Measurement Science Review
type: channel
description: 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: 0.8 Five Year Impact Factor: 0.9 Cite Score: 1.9 SJR: 0.240 SNIP: 0.538 JCI: 0.22 MNiSW: 70 Google Scholar h5-index: 13 Google Scholar h5-median: 21 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
publisher: Slovak Academy of Sciences In partnership with: Paradigm Publishing Services
language: en
publication name: Measurement Science Review
issn: 1335-8871
items: A New Low-Noise X-Ray Imaging Detector
items: Computer Simulation of Calculation Frequency Deviation from Odd Frequency Spectrum
items: Indirect Thermographic Measurement of the Contact Temperature of an Electromechanical Relay
items: Evaluation of Dimensional Accuracy Evaluation in Additive Manufacturing with Complex Measurement Artifacts
items: Research on Tool Wear State Recognition Method Based on Multi-Scale Feature Extraction and Deep Residual Network Fusion
items: Test and Analysis of the Influence of Lake Water Obstacle on the Near-field Timing Signal of BPC
items: Statistical Characterisation of GNSS Data for a Stationary Receiver using Non-Gaussian Distributions
items: Evaluation of Absorbing Material Shielding Effectiveness Using Baum–Liu–Tesche Based Method
items: Metrological Evaluation of Optical Surfaces Using Goniometric and Spectrophotometric Techniques
items: A Compact Design of a Two-stage Piecewise Linear ADC Used for Sensor Linearity Improvement
items: Enhancing the Performance of Pulse Position Coded Excitation for Photoacoustic Imaging by Denoising Autoencoder
items: Development of a Time-Integration Method for Analyzing the Photoresponse of Image Sensors
items: Robust Pose Estimation by Fusing Partial Color and Depth Imagery
items: Using the Characteristics of the Sample Range of Repeated Observations of a Measurand to Estimate Its Numerical Value and Type A Standard Uncertainty
items: AD-HOLDER: Alzheimer's Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images
items: The Three Most Common Needs for Training on Measurement Uncertainty
items: Evaluating the Detection Performance of a Drug Screening Device
items: A Nested DUAL-axis Accelerometer with EnhancedTemperature Robustness for Testing Across the EntireOperational Temperature Range
items: TRI-BCC: Tri-Level Breast Cancer Classification via Transfer Learning Networks with Histopathogical Images
items: DEEP-BTS: Deep Learning based Brain Tissue Segmentation using ResU-Net Model
items: Analysis and Graphical Evaluation of Pressure Changes in Pneumatic Circuits for Industrial Applications
items: Filter and Sampling Rate Optimization for PPG-Based Detection of Autonomic Dysfunction: An ECG-guided Approach
items: Detection of Laryngeal Pathologies from Voice using EMD-based Mel-Spectrograms and Scalograms with AlexNet
items: A Novel Scalable Trust-Aware Deep Reinforcement Learning Algorithm for Energy-Efficient and Secure Routing in Software-Defined Wireless Sensor Networks for IoT
items: Adaptive Measurement Selection for Scalable Distributed Graph Optimization in Multi-UAV Relative Positioning
items: Exploration of Infrared Thermography as an Alternate Tool for the Detection of Gastric Diseases
items: KazStandard Primary Dew/Frost Point Generator
items: Skin Impedance Analysis for Drug Delivery: Integration of Poisson-Boltzmann-Nernst-Planck Model and Ebola Optimisation Algorithm
items: GGLCM: A Real-Time Early Anomaly Detection Method for Mechanical Vibration Data with Missing Labels
items: Feed Rate-Adaptive Predictive PH Interpolation for Real-Time NURBS Toolpath Execution in High-Speed CNC Machining

A New Low-Noise X-Ray Imaging Detector
type: item
description: 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.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: curved solenoid
  topic: keywords
subject: temporal magnification
  topic: keywords
subject: X-ray detector
  topic: keywords
creator: Liping Tian
creator: Yang Yang
creator: Lingbin Shen
creator: Penghui Feng
creator: Bo Wang
creator: Yan Xu
creator: Jinshou Tian
creator: Yongsheng Gou
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-23
publication date: 2026-01-23
starting page: 40
ending page: 45
doi: 10.2478/msr-2026-0006

