MSR Since Volume 8 (2008), each published paper is provided with a DOI (Digital Object Identifier). Please note that this web page related to Open Journal System is in use from January 2023, i.e. starting from Volume 23. Measurement Science Review is a fully open access scientific journal licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) eISSN: 1335-8871 Open volume since January 2025 Impact Factor: 1.3 Five Year Impact Factor: 1 Cite Score: 2.8 SJR: 0.291 SNIP: 0.754 JCI: 0.22 MNiSW: 70 Google Scholar h5-index: 13 Google Scholar h5-median: 19 Journal is indexed in 40 databases Languages: English Journal Subjects: Engineering, Electrical Engineering, Control Engineering, Metrology and Testing Scope of the Journal: Theoretical Problems of Measurement Measurement of Physical Quantities Measurement in Biomedicine
Slovak Academy of Sciences In partnership with: Paradigm Publishing Services
The Rapid Touchless 3D Fingerprint Imaging Based on Full-field Microscopic Fringe Projection Technique
Accurate three-dimensional (3D) fingerprint imaging is essential for next-generation biometric systems requiring high spatial resolution and microfeature fidelity. This study presents a novel, rapid, non-contact 3D fingerprint imaging system that integrates a seven-step phase-shifting fringe projection technique with a microscope-based optical configuration and structured RGB illumination. The system achieves lateral resolution better than 40 µm and vertical resolution below 5 µm, enabling precise reconstruction of ridge-valley structures and third-level features, such as sweat pores and ridge-edge contours. A comparative analysis of illumination wavelengths shows that green light provides the highest measurement accuracy, yielding a wrapped-phase standard deviation of 0.07 rad, a signal-to-noise ratio (SNR) exceeding 25 dB, and a root mean square error of 1.8 pixels in the unwrapped phase. Surface profiling results confirm the system’s capability to resolve ridge heights in the range of 45-65 µm and pore diameters between 80-150 µm. Compared to existing techniques, the proposed method offers enhanced spatial resolution, improved preservation of fine surface features, and a novel assessment of spectral effects on 3D reconstruction performance. With an acquisition time of less than one second, this system demonstrates a strong potential for applications in high-security biometric authentication, forensic investigation, and spoof-resistant technologies. Future research will focus on enabling real-time data processing, system miniaturization, and improved performance under dynamic or uncontrolled environmental conditions.
Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
Measurements of Potential Positive Ion Sources of Gadolinium
The long-term goal of the research is to develop a positive (Gd II) ion source for further experiments, particularly for the studies of negative ions. Gadolinium (Gd) is a hardly volatile element (melting temperature 1585 K, boiling point 3273 K) with metallic properties; therefore, the atomization and ionization of Gd requires specific efforts. In this study, two methods were used to obtain the sources of Gd atoms and ions and to study their spectroscopic properties: laser-induced breakdown spectroscopy (LIBS) and a hybrid plasma technique — a combination of hollow cathode discharge (HC) with low-pressure radiofrequency inductively coupled plasma (RF-ICP). For this research, samples of gadolinium metal and Gd2O3 were used.
inductively coupled lowpressure RF plasma
Copyright (c) 2026 Measurement Science Review https://creativecommons.org/licenses/by-nc-nd/4.0
Accuracy Improvement of DIC Method Displacement Measurement Based on Image Stabilization
Digital image correlation (DIC) is a cornerstone technique for displacement measurement, offering transformative potential for structural health monitoring (SHM) applications due to its non-contact nature and cost-effectiveness. However, in practical applications, image jitter caused by extended monitoring distances and suboptimal optical conditions degrades accuracy. Given the significant relationships among pixel resolution, stability, and DIC measurement accuracy, this paper proposes a study to improve image stability using corner detection methods to improve the measurement accuracy of DIC technology. First, a camera captures multiple dynamic displacement images of slowly moving objects at different distances. Then, image stabilization technology is applied to the images for algorithmic processing, followed by DIC analysis. The results show that under normal acquisition conditions, the relative errors of DIC-analyzed data at different distances range from 3.8 % to 8.6 %, with displacement errors increasing with distance. In contrast, the relative errors of data processed with image stabilization technology range from 3.2 % to 6.0 %, demonstrating accuracy improvements of 4.2 % to 34.1 % (the improvement in accuracy increases with increasing test distance). This indicates that image stabilization technology can effectively improve the accuracy of DIC-based displacement measurement.
