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
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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.
Copyright (c) 2026 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
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.
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/
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.
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.
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.
Evaluating the Detection Performance of a Drug Screening Device
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.
A Nested DUAL-axis Accelerometer with EnhancedTemperature Robustness for Testing Across the EntireOperational Temperature Range
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.
micro-electro-mechanical systems accelerometer
TRI-BCC: Tri-Level Breast Cancer Classification via Transfer Learning Networks with Histopathogical Images
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.
DEEP-BTS: Deep Learning based Brain Tissue Segmentation using ResU-Net Model
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.
contrast stretching adaptive trilateral filter
Analysis and Graphical Evaluation of Pressure Changes in Pneumatic Circuits for Industrial Applications
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.
pneumatic systems, pressure changes (faults), filtering of unwanted states, failure detection
Filter and Sampling Rate Optimization for PPG-Based Detection of Autonomic Dysfunction: An ECG-guided Approach
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.
Detection of Laryngeal Pathologies from Voice using EMD-based Mel-Spectrograms and Scalograms with AlexNet
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.
A Novel Scalable Trust-Aware Deep Reinforcement Learning Algorithm for Energy-Efficient and Secure Routing in Software-Defined Wireless Sensor Networks for IoT
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.
Adaptive Measurement Selection for Scalable Distributed Graph Optimization in Multi-UAV Relative Positioning
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.
Exploration of Infrared Thermography as an Alternate Tool for the Detection of Gastric Diseases
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.
Copyright (c) 2025 Measurement Science Review https://creativecommons.org/licenses/by-nc-nd/4.0
KazStandard Primary Dew/Frost Point Generator
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.
Skin Impedance Analysis for Drug Delivery: Integration of Poisson-Boltzmann-Nernst-Planck Model and Ebola Optimisation Algorithm
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.
GGLCM: A Real-Time Early Anomaly Detection Method for Mechanical Vibration Data with Missing Labels
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.
Feed Rate-Adaptive Predictive PH Interpolation for Real-Time NURBS Toolpath Execution in High-Speed CNC Machining
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.
non-uniform rational b-splines machining
Performance Estimation of Low Power and Area-Efficient Parallel Pipelined FFT
We present a novel parallel and pipelined fast Fourier transform (FFT) architecture for high-speed and low-power applications, a critical component in wireless communications and digital signal processors. The new FFT model implements a data-inverted Vedic multiplier in the FFT architecture, which reduces data switching activity in the input patterns to minimize dynamic power consumption and computational delay. The proposed architecture incorporates a low-power bit inversion (BI) multiplier scheme for a minimum number of complex multiplications with a high-speed partial product generation technique in FFT computation. This research focuses on the investigation and implementation of a modified butterfly unit as the best choice compared to other low-power and high-speed multipliers, such as Booth and Wallace multipliers for FFT processors. The BI multiplier design was synthesized in a field programmable gate array (FPGA), and the results show that the area efficiency could be improved by about 30 % and the power consumption and delay could be reduced by 56 %. The proposed FFT processor utilizes only 8 % of the available look-up tables (LUTs) with a 1:3 ratio in resource utilization and a 56 % reduction in delays compared to previous research. This makes this architecture best suited for high-speed wireless communications and 5G applications. This BI-Vedic multiplier is used in convolutions, FFT, and digital signal processing (DSP) filters where fast multiplication is critical. Throughput in applications with real-time signals is improved. It is also used in image and video processing and is critical for algorithms that manipulate pixels, scale, and compress data when many multiplications need to be performed quickly. IoT and embedded systems are beneficial for low-power systems as BI reduces power consumption and switching activity.
Adulteration Control of Lavender Essential Oil by using Its Electrical Properties in the Low-Frequency Range
With the development of technology, analysis methods have also increased and, as with many products, the properties of essential oils can be studied using methods based on electrical properties. In this study, the electrical properties of lavender essential oil mixed with various fixed oils were to be investigated to determine the adulteration rate more quickly and easily. Corn oil, olive oil, and sunflower oil were selected as fixed oils to obtain the oil samples with different adulteration rates. In this context, the real and imaginary parts of the permittivity, the loss tangents, the electrical conductivities, the amplitudes, and the phases of the impedances of the oil samples were measured. The measurements were also used to create an equivalent Debye circuit model for the lavender essential oil in order to analyze its electrical properties. The results showed that the lavender essential oil and the oil samples mixed at different rates showed different results in terms of electrical properties at different frequencies. According to the measurements, electrical conductivity was a critical parameter for a mixture of pure lavender essential oil and corn oil. The loss tangent was investigated for the mixture of pure lavender essential oil and olive oil, and the mixture of pure lavender essential oil and sunflower oil. It was observed that the electrical conductivity in the studied frequency band increased when the adulteration rate in the mixed corn oil samples increased. In addition, the loss tangent of the olive oil mixture and the sunflower oil mixture increased as the adulteration rate of said oil samples increased. By measuring the electrical properties, it may be possible to determine whether there is an adulteration and dilution process for particularly valuable essential oils, and if so, in what proportion.