Computer Simulation of Calculation Frequency Deviation from Odd Frequency Spectrum
type: item
description: 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.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: nuclear magnetic resonance
  topic: keywords
subject: NMR
  topic: keywords
subject: odd frequency function
  topic: keywords
subject: complex frequency spectrum
  topic: keywords
subject: computer simulation
  topic: keywords
creator: Peter Andris
creator: Ivan Frollo
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-22
publication date: 2026-01-22
starting page: 10
ending page: 13
doi: 10.2478/msr-2026-0002

Indirect Thermographic Measurement of the Contact Temperature of an Electromechanical Relay
type: item
description: 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.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: electromechanical relay
  topic: keywords
subject: finite element method (FEM)
  topic: keywords
subject: indirect thermographic measurement
  topic: keywords
subject: thermography
  topic: keywords
creator: Krzysztof Dziarski
creator: Łukasz Drużyński
creator: Arkadiusz Hulewicz
creator: Józef Piechocki
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-03-04
publication date: 2026-03-04
starting page: 67
ending page: 72
doi: 10.2478/msr-2026-0009

Evaluation of Dimensional Accuracy Evaluation in Additive Manufacturing with Complex Measurement Artifacts
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: additive manufacturing
  topic: keywords
subject: measurement artifact
  topic: keywords
subject: 3D scanning
  topic: keywords
subject: coordinate measuring machine
  topic: keywords
subject: CMM
  topic: keywords
creator: Richard Joch
creator: Miroslav Cedzo
creator: Alžbeta Briliaková
creator: Jozef Mrázik
creator: Mário Drbúl
creator: Jaromír Markovič
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-23
publication date: 2026-01-23
starting page: 33
ending page: 39
doi: 10.2478/msr-2026-0005

Research on Tool Wear State Recognition Method Based on Multi-Scale Feature Extraction and Deep Residual Network Fusion
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: tool wear
  topic: keywords
subject: multi-scale features
  topic: keywords
subject: deep residual network
  topic: keywords
subject: cutting vibration
  topic: keywords
subject: continuous wavelet transform（CWT）
  topic: keywords
creator: Erliang Liu
creator: Cong Liu
creator: Yuhang Du
creator: Baiwei Zhu
creator: Limin Shi
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-22
publication date: 2026-01-22
starting page: 14
ending page: 23
doi: 10.2478/msr-2026-0003

Test and Analysis of the Influence of Lake Water Obstacle on the Near-field Timing Signal of BPC
type: item
description: 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.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: BPC
  topic: keywords
subject: ground wave
  topic: keywords
subject: field strength
  topic: keywords
subject: low-frequency time-code
  topic: keywords
creator: Fan Zhao
creator: Luxi Huang
creator: Xin Wang
creator: Zhen Qi
creator: Xiaoqian Ren
creator: Ping Feng
creator: Yu Hua
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-23
publication date: 2026-01-23
starting page: 24
ending page: 32
doi: 10.2478/msr-2026-0004

Statistical Characterisation of GNSS Data for a Stationary Receiver using Non-Gaussian Distributions
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: non-Gaussian
  topic: keywords
subject: weighted maximum likelihood estimation
  topic: keywords
subject: generalised hyperbolic distribution
  topic: keywords
subject: goodness-of-fit test
  topic: keywords
subject: uncertainty quantification
  topic: keywords
subject: confidence interval
  topic: keywords
creator: Abu Bantu
creator: Józef Wiora
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 338
ending page: 346
doi: 10.2478/msr-2025-0037

Evaluation of Absorbing Material Shielding Effectiveness Using Baum–Liu–Tesche Based Method
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: shielding effectiveness
  topic: keywords
subject: BLT equation
  topic: keywords
subject: absorbing material
  topic: keywords
subject: cavity shielding
  topic: keywords
subject: electromagnetic shielding
  topic: keywords
creator: Chunmei Dou
creator: Rumeng Wang
creator: Hsiung-Cheng Lin
creator: Yan Zhang
creator: Jiayi Chai
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-03-04
publication date: 2026-03-04
starting page: 57
ending page: 66
doi: 10.2478/msr-2026-0008

Metrological Evaluation of Optical Surfaces Using Goniometric and Spectrophotometric Techniques
type: item
description: 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.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: Bidirectional Reflectance Distribution Function
  topic: keywords
subject: goniometric spectrophotometry
  topic: keywords
subject: optical surface metrology
  topic: keywords
subject: RoboCapp
  topic: keywords
subject: spectral radiance factor
  topic: keywords
creator: Jana Gerneschová
creator: Jan Rybář
creator: Geiland Porrovecchio
creator: Marek Šmíd
creator: Peter Onderčo
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 400
ending page: 404
doi: 10.2478/msr-2025-0044