An Intraoperative Method for Measuring Acetabular Cup Inclination and Anteversion in Total Hip Arthroplasty Using an Inertial Measurement Unit
Aiming to guide acetabular cup placement within the “safe zone” (40°±10° inclination, 15°±10° anteversion), this paper introduces an intraoperative inertial measurement unit (IMU)-based method for measuring cup orientation in total hip arthroplasty (THA), featuring registration and measurement phases. The registration phase establishes human-body-to-world coordinate transformations without reliance on bony landmarks or invasive contact, while the measurement phase enables real-time estimation of cup inclination and anteversion. Four experiments were conducted using a 3-axis tilt table to validate: 1) IMU’s basic angle estimation accuracy (RMSE 0.315°–0.423°); 2) robust acquisition of rotation axis vectors during the registration phase; 3) high measurement accuracy for inclination (RMSE 0.278°) and anteversion (RMSE 0.296°), with an average error vector magnitude of 0.373° (well within clinical tolerance); and 4) acceptable errors due to IMU pose changes (average error: 0.987°) and IMU drift (error increase: approximately 3.5-fold over 20 minutes, mitigated by mid-procedure reregistration). While theoretically and experimentally feasible, the method relies on patient-operating table immobility and lacks clinical validation. Offering high accuracy and cost-effectiveness, it shows potential as a standard THA navigation method with further optimization.
An Analytical Solution for the Eddy Current Coil Located over a Conductive Disc with Two Layers of Different Radii
In this work, an analytical model was derived for an air-core probe placed above a disc whose upper layer has a larger diameter than the lower layer. The proposed solution can be utilised in eddy current testing of screws, rivets, valves, and other elements with similar geometry, and made of electrically conductive materials. The final formulas for the change in the probe impedance were obtained using the truncated region eigenfunction expansion (TREE) method and expressed in closed form. The calculations were performed in Matlab for two disc configurations made of bronze, brass, and graphite. The results of the calculations of the impedance components were compared with the results of the measurements. Very good agreement was obtained within the entire frequency range. In no case did the errors in determining changes in resistance or reactance exceed 2 %.
Methods and Practical Aspects of Measuring 1/f Noise in Zero-drift Operational Amplifiers
Operational amplifiers are an important ingredient of analogue electronic circuits, and their noise performance is an essential design parameter. However, at low frequencies, data provided by component manufacturers usually cover only a limited range (typically 0.1 Hz to 10 Hz). The frequencies below this range, critical for high-stability and precision circuits, such as signal conditioning stages, voltage references, or digitizing voltmeters, are rarely addressed. Therefore, it is often necessary to characterize the parts. The traditional noise measurement technique requires the amplifier to be configured for very high gain, which is seldom the way the amplifier is used. Different techniques for voltage noise density measurement are presented and compared in this paper, including two methods that allow for noise measurement with a unity gain configuration - a mode that has not been explored until now. Practical aspects required to obtain reliable results are highlighted. A side product of this work is an open-hardware design of an ultra low-noise amplifier, which can be used to measure the noise performance of operational amplifiers or resistors using regular measuring equipment readily available at universities or industrial laboratories.
Mechanisms of Pressure Loss in Chamfered Orifice Plates: Coupled Effects of Plate Thickness and Reynolds Number
To study the effect of chamfered structure on the flow characteristics of a throttled orifice plate, orifice plates with different thicknesses and chamfered structures were analyzed using the computational fluid dynamics (CFD) method. The results show that the thickness of the orifice plate significantly affects the flow characteristics, and the influence of the Reynolds number (Re) on the pressure loss coefficient shows an opposite trend on thin and thick orifice plates (the loss of the thin plate increases with the increase of Re, while that of the thick plate decreases). The chamfered structure effectively improves the flow pattern in the orifice plate and reduces the pressure loss. Lower pressure-loss coefficients are usually obtained with a chamfer angle of 30°–45°. In addition, the thickness of the openings affects the optimal chamfer angle: as thickness increases from 0 to 2.5 mm, the optimal angle shifts from 15° to 45°.
