rss_2.0Measurement Science Review FeedSciendo RSS Feed for Measurement Science Review Science Review Feed Quality of Service (QoS) in Wireless Multimedia Sensor Networks using Epsilon Greedy Strategy<abstract> <title style='display:none'>Abstract</title> <p>Wireless Multimedia Sensor Networks (WMSNs) are networks consisting of sensors that have limitations in terms of memory, computational power, bandwidth and battery life. Multimedia transmission using Wireless Sensor Network (WSN) is a difficult task because certain Quality of Service (QoS) guarantees are required. These guarantees include a large quantity of bandwidth, rigorous latency requirements, improved packet delivery and lower loss ratio. The main area of research would be to investigate the process of greedy techniques that could be modified to guarantee QoS provisioning for multimedia traffic in WSNs. This could include optimization of routing decisions, dynamic allocation of resources and effective congestion management. This study introduces a framework called Epsilon Greedy Strategy based Routing Protocol (EGS-RP) for multimedia content transmission over WSN. The framework focuses on energy efficiency and QoS by using reinforcement learning to optimize rewards. These incentives are determined by a number of variables, including node residual energy, communication energy and the effectiveness of sensor type-dependent data collection. Experimental analysis was conducted to evaluate the effectiveness of the proposed routing strategy and compare it with the performance of standard energy-aware routing algorithms. The proposed EGS-RP achieves a throughput of 217 kbps, a bandwidth of 985 bps, a packet delivery ratio of 94.45% and an energy consumption of 32%.</p> </abstract>ARTICLEtrue and Development of Dual Band Millimeter Wave Substrate Integrated Waveguide Antenna Array<abstract> <title style='display:none'>Abstract</title> <p>New communication paradigms have emerged to make better use of the available wireless spectrum due to its scarcity. Millimeter wave high-frequency spectrum could offer a viable solution to the problem of spectrum scarcity. Millimeter wave devices and antennas are becoming increasingly popular and are used in a wide variety of applications and planned Fifth Generation (5G) wireless communication networks. In this work, we develop a Substrate Integrated Waveguide (SIW) based antenna array and millimeter-wave feeding network with the aim of achieving optimal performance. A microstrip array antenna is developed for use at millimeter wave frequencies of 28 GHz and 38 GHz. Next, an SIW array antenna will be created. For high-frequency uses, SIW technology excels due to its low loss, easy integration and high quality factor. The two unequal longitudinal slots in a slotted SIW antenna cause the structure to resonate at 28 GHz and 38 GHz. The SIW structure is fabricated by making two parallel rows of metallic vias, carefully determined through sizes to ensure minimal internal losses. A microstrip line that transitions into a SIW feeds into the proposed layout. In this paper, the authors investigate the design and construction of an integrated waveguide antenna array for use at dual millimeter-wave frequencies.</p> </abstract>ARTICLEtrue Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks<abstract> <title style='display:none'>Abstract</title> <p>Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit information over the underwater environment. Localization plays a vital role in the effective deployment, navigation and coordination of these nodes for many applications, namely underwater surveillance, underwater exploration, oceanographic data collection and environmental monitoring. Due to the unique characteristics of underwater transmission and acquisition, this is a fundamental challenge in underwater networks. However, localization in UWSNs is problematic due to the unique features of underwater transmission and the harsh underwater environment. To address these challenges, this paper presents an Improved Grey Wolf Optimization Based Node Localization Approach in UWSN (IGWONL-UWSN) technique. The presented IGWONL-UWSN technique is inspired by the hunting behavior of grey wolves with