A Compact Design of a Two-stage Piecewise Linear ADC Used for Sensor Linearity Improvement
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: Measurement of Physical Quantities
  topic: category
subject: sensor linearization
  topic: keywords
subject: flash ADC
  topic: keywords
subject: two-stage piecewise linear ADC
  topic: keywords
subject: comparator count
  topic: keywords
subject: resistor count
  topic: keywords
subject: compact design
  topic: keywords
creator: Jelena Jovanović
creator: Dragan Denić
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 315
ending page: 320
doi: 10.2478/msr-2025-0034

Enhancing the Performance of Pulse Position Coded Excitation for Photoacoustic Imaging by Denoising Autoencoder
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: photoacoustic imaging
  topic: keywords
subject: denoising autoencoder
  topic: keywords
subject: averaging technique
  topic: keywords
subject: signal-to-noise ratio
  topic: keywords
subject: artifact reduction
  topic: keywords
subject: pulse position modulation
  topic: keywords
creator: Abdulrhman Alshaya
creator: Suhail Alshahrani
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-22
publication date: 2026-01-22
starting page: 1
ending page: 9
doi: 10.2478/msr-2026-0001

Development of a Time-Integration Method for Analyzing the Photoresponse of Image Sensors
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: digital camera
  topic: keywords
subject: image sensor
  topic: keywords
subject: photoresponse
  topic: keywords
subject: frequency dependence
  topic: keywords
subject: photometry
  topic: keywords
subject: sinusoidal light
  topic: keywords
creator: Nobuaki Shimoji
creator: Yuto Suzuki
rights: Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-03-04
publication date: 2026-03-04
starting page: 46
ending page: 56
doi: 10.2478/msr-2026-0007

Robust Pose Estimation by Fusing Partial Color and Depth Imagery
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: Measurement of Physical Quantities
  topic: category
subject: point cloud
  topic: keywords
subject: ransformation
  topic: keywords
subject: relative pose
  topic: keywords
subject: coherent point drift
  topic: keywords
subject: Kinect II
  topic: keywords
subject: Vicon
  topic: keywords
creator: Mehmet Akif Alper
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 309
ending page: 314
doi: 10.2478/msr-2025-0033

Using the Characteristics of the Sample Range of Repeated Observations of a Measurand to Estimate Its Numerical Value and Type A Standard Uncertainty
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: repeated measurements
  topic: keywords
subject: extreme values of a sample
  topic: keywords
subject: sample range
  topic: keywords
subject: midrange
  topic: keywords
subject: Monte Carlo method
  topic: keywords
subject: estimates of the measurand
  topic: keywords
subject: type A measurement uncertainty
  topic: keywords
creator: Igor Zakharov
creator: Olesia Botsiura
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 366
ending page: 370
doi: 10.2478/msr-2025-0040

AD-HOLDER: Alzheimer's Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: Measurement of Physical Quantities
  topic: category
subject: Measurement in Biomedicine
  topic: category
subject: MRI images
  topic: keywords
subject: PET images
  topic: keywords
subject: Histogram of Oriented Gradients
  topic: keywords
subject: machine learning
  topic: keywords
subject: Light Gradient Boosting Machine
  topic: keywords
subject: graph-based segmentation
  topic: keywords
creator: Mahalakshmi S
creator: Valarmathi K
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 389
ending page: 399
doi: 10.2478/msr-2025-0043

The Three Most Common Needs for Training on Measurement Uncertainty
type: item
description: 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.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: measurement uncertainty
  topic: keywords
subject: Education
  topic: keywords
subject: training
  topic: keywords
creator: Katy Klauenberg
creator: Peter Harris
creator: Philipp Möhrke
creator: Francesca Pennecchi
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-12-03
publication date: 2025-12-03
starting page: 257
ending page: 275
doi: 10.2478/msr-2025-0029

Evaluating the Detection Performance of a Drug Screening Device
type: item
description: This study focuses on detecting addictive substances in the human body using modern instrumental methods, specifically the Dräger DrugTest 5000 (DDT 5000) device. Addictive substances are classified by legal status, chemical structure, and patterns of use. The study evaluates various biological matrices for drug detection, identifying oral fluid as the most suitable option due to its non-invasiveness and appropriate detection window. The core objective is to experimentally verify the detection limits of selected substances using the DDT 5000 and compare the findings with the manufacturer’s specifications. Results indicate that most detection limits are below or consistent with those declared by the manufacturer, except for heroin. A notable finding concerns tetrahydrocannabinol (THC), where extremely low and potentially unreliable detection limits were observed, suggesting the need for further verification. In conclusion, the DDT 5000 demonstrates high sensitivity, specificity, and accuracy, making it a reliable tool for rapid detection of most abused substances.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: addictive substances
  topic: keywords
subject: detection
  topic: keywords
subject: human body
  topic: keywords
subject: detection limit
  topic: keywords
creator: Luděk Král
creator: Jiří Tesař
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 380
ending page: 388
doi: 10.2478/msr-2025-0042