System Maintainability Estimation with Multi-Peak Time Distribution based on the Bayesian Melding Method
In some test phases of equipment, the small sample size of test data and the absence of some maintenance operations may lead to a multi-peak phenomenon in data distribution, which is a challenge for Bayesian information fusion based on maintainability assessment. In this paper, prior information at two levels, the system level and the maintenance operation level, is integrated with the field test data via the Bayesian melding method (BMM). Mixture priors are used to avoid prior-data conflicts in the Bayesian framework, and a Bayesian posterior distribution is used to estimate system maintainability. Adaptive sampling importance resampling (ASIR) is used to overcome computational difficulties in simulation algorithms. Compared to the other methods, the proposed method provides more information sources for maintainability estimation, whose estimation effect is shown to be satisfactory based on two validation cases.
Efficiency Assessment of a High-Speed Tracked Vehicle Hybrid Powertrain
The paper analyzes the difference in power balance and efficiency between a modernized, hybrid high-speed tracked vehicle powertrain model and a mechanical powertrain model corresponding to the real vehicle developed and verified in previous research. This is to prove the argument of the efficiency benefits of electrifying the vehicle turning mechanism and eliminating the friction elements slip. The simulation models of both powertrains presented in this paper are subjected to the same simulation conditions, with the powertrain design solutions being the only variables. The results presented show that the vehicle with a hybrid powertrain achieves the required turning radius about four seconds earlier, with about 50 % less internal combustion engine (ICE) power required for the analyzed working regime. The hybrid powertrain offers an infinite number of calculated turning radii within the range of electric motor rpm, instead of one calculated turning radius in an existing powertrain. This results in a reduction in the total power required for the turning process as there are no losses due to friction element slip.
Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine
The rapid development of intelligent systems has had a significant impact on healthcare, forensics, and medicine, offering innovative solutions to critical problems. Breast cancer, which affects a large number of women each year, requires effective methods for early detection and accurate diagnosis to improve patient outcomes. This study introduces a hybrid feature selection method based on genetic algorithm (GA) and Bucket of Models (BoM) approach to improve breast cancer detection and classification. In the proposed method, GA is used to identify the most relevant features from the breast cancer diagnosis data, to improve the efficiency of the classification process. BoM is then used to select the optimal classification model from a set of candidates, to further improve the accuracy of diagnosis. The support vector machine (SVM) is used as the primary classifier due to its robustness in classifying medical data. The GA feature selection process includes encoding chromosomes, initializing the population, evaluating fitness, and iterating through reproduction steps, that systematically evaluate and select the most informative features for breast cancer diagnosis. In this study, a breast cancer detection accuracy of 97.16 % was achieved, which is a superior performance compared to existing state-of-the-art methods. This study contributes to the development of more accurate and efficient breast cancer screening tools to help healthcare providers make informed diagnostic decisions.
A Bearing Fault Diagnosis Model based on Minimum Average Composite Entropy and Parallel Attention Mechanism Convolutional Neural Network
This work addresses the issues of low diagnostic accuracy and weak generalization in rotating machinery bearing fault diagnosis, especially under complex noise conditions. In this paper, a novel bearing fault diagnosis method is proposed. This method, known as MACE + PFACNN, combines the minimum average composite entropy (MACE) with a parallel fusion attention convolutional neural network (PFACNN). In MACE, the minimum average composite entropy, which is composed of the Renyi entropy and the sample entropy, is used as a fitness function to guide the dung beetle optimization algorithm for fault feature extraction. Then, the extracted signal features are converted into angle and field and angular difference fields by Gramian angle field transformation. Finally, a PFACNN is used for fault diagnosis. Experimental data and bench tests show that the proposed model achieves a classification accuracy of 99.93 %. Compared with the baseline model, the noise resistance under complex noise conditions has improved by more than 15 %, and the generalization ability has increased by 3.68 %.