single-hole and multi-hole orifice plate
Sensor of Mechanical Quantity Based on the RF Principle
This paper presents the design and analysis of a mechanical quantity sensor based on a radio-frequency principle, while addressing the high power consumption and circuit complexity of existing solutions. The proposed approach focuses on minimizing power consumption and reducing the number of required components, while enabling simple tunability by modifying a single parameter, namely the inductance. A compact compliant mechanical body serves as a capacitive element and exhibits a linear relationship between the applied force and plate displacement. Measurements confirm clear dependencies of capacitance and oscillator frequency on the acting force, supported by simulations and theoretical calculations. The optimized 3D-printed design ensures flexibility, repeatability, and controlled deformation. The resulting sensor enables tunability and wireless transmission of measured force (weight) data via radio frequency (RF)-based frequency changes, while also providing a suitable platform for the use of stiffer, more brittle materials, potentially leading to significant miniaturization of the sensor dimensions.
The Uncertainty of the Indirect Thermographic Measurement of the Temperature of the Semiconductor Element
The indirect thermographic measurement of the temperature of a semiconductor element consists of two stages. The first involves a thermographic measurement of the case temperature of the semiconductor element. The second involves determining the relationship between the case temperature and the semiconductor element’s temperature. This is possible based on simulation studies using the finite element method (FEM). This type of measurement is subject to a certain level of uncertainty. The measurement uncertainty depends on a number of factors that affect the measurement result, as well as on the probability distributions assigned to them. As a result of the conducted research, the uncertainty value was determined. This uncertainty concerns the indirect thermographic measurement of the semiconductor element’s temperature.
A newly proposed low-noise and high temporal resolution X-ray imaging detector based on the curved solenoid design is described in this paper. Three-dimensional models are developed in CST Particle Studio (CST-PS) to systematically investigate the temporal and spatial magnifications. The effects of the ramp rate of the modulation signal between the photocathode and the acceleration mesh, the different electron emission positions of the photocathode, the magnetic field strength, and the distance between the microchannel plate (MCP) and the curved solenoid outlet on the overall performance of the whole structure are studied, which shows that the electron emission position has a dominant effect over the temporal transit and temporal dispersion. Additionally, the temporal magnification factor increases with the ramp rate of the modulated signal. Within the effective photocathode of Φ 16 mm, the temporal magnification factor exceeds 13. Furthermore, the spatial magnification is linearly proportional to the distance between the MCP and the solenoid outlet, suggesting that MCPs of varying sizes can be effectively coupled.
Computer Simulation of Calculation Frequency Deviation from Odd Frequency Spectrum
The homogeneity of the static magnetic field is crucial for nuclear magnetic resonance (NMR) experiments. The NMR instrumentation is often used to map this field. A spectrum containing information about the static magnetic field is obtained from the signal of a suitable experiment. The digital signal is usually available as a set of real numbers, which can be processed by Fourier transformation either directly or after being transformed into complex numbers. In the first case, the resulting spectrum consists of complex numbers representing an even frequency function. In the second case, the resulting numbers are also complex, but the spectrum is odd. The calculation with an even frequency function was performed and published at this institution. The calculation with an odd function was also solved and published at this institution, but it was not the main focus of the research and publication. Therefore, some questions arose, which this research sought to answer. In both cases, the research method was computer simulation.
Accurate Frequency Detection Technology Based on Quantization Phase Accumulation Effect
To address the quantization phase accumulation effect caused by different frequency signals during phase comparison, an accurate frequency detection technology is presented. Through quantized phase analysis, statistics, and processing of the phase difference between the different frequency signals, high-precision measurement of the measured signal is realized. This detection method uses any phase difference within a group period as the switch signal of the counter, overcoming the ±1-word quantization error in traditional frequency measurement and improving measurement accuracy and detection speed. By integrating FPGA technology, the volume of the measurement equipment has been simplified, and the cost and power consumption have been reduced. The experimental results and analysis show that the method is advanced and scientific, and the actual measurement uncertainty reaches the level of 10-13 at 1 s. Compared with traditional frequency measurement technology, this frequency detection technology offers significant advantages in power consumption, device size, and detection rate. It is crucial for Beidou satellite time synchronization, radar detection, high-precision space positioning, and other high-tech applications.