A Nested DUAL-axis Accelerometer with EnhancedTemperature Robustness for Testing Across the EntireOperational Temperature Range
type: item
description: This paper investigates a nested biaxial micro-electro-mechanical systems (MEMS) accelerometer with low coupling error, derives the kinetic model equations of the accelerometer, and performs finite element simulations of the operating modes and mechanical coupling of the structure. The accelerometer uses an inner and outer nested structure with non-rigid links to reduce cross-coupling between orthogonal sensitive axes. Closed-loop control is employed to further improve the device stability and reduce the coupling coefficient. The preparation and testing of the structure was finally completed. Without compensation, tests show that the accelerometer works in closed-loop mode with low cross-coupling, and the inter-axis cross-coupling coefficients are less than 1.31 % and 2.4 %, respectively. The accelerometer exhibits an X-axis output drift of 1.26 mg/°C and a Y-axis output drift of 0.91 mg/°C in its natural state over the full temperature range of −40 °C to 60 °C. After compensation by the temperature compensation system, the X-axis output bias is optimized to 0.02008 mg/°C and the Y-axis output bias to 0.02041 mg/°C.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: micro-electro-mechanical systems accelerometer
  topic: keywords
subject: low coupling
  topic: keywords
subject: nested twin axis
  topic: keywords
creator: Yanchao Ren
creator: Guodong Duan
creator: JingJing Jiao
creator: Xiaoping Hu
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 321
ending page: 326
doi: 10.2478/msr-2025-0035

TRI-BCC: Tri-Level Breast Cancer Classification via Transfer Learning Networks with Histopathogical Images
type: item
description: Breast cancer (BC) is the second most common hereditary cancer in women and is most likely to affect women. The high cost of treating BC has made its prevention an important health issue. However, early detection of BC is a time-consuming and difficult task when using mammogram images. This work introduces a novel TRI-BCC model for BC detection using transfer learning-based tri-level classification with histopathological images. Initially, input images undergo a denoising phase using the Adaptive Brightness Contrast Dynamic Histogram Equalization (ABCDE) technique, which removes noisy artifacts to enhance overall image quality. Following this pre-processing step, a data augmentation phase is employed to synthetically expand the training dataset, thereby improving model generalization and robustness against overfitting. The augmented and pre-processed images are subsequently provided as input to the hybrid Golden Eagle-Whale Optimization (GWO) algorithm, for the segmentation of lesions. In the tri-level classification, transfer learning (TL) models: CapsuleNet, EfficientNet, ShuffleNet, GoogleNet, MobileNet and ResNet are employed to categorize images as benign or malignant (Level-I). Benign cases (Level-II) include Adenosis (AS), Phyllodes tumor (PT), Tubular adenoma (TA) and Fibroadenoma (FA), while malignant cases (Level-II) include Papillary (PC), Mucinous (MC), Lobular (LC), and Ductal (DC) carcinoma. Malignant images are processed using a Randomized Decision Tree (RDT) to classify them into different cancer stages (Level-III) for precise diagnosis. Based on experimental analysis, the TRI-BCC model achieves an overall accuracy of 99.06 % for the categorization of BC. The proposed TRI-BCC model improves overall accuracy by 6.11 %, 0.78 %, 2.66 %, and 13.18 % compared to UNet+YOLO, fine-tuned networks, hybrid deep neural network, and Pa-DBN-BC, respectively.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: breast cancer
  topic: keywords
subject: transfer learning
  topic: keywords
subject: histopathological images
  topic: keywords
subject: tri-level classification
  topic: keywords
subject: Golden Whale Optimization algorithm
  topic: keywords
creator: Sridevi Rajalingam
creator: Kavitha Maruthai
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 327
ending page: 337
doi: 10.2478/msr-2025-0036

DEEP-BTS: Deep Learning based Brain Tissue Segmentation using ResU-Net Model
type: item
description: Brain tissue segmentation (BTS) in MRI is essential for diagnosing neurological disorders, mapping brain structures, and analyzing disease progression. A major challenge in BTS is intensity inhomogeneity, where non-uniform illumination in MRI scans causes intensity variations, making it difficult to accurately differentiate gray matter (GM), cerebrospinal fluid (CSF), and white matter (WM). To address these challenges, a novel deep learning-based DEEP-BTS model has been proposed for BTS with brain MRI images. The input images are collected from the BrainWeb dataset, where MRI images undergo skull stripping to remove unnecessary regions. After skull stripping, the collected images are pre-processed using a contrast stretching adaptive trilateral filter (CSATF) to improve image quality, reduce noise artifacts, and perform augmentation to increase data diversity to ensure robust model training. The pre-processed images are then fed into the ResU-Net, which segments different brain tissues, including CSF, GM, and WM. The proposed DEEP-BTS model is evaluated based on its accuracy (AC), specificity (SP), recall (RE), precision (PR), F1 score (F1), Jaccard index (JI), and Dice index (DI). The proposed DEEP-BTS achieved a segmentation accuracy of 98.91 % for BTS. The proposed ResU-Net outperformed Fuzzy C-Means, M-Net, and U-Net methods, achieving 98.33 % CSF, 98.04 % GM, and 99.15 % WM, indicating improved segmentation accuracy.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: brain tissue segmentation
  topic: keywords
subject: contrast stretching adaptive trilateral filter
  topic: keywords
subject: cerebrospinal fluid
  topic: keywords
subject: gray matter
  topic: keywords
subject: white matter
  topic: keywords
creator: Sivaprakash P
creator: Banumathi J
creator: Ashis Kumar Mishra
creator: Jayapriya P
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 371
ending page: 379
doi: 10.2478/msr-2025-0041

Analysis and Graphical Evaluation of Pressure Changes in Pneumatic Circuits for Industrial Applications
type: item
description: Measuring and evaluating the condition of pneumatic systems (PS) is an important identification tool for many industrial applications. Therefore, this paper focuses on the detection of changes (faults) in these circuits by analysing system pressures. The proposed approach allows dynamic monitoring of the pressure profile when the run-in processes have an identical sequence (i.e. deviations and changes become steady at a specific frequency). The analysis performed and the subsequent graphical representation provide valuable insights into the running process for management. Proof-of-concept experiments are carried out with a sensor (AVENTICS) on a simple pneumatic circuit with constant motion, which serves as a sample example. Their results show the usefulness of this method, and the collected data will serve as a suitable basis for implementation in machine learning models.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: pneumatic systems
  topic: keywords
subject: pressure changes (faults)
  topic: keywords
subject: pneumatic systems, pressure changes (faults), filtering of unwanted states, failure detection
  topic: keywords
subject: failure detection
  topic: keywords
creator: Marek Vagaš
creator: Ondrej Majercak
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-12-03
publication date: 2025-12-03
starting page: 223
ending page: 228
doi: 10.2478/msr-2025-0026

Filter and Sampling Rate Optimization for PPG-Based Detection of Autonomic Dysfunction: An ECG-guided Approach
type: item
description: Photoplethysmography (PPG) is well suited for wearable health applications, but has a lower frequency spectrum than electrocardiography (ECG) and is more affected by motion artifacts. In this study, ten signal filters from three categories were investigated in combination with different sampling rates to evaluate their effects on PPG signal quality. A correlation and accuracy analysis was performed comparing the interbeat intervals detected in PPG and ECG using Pearson correlation and absolute error. The results showed that specific filters with sampling rates as low as 40 Hz perform well in detecting autonomic neuropathy. The results highlight the potential of PPG with optimized filters and sampling rates for clinical screening of the autonomic nervous system (ANS) in wearable health monitoring.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: photoplethysmography
  topic: keywords
subject: electrocardiography
  topic: keywords
subject: healthcare
  topic: keywords
subject: wearable
  topic: keywords
subject: autonomous
  topic: keywords
subject: neuropathy
  topic: keywords
creator: Yi-Hui Kao
creator: ِDanyal Shahmirzadi
creator: Sung-Tsang Hsieh
creator: Wen-Fong Wang
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-10-01
publication date: 2025-10-01
starting page: 200
ending page: 211
doi: 10.2478/msr-2025-0024