Indirect Thermographic Measurement of the Contact Temperature of an Electromechanical Relay
The temperature of an electromechanical relay is related to the value of the contact resistance. The value of the contact resistance depends, among other things, on the degree of wear and oxidation of the contacts. A too high temperature of the relay contacts leads to their further degradation and an increase in contact resistance. For this reason, monitoring this value is important. In this paper, monitoring of the contact temperature of an electromechanical relay using indirect thermographic temperature measurement is proposed. This approach consists of two parts: the first part is based on thermographic measurement of the relay case, and the second part estimates the contact temperature based on the relationship between the case temperature (measured by a thermal camera) and the contact temperature. The finite element method (FEM) was used in the second part. The results obtained using this method were validated through comparison with experimental measurements acquired by an alternative method.
Evaluation of Dimensional Accuracy Evaluation in Additive Manufacturing with Complex Measurement Artifacts
Accuracy and repeatability are among the key parameters in assessing the quality of additive manufacturing (AM) outputs, as they determine the usability of parts in technical and industrial practice. This study compares a standardized artifact defined by ISO/ASTM 52902 with a newly designed custom artifact, with measurements performed using a coordinate measuring machine (CMM) and 3D scanning. The dimensional deviations obtained for individual axes were statistically evaluated to determine whether the differences between artifacts and measurement methods were statistically significant. Additional analysis based on deviation mapping against the CAD model provided a more detailed view of process behavior for both simple and complex geometries. The results confirmed that the designed artifact can be considered a viable alternative to standardized solutions and offers advantages in practical applications, particularly in the context of quality control and optimization of measurement procedures.
Research on Tool Wear State Recognition Method Based on Multi-Scale Feature Extraction and Deep Residual Network Fusion
To enhance the intelligence of machining processes, accurate recognition of tool wear states has become a key issue in the manufacturing field. However, due to the non-stationary and high-dimensional nature of cutting signals, traditional methods face significant challenges in feature extraction and state classification. In the context of cutting processes, challenges such as difficulty in identifying tool wear states and the complex composition of monitoring information features persist. To address these issues, this paper proposes a deep learning model that integrates multi-scale feature extraction with a residual connection network (Multi-scale ResNet). Specifically, cutting vibration signals are processes using continuous wavelet transform (CWT), which enables the conversion of time-frequency information into images. The proposed deep learning model is then used for feature extraction and state identification. The proposed model is validated through cutting experiments conducted on γ-TiAl alloys. Experimental results show that the Multi-scale ResNet model achieves higher recognition accuracy than traditional models such as convolutional neural networks – support vector machines (CNN–SVM), Transformer, and ResNet in the initial and normal wear stages. It effectively mitigates misjudgments associated with initial and normal wear, achieving a prediction accuracy of 93.8 %, a recall rate of 94.2 %, and an F1 score of 94 %. This model offers a novel and effective approach for tool wear state monitoring, contributing to improved cutting processing efficiency and increased intelligence in production.
EM Design, Realization and Measurement of a Hexagonal SIW-Based Bandpass Filter for Wireless Communication
This paper presents a new design for a third-order bandpass hexagonal substrate integrated waveguide-defected ground structure (SIW-DGS) filter for mobile communications applications. The proposed design operates at 7.90 GHz. It combines the flexibility of rectangular cavities and the efficiency of circular cavities to enhance the filter’s electromagnetic (EM) performance and selectivity. To facilitate the filter analysis and improve its computational efficiency, the developed structure is decomposed into individual components and simulated separately using high frequency structure simulator (HFSS) and applied wave research (AWR) software that help extracting critical parameters based on a general coupling matrix (CM) using RT/Rogers 4003 substrate with a dielectric constant εr = 3.55, tan δ = 0.0027 and a thickness of 0.508 mm. The simulated EM filter response shows an insertion loss of up to 1.58 dB and a return loss of less than −23.5 dB over the operating frequency range from 7.77 GHz to 8.05 GHz, demonstrating a high EM performance. Accordingly, an efficient third-order bandpass hexagonal SIW-DGS filter prototype of a 17 × 22.9 × 0.542 mm³ volume is realized and demonstrates an accurate EM response with a defined zero transmission, as expected. Measured results of the optimized third-order bandpass hexagonal SIW-DGS filter with a trisection cross-coupled structure and a fractional bandwidth (FBW) of 3.54 % show high agreement with the calculated and simulated results, which validates the efficiency of the developed design as a highly accurate candidate for modern communication systems requiring compact structures.