Detection of Laryngeal Pathologies from Voice using EMD-based Mel-Spectrograms and Scalograms with AlexNet
type: item
description: In this paper, a novel method for detecting of laryngeal pathologies using deep neural networks and time–frequency signal processing techniques is presented. The proposed approach combines empirical mode decomposition (EMD) and wavelet analysis to extract discriminative features from healthy and pathological voice recordings obtained from the Saarbrücken Voice Database (SVD). Each voice signal is pre-processed and decomposed into intrinsic mode functions (IMFs), from which the most relevant IMF is selected based on a temporal energy criterion. Two sets of features are derived from the selected IMF: Mel-frequency cepstral coefficients (MFCCs) and continuous wavelet transform (CWT) coefficients. These features are converted into Mel-spectrogram and scalogram images, respectively, which serve as inputs to the AlexNet convolutional neural network (AlexNet-CNN) for automatic binary classification. To the best of our knowledge, this is the first study to incorporate scalogram representations with AlexNet-CNN in the context of pathological voice detection. The results show that the proposed method achieves a classification accuracy of 85.66 % when using Mel-spectrograms and 86.4 % when using scalograms, demonstrating its potential for effective and interpretable voice pathology screening.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: laryngeal pathology detection
  topic: keywords
subject: voice signal processing
  topic: keywords
subject: empirical mode decomposition
  topic: keywords
subject: Mel-spectrogram
  topic: keywords
subject: scalogram
  topic: keywords
creator: Sofiane Cherif
creator: Abdelhafid Kaddour
creator: Abdelmoudjib Benkada
creator: Said Karoui
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-12-03
publication date: 2025-12-03
starting page: 276
ending page: 283
doi: 10.2478/msr-2025-0030

A Novel Scalable Trust-Aware Deep Reinforcement Learning Algorithm for Energy-Efficient and Secure Routing in Software-Defined Wireless Sensor Networks for IoT
type: item
description: Wireless Sensor Networks (WSNs) are the backbone of Internet of Things (IoT) ecosystems, but they remain constrained by limited energy, dynamic topologies, and increasing security threats. Conventional metaheuristic-based routing protocols typically optimize either energy efficiency or security, but rarely achieve both in a scalable manner. To address this research gap, we propose a trust-aware Software Defined Wireless Sensor Network (SDWSN) framework that integrates the Coati Optimization Algorithm (COA) for multi-objective routing with a hyperelliptic curve (HEC)-based blind signcryption scheme for lightweight yet robust data security. The novelty of this work lies in the joint optimization of energy, delay, trust, and hop count while simultaneously ensuring confidentiality, integrity, and anonymity through blind signcryption. Unlike traditional ECC and RSA, the proposed HEC-based scheme reduces computational complexity, making it suitable for resource-constrained IoT devices. The architecture leverages software-defined networking (SDN) programmability and the OpenFlow protocol to dynamically adapt routes based on real-time trust and energy metrics. Simulation results in NS-3 show that the proposed COA-HEC model significantly outperforms existing schemes (SEHR, IBFA, ESMR, GMPSO) by improving throughput (18.8 %–59.4 %), packet delivery ratio (by 4.8 %–12.4 %), and reducing average delay (up to 61 %) and energy consumption. The proposed framework establishes a scalable and secure routing paradigm for real-time IoT applications such as industrial automation, healthcare monitoring, and smart cities, thus advancing the state of the art in trust-aware SDWSNs.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: Measurement of Physical Quantities
  topic: category
subject: Coati Optimization Algorithm
  topic: keywords
subject: blind signcryption
  topic: keywords
subject: OpenFlow protocol
  topic: keywords
subject: trust and energy
  topic: keywords
subject: delay and hop
  topic: keywords
creator: Jasmine Alponse
creator: Yaashuwanth C
creator: Prathibanandhi K
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 358
ending page: 365
doi: 10.2478/msr-2025-0039

Adaptive Measurement Selection for Scalable Distributed Graph Optimization in Multi-UAV Relative Positioning
type: item
description: Autonomous relative positioning is a critical challenge for unmanned aerial vehicle (UAV) swarms. In this study, we address the optimization of measurement constraint selection in the distributed relative positioning process of large-scale UAV swarms. We introduce a distributed graph optimization (DGO) scheme for swarm relative positioning, which enables global consistent relative position estimation through limited inter-UAV information sharing. To prevent the calculation time from escalating with swarm size, this method allows users to specify the number of relative measurement constraints used in the calculation. Building on the Cramér-Rao Lower Bound (CRLB) and Fisher Information Matrix (FIM) theory, we further propose a theoretically optimal method for selecting relative measurement constraints. Different from traditional distributed optimization methods that use fixed constraints, the proposed method can adaptively select the most theoretically advantageous constraints to improve accuracy, resulting in higher precision and improved adaptability to dynamic environments. To validate the effectiveness of the proposed method, we conducted numerical experiments with different swarm sizes and sensing error conditions. The results show that the proposed method has higher accuracy and stability compared to self-pose estimation methods and other measurement selection approaches, while having a low computational load. This work represents the first attempt to incorporate the FIM into constraint selection for distributed localization of UAV swarms.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: UAV swarm
  topic: keywords
subject: distributed graph optimization
  topic: keywords
subject: Fisher Information Matrix
  topic: keywords
subject: optimal measurement constraint
  topic: keywords
creator: Chengsong Xiong
creator: Zheng You
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-10-01
publication date: 2025-10-01
starting page: 190
ending page: 199
doi: 10.2478/msr-2025-0023

Exploration of Infrared Thermography as an Alternate Tool for the Detection of Gastric Diseases
type: item
description: The electrical impulses, known as electrogastrograms (EGGs), originate from the muscles of the stomach and are due to the physiological activity of the digestive system. The characteristics of these signals can be used to diagnose various human gastrointestinal disorders. Although the recording of EGG signals is a well-recognized diagnostic procedure, acquiring these signals from human subjects is difficult due to movement artifacts, and difficulties in electrode placement, and is also a time-consuming technique. Infrared thermography (IRT) is a non-contact and non-invasive technique used to detect various pathologies in humans. In this work, an attempt was made to establish the correlation between the information content (IC) of electrogastrograms and the information content of abdominal thermal images, in both normal and bradygastric subjects using Rényi entropy. The results show a strong correlation between the entropy of EGG signals and abdominal IRT images, in both normal and bradygastric subjects, with a correlation coefficient of 0.95 and 0.92, respectively. Since the information in the EGG signals and IRT images are strongly correlated, IRT can therefore be proposed as a diagnostic technique to replace EGG in the assessment of digestive diseases.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: electrogastrograms
  topic: keywords
subject: infrared thermography
  topic: keywords
subject: bradygastria
  topic: keywords
subject: Rényi entropy
  topic: keywords
subject: gastrointestinal disorders
  topic: keywords
subject: non-invasive diagnosis
  topic: keywords
creator: Donna Miriam Roy
creator: Kamalanand Krishnamurthy
creator: R. L. J. de Britto
creator: Emmanuel Cyril
rights: Copyright (c) 2025 Measurement Science Review https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-03
publication date: 2025-12-03
starting page: 248
ending page: 256
doi: 10.2478/msr-2025-0028

KazStandard Primary Dew/Frost Point Generator
type: item
description: This article presents studies on the dew point/frost temperature generator using the single-pressure (1-P) and single-temperature (1-T) method first developed by KazStandard to improve the calibration accuracy of dew point hygrometers. The method of applying the dew point temperature scale is based on the supply of humid air with high instability through a thermostatic saturator, which enables increased measurement accuracy. The characteristics include a study of the saturator's efficiency, the stability of the dew point temperature reproduction and a comparison with a calibrated dew point hygrometer. The study was conducted in the dew point temperature range from −50°C to 20°C, and the sources of measurement uncertainty were analyzed.
subject: Articles
  topic: section
subject: Measurement of Physical Quantities
  topic: category
subject: standards
  topic: keywords
subject: measurements
  topic: keywords
subject: humidity generator
  topic: keywords
subject: dew point temperature
  topic: keywords
subject: uncertainty
  topic: keywords
subject: saturator
  topic: keywords
creator: Assylbek Zhumagali
creator: Temirbolat Akimzhanov
creator: Raiymbek Zhumagali
creator: Sultanbek Smagulov
creator: Bahytkul Baihozhaeva
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-10-01
publication date: 2025-10-01
starting page: 172
ending page: 177
doi: 10.2478/msr-2025-0021