Conducted noise, radiated noise, EMI in photovoltaic systems, noise measurement, electromagnetic noise analysis.
Test and Analysis of the Influence of Lake Water Obstacle on the Near-field Timing Signal of BPC
The lake water environment is a common and complex scenario for the application of radio time synchronization. When the timing signal enters lake water, signal strength attenuation and phase shift occur, which greatly affect time service performance. By comparing the differences in signal field strength and time difference at the same distance from the transmitting station along two different direction paths – one through soil and the other through lake water – the influence of the freshwater lake transmission medium on the propagation of BPC timing signals within the ground wave range was analyzed. This paper uses the BPC timing signal as an example to describe the propagation mode and attenuation characteristics of the signal when it enters the lake water environment, and establishes a BPC field strength and time difference measurement system to analyze possible field strength and time difference variation laws of the BPC timing signal at different propagation distances in lake water. Studies show that at a distance of 25 km from the transmitting station, when the signal passes through lake water, the field strength attenuation is approximately 0.24 dBμV/m and the time difference increases by approximately 8.04 μs. At a distance of 138 km from the transmitting station, when the signal passes through lake water, the field strength attenuation is approximately 0.6 dBμV/m and the time difference increases by approximately 14.1 μs.
Statistical Characterisation of GNSS Data for a Stationary Receiver using Non-Gaussian Distributions
Accurately characterising datasets is crucial for effective statistical modelling, particularly when analysing Global Navigation Satellite System (GNSS) data. While traditional approaches often assume a Gaussian distribution, real-world GNSS datasets frequently exhibit heavy-tailed and skewed properties, prompting the need to explore alternative statistical models. The study examines the suitability of non-Gaussian distributions, specifically the Laplace, skew-normal, skew-t, and generalised hyperbolic (GH) distributions, for modelling GNSS data obtained from a stationary receiver. Using empirical GNSS datasets, we estimate parameters within confidence intervals (CIs) through weighted maximum likelihood estimation (WMLE). Model performance is assessed using log-likelihood analysis, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and root mean squared error (RMSE). Our comparative analysis shows that heavy-tailed and skewed distributions, particularly those offering greater flexibility in capturing extreme deviations, consistently out-perform the conventional normal distribution. Among the non-Gaussian models considered, the GH distribution provides the best overall performance. These results emphasise the importance of selecting appropriate statistical models to improve uncertainty quantification in GNSS-based measurements.
Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
BTS-NEUNET: Brain Tissue Segmentation via White Shark Optimized Features based Nested U-Net
Segmenting brain tissue can provide valuable insights into its structure and function. Magnetic resonance imaging (MRI)-based tissue segmentation is an essential procedure for improving tractography and quantifying brain microstructure. In this work, a novel BTS-NEUNET framework is proposed for brain tissue segmentation based on multimodal MRI images. The multimodal MRI images, such as T1W, T2W, perfusion-weighted imaging (PWI), and diffusion-weighted imaging (DWI), undergo pre-processing using the wavelet transform-based bilateral (WTBB) filter and the curvelet transform-based adaptive Gaussian notch (CTBAGN) filter to enhance the image quality. A hybrid DenseGoogLe network is used to extract the relevant features from the enhanced multimodal images. The proposed BTS-NEUNET method uses the White Shark Optimization Algorithm to select features from MRI images. The four types of brain tissues such as grey matter, white matter, cerebrospinal fluid, and ischemic lesions are classified using a Deep Belief Network (DBN). Brain tissues are classified using a nested, attention-based U-Net. The proposed BTS-NEUNET method's performance is assessed using Accuracy, Precision, Recall, Specificity, and F1-Score. The proposed DenseGoogLeNet method for feature extraction achieves an overall Accuracy of 1.64 %, 4.53 %, 0.76 %, and 3.94 % higher than ShuffleNet, ResNet, GhostNet, and MobileNet, respectively. The proposed BTS-NEUNET method achieves the highest Accuracy rate of 99.60 %. The proposed BTS-NEUNET method improves overall Accuracy by 1.92 %, 1.34 %, and 1.74 % over existing methods such as DDSeg, optimal support vector machine (SVM), and chaotic based enhanced Firefly Algorithm integrated with Fuzzy C-Means (CEFAFCM), respectively.
Evaluation of Absorbing Material Shielding Effectiveness Using Baum–Liu–Tesche Based Method
The shielding efficacy of the metal cavity can be effectively enhanced using absorbing materials coated on the inner wall of the cavity. With highly potential materials such as graphene, silicon carbide (SiC), polytetrafluoroethylene (PTFE), and Ni/rGO in shielding, an analytical method based on the Baum–Liu–Tesche (BLT) equation is proposed to evaluate their shielding effectiveness (SE) in the open cavity. Compared to the electromagnetic field simulation software, i.e., CST Studio Suite, the resonant frequency of the coated cavity using the proposed model can be obtained with an average error of no more than 8 dB. The findings reveal that PTFE with a 1 mm thickness coated on the inner wall of the cavity can achieve the best shielding effect, but the resonant frequency of the cavity remains unchanged. On the other hand, the increased material thickness by adding the other three materials has no effect on the frequency located at the resonance modes of TE101 and TE102, except for the high-order resonance TE103. However, it is found that when a 2 mm thickness of these three materials is coated, the shielding ability achieves its best status. It verifies that the proposed BLT analytical method presents superiority in terms of computational rapidity and accuracy, with fewer resources occupied.
Analysis of the Influence of Selected Parameters on the Success Rate of Shotgun Firing on UAVs
In contemporary conflicts, land forces – including artillery units and even individual soldiers – have increasingly faced threats posed by small, inexpensive yet highly effective unmanned aerial systems (UAS). Even though extensive research has been conducted, it has proven challenging to support artillery efforts to counter Class I UAVs effectively. There have been several relatively successful attempts to examine artillery combat capabilities using UAV assets, depending on the technologies and methodologies used. Mathematical models and simulation techniques offer the possibility of predicting hit hazards, determined by detection capabilities, range determination, the probability of small-arms engagement, and the law of destruction. Specifically, this study analyses the probability that a Class I UAV is hit by massed shotgun fire (pellet cloud) as a function of key firing parameters, in particular the aiming error, lead determination error, and the dispersion characteristics of the weapon. The proposed Monte Carlo modelling framework enables parametric studies of counter‑UAV shotgun engagements and provides quantitative guidance for selecting suitable weapons and shooting conditions.
Metrological Evaluation of Optical Surfaces Using Goniometric and Spectrophotometric Techniques
This paper presents a comprehensive metrological approach for evaluating the appearance and optical properties of complex material surfaces using advanced goniometric and spectrophotometric techniques. The methodology is implemented on the newly developed robotic goniospectrophotometer RoboCapp, designed at the Czech Metrology Institute (CMI) to enable spectral and spatially resolved measurements across arbitrary illumination and observation geometries. To the best of our knowledge, this is the first measurement system to integrate quasi-monochromatic illumination with a polarization-independent detection unit providing a dynamic range spanning ten orders of magnitude. This unique combination enables high-accuracy bidirectional measurements of reflected and transmitted flux, even for samples exhibiting strong diffusivity or very low transmittance, while supporting the numerical emulation of arbitrary illuminants and detector responsivity functions. The proposed measurement procedures ensure high accuracy and full SI traceability in determining the Bidirectional Reflectance Distribution Function (BRDF). The performance of the system is demonstrated through measurements of a quasi-Lambertian diffuse standard sample (Spectralon 99) in 0°/45° geometry over the visible spectral range (400–780) nm. An uncertainty budget is provided, with results showing that the dominant contribution arises from the sample–detector aperture distance.
Bidirectional Reflectance Distribution Function
A Compact Design of a Two-stage Piecewise Linear ADC Used for Sensor Linearity Improvement
This paper presents a compact two-stage piecewise linear analog-to-digital converter (ADC) that enhances sensor linearity while featuring a reduced design complexity. Unlike a traditional two-stage piecewise linear ADC, the proposed architecture achieves the same resolution with fewer components by using a single flash ADC for both conversion stages and by adding a comparator at the start of each conversion stage. The flash ADC has a resolution of (n/2−1) bits, where n is the total resolution of the two-stage piecewise linear ADC. This compact architecture enables efficient and simultaneous linearization and digitization of sensor output while significantly reducing energy consumption and implementation costs. Numerical results confirm the compactness and cost-effectiveness of the proposed design, showing up to a 75 % reduction in comparator count and up to a 25 % reduction in resistor count compared to the traditional design of the two-stage piecewise linear ADC with the same total resolution. These significant savings make the proposed two-stage piecewise linear ADC design well-suited for applications with strict energy and space constraints.
Quality Assurance in the Calibration of Platinum ResistanceThermometers (Pt-100) at NIS
Ensuring the quality and accuracy of measurements is a critical aspect of the calibration process. One of the major challenges that calibration laboratories face is limited participation in international or bilateral comparisons and proficiency testing. This research aims to identify potential deviations or weaknesses in the measurement and calibration processes by implementing both scientific and practical methods to enhance measurement quality. A Python-based automation program was developed to detect deviations from acceptable tolerance limits in real-time during calibration. The proposed methodology was applied to the calibration of a platinum resistance thermometer (Pt-100) at the Thermal Measurements Laboratory of the National Institute for Standards (NIS), Egypt. The experiment demonstrated successful implementation: when measurements exceeded predefined tolerance limits, the Python program triggered an alert to halt the calibration and initiate error diagnostics. Calibration was conducted at 70 °C, 150 °C, and 200 °C, with quality assurance procedures specifically applied to the 70 °C point as a case study. This methodology provides a replicable model for laboratories aiming to integrate conventional quality control (QC) with automated monitoring. The integration of statistical process control (SPC) and automation enhances reliability, minimizes human error, and offers a replicable framework for strengthening quality assurance in calibration laboratories.
Enhancing the Performance of Pulse Position Coded Excitation for Photoacoustic Imaging by Denoising Autoencoder
Photoacoustic (PA) imaging is a hybrid technique that combines light illumination and ultrasound detection to generate images of tissue. Advances in laser technology allow laser diodes that are low cost, compact, and have a high pulse repetition frequency (PRF) to improve the frame rate and signal-to-noise ratio (SNR) of PA imaging. This improvement is achieved by employing PA-coded excitation techniques. However, PA-coded excitation is limited by side-lobes and artifact signals, particularly when the code length is short. Pulse position modulation (PPM) is a type of coded excitation that achieves the highest code gain with a short code length. This study explores a signal-processing approach that integrates PPM-coded excitation with a denoising autoencoder to reduce the generated side lobs and artifact signals and enhance the SNR of the PA signals. The denoising autoencoder is designed to address the varying shapes of side lobes that occur with different PPM code lengths, resulting in improved attenuation and removal of artifacts and background noise. The results show that the denoising autoencoder is particularly effective when the amplitude of the decoded PA signal exceeds that of the background noise, enabling reduced acquisition time and memory requirements for RF data collection. This work offers a promising approach to overcoming the limitations of PPM-coded excitation in PA imaging, supporting further improvements in the quality and reliability of PA signals for various medical applications.
Development of a Time-Integration Method for Analyzing the Photoresponse of Image Sensors
This study introduces a novel method for analyzing image sensors, supported by both theoretical and experimental verification. We first theoretically derived that when sinusoidal light with constant amplitude is directed onto an image sensor with fixed exposure times, the radiant energy reaching each pixel remains unchanged, irrespective of frequency fluctuations. However, when a rapid brightness variation significantly influences the photoresponse of the photodiodes in the image sensor, pixel values are expected to vary with frequency. To experimentally assess the photoresponse of image sensors, we developed a light-emitting diode (LED) light source capable of emitting sinusoidal waves up to 1 MHz, paired with a photosensor designed for this frequency range. We then exposed the image sensors of three cameras to the sinusoidal light and analyzed the frequency dependence of the pixel values. The experimental results demonstrated that the pixel values remained nearly constant (relative error of approximately 5 % or less) up to 1 MHz, showing no frequency dependence in the photoresponse within this range. These findings validate the theoretical analytical method and confirm that the image sensors of the three cameras accurately captured sinusoidal light up to 1 MHz without being influenced by the physical properties of the sensors or design parameters. However, this does not establish the limit or cutoff frequency of the image sensors.
Robust Pose Estimation by Fusing Partial Color and Depth Imagery
Pose estimation algorithms are an extensively studied research topic in the field of computer vision and machine learning. Even though many algorithms attempt to solve the problem, most algorithms are still not accurate enough to recover poses in real-world applications. Therefore, we have developed a new approach that utilizes depth cues and optical flow measurements that presents improved pose recovery in real-world pose estimation applications. We also present a camera calibration method that creates projection matrices for pose estimation from cameras, which enables angular comparison for relative pose estimates from two sensor systems positioned at different locations. We applied and tested the proposed algorithm in the laboratory settings and compared our findings with a commercial and a gold standard pose estimation system. Angular pose errors were reported.
Using the Characteristics of the Sample Range of Repeated Observations of a Measurand to Estimate Its Numerical Value and Type A Standard Uncertainty
The article lists situations where it is impractical or impossible to record all observation results during repeated measurements, only their minimum and maximum values. The Monte Carlo method is used to analyze the efficiency of various estimators of the expected value for different distribution laws of observation results. The possibility of determining the estimate of the numerical value and type A standard uncertainty of the measurand using the sample range of results of multiple observations is considered, taking into account their number and the distribution law. The Monte Carlo method is used to obtain the dependence of the coefficient for converting the sample range of multiple measurement results into the sample standard deviation for different distribution laws. The novelty of the article lies in the experimental procedure presented for obtaining the dependence of the conversion coefficient on the number of indicating measuring instrument readings without determining their distribution law. An example is provided for evaluating the numerical value and type A measurement standard uncertainty using the parameters of the sample range of measured humidity values from a standard hygrometer. The novelty of this work is the empirical determination of α for real indication measuring instrument (IMI) readings, which can be used to propose calibration-like procedures for range-based assessment.
AD-HOLDER: Alzheimer's Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images
Alzheimer's disease (AD) is an irreversible brain condition that impairs memory and cognitive processes. However, existing Alzheimer detection methods have shown low diagnostic accuracy (ACC) due to limited images and inefficient feature analysis. In this paper, a novel AD-HOLDER model is proposed for early recognition of AD using dual imaging methods: magnetic resonance imaging (MRI) and positron emission tomography (PET). The proposed AD-HOLDER model presents an integrated framework that uniquely combines Deep Image Prior (DIP) denoising, Histogram of Oriented Gradients (HOG) feature extraction, and Light Gradient Boosting Machine (LGBM) classification for AD detection from MRI and PET images. The HOG method aims to enhance the spatial and contextual representation of neurological patterns by combining structural features from MRI and statistical features from PET images. A classifier based on the LGBM processes the dual features to classify images as either normal or abnormal, effectively capturing complex patterns and improving classification ACC. The abnormal region is segmented using a graph-based segmentation (GBS) model to accurately detect affected areas for accurate detection of AD. The effectiveness of the proposed AD-HOLDER model is evaluated using ACC, specificity (SPE), precision (PRE), recall (REC), and F1-score (F1) based on the OASIS dataset. The proposed AD-HOLDER model achieves a classification ACC of 99.12 % through machine learning. The proposed AD-HOLDER model increases overall ACC by 1.55 %, 25.44 %, and 3.14 % compared to the Gradient Boosting Algorithm (GBA), Explainable Artificial Intelligence (XAI), and Computer-Aided Diagnosis (CAD) systems, respectively.
The Three Most Common Needs for Training on Measurement Uncertainty
Measurement uncertainty is key to assessing, stating and improving the reliability of measurements. An understanding of measurement uncertainty is the basis for confidence in measurements and is required by many communities; among others in national metrology institutes, accreditation bodies, calibration and testing laboratories, as well as in legal metrology, at universities and in different metrology fields. An important cornerstone to convey an understanding of measurement uncertainty is to provide training. This article identifies the status and the needs for training on measurement uncertainty in each of the above communities as well as among those teaching uncertainty. It is the first study to do so across many different disciplines, and it merges many different sources of information with a focus on Europe. As a result, awareness on the training needs of different communities is raised and teachers of uncertainty are supported in addressing their audiences’ needs as well as in improving their uncertainty-specific pedagogical and technology-related knowledge. The three needs that are most commonly encountered in the communities requiring an understanding of measurement uncertainty, are 1) to address a general lack of training on measurement uncertainty, 2) to gain a better overview of existing training on measurement uncertainty in several communities, and 3) to deliver more training on specific technical topics including use of a Monte Carlo method for propagating probability distributions and treating multivariate measurands and measurement models. These needs will serve to guide future developments in uncertainty training and will, ultimately, contribute to increasing the understanding of uncertainty.