Skin Impedance Analysis for Drug Delivery: Integration of Poisson-Boltzmann-Nernst-Planck Model and Ebola Optimisation Algorithm
type: item
description: Transdermal drug delivery (TDD) is a non-invasive approach for administering therapeutic medications through the skin. This research proposes a spine–leaf resistor–capacitor (SLRC) circuit to analyse the impedance characteristics of different skin layers during drug delivery. To optimise the SLRC circuit parameters, the Ebola Optimisation Algorithm (EOA) minimises errors and improves computational efficiency. The Poisson–Boltzmann–Nernst–Planck (PBNP) model is used to calculate input parameters such as ion concentrations and electrotransport flux, to derive the drug concentration based on input parameters. A MATLAB simulator calculates drug penetration across stratified skin regions. According to the SLRC results, the Montague, constant phase element (CPE), and Tregear models performed better in predicting drug penetration and electrotransport flux than more conventional models. Compared to the traditional Montague and Tregear models, the EOA improved performance by reducing the mean absolute percentage error (MAPE) to as low as 0.134. The proposed model achieved an R2 value of 0.98 when validated against the impedance datasets, confirming high prediction accuracy. Furthermore, simulated results revealed that an optimal frequency of 4 × 10³ Hz provides the best impedance and drug penetration, demonstrating the importance of frequency and current density in increasing drug levels. The proposed model enhances the precision and efficiency of TDD, offering significant potential for clinical and pharmaceutical applications.
subject: Articles
  topic: section
subject: Measurement in Biomedicine
  topic: category
subject: transdermal drug delivery
  topic: keywords
subject: iontophoresis
  topic: keywords
subject: spine–leaf resistor–capacitor
  topic: keywords
subject: Ebola Optimisation Algorithm
  topic: keywords
subject: Poisson–Boltzmann– Nernst–Planck
  topic: keywords
creator: Maceal Tony L
creator: Shaji R.S
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-12-23
publication date: 2025-12-23
starting page: 347
ending page: 357
doi: 10.2478/msr-2025-0038

GGLCM: A Real-Time Early Anomaly Detection Method for Mechanical Vibration Data with Missing Labels
type: item
description: Early anomaly detection plays a central role in the scientific maintenance of mechanical equipment. Although the application is limited by weak monitoring, it encounters the problem of missing labels. To overcome this challenge, the Gramian gray level co-occurrence matrix (GGLCM) analysis method is proposed, which includes three phases: first, the time-series are input into the Gramian angular field (GAF) in real time for signal dimension reconstruction. Second, the gray level co-occurrence matrix (GLCM) is applied to the reconstructed signal. Since the GAF preserves the dependencies in the time-series, the limitation of missing labels is significantly weakened. Third, a continuous alarm mechanism is developed for reliable detection. Finally, the GGLCM is verified by actual vibration datasets of overloaded bearings.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: early anomaly detection
  topic: keywords
subject: feature reconstruction
  topic: keywords
subject: missing labels
  topic: keywords
subject: reliable detection
  topic: keywords
subject: real-time detection
  topic: keywords
creator: Hu Yu
creator: Xiaoyu Che
creator: Chao Zhang
creator: Rupeng Zhu
creator: Weifang Chen
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2024-07-22
publication date: 2024-07-22
starting page: 157
ending page: 163
doi: 10.2478/msr-2025-0019

Feed Rate-Adaptive Predictive PH Interpolation for Real-Time NURBS Toolpath Execution in High-Speed CNC Machining
type: item
description: This study presents a novel feed rate-adaptive predictive interpolation algorithm that combines Pythagorean-hodograph (PH) curves with non-uniform rational b-splines (NURBS) to improve real-time toolpath generation in high-speed CNC machining. Unlike traditional NURBS interpolators, which are prone to feed rate fluctuations and poor curvature handling, the proposed method incorporates a curvature-aware predictive module that dynamically adjusts the feed rate based on local geometry and system dynamics. Key innovations include sharp corner detection, PH-based smoothing, and jerk-limited feed rate profiling. Simulation and experimental results show that the proposed algorithm significantly reduces path-following errors (on average by more than 60 % RMS error reduction) while ensuring smoother, bounded-acceleration trajectories. The algorithm consistently maintains geometric accuracy across different toolpath orientations, making it a robust solution for precision manufacturing tasks that require both real-time responsiveness and high accuracy.
subject: Articles
  topic: section
subject: Theoretical Problems of Measurement
  topic: category
subject: Measurement of Physical Quantities
  topic: category
subject: Pythagorean-hodograph
  topic: keywords
subject: predictive interpolation
  topic: keywords
subject: feed rate adaptation
  topic: keywords
subject: non-uniform rational b-splines machining
  topic: keywords
subject: path-following error
  topic: keywords
subject: CNC trajectory planning
  topic: keywords
creator: Guosheng Tian
rights: Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2025-10-23
publication date: 2025-10-23
starting page: 284
ending page: 299
doi: 10.2478/msr-2025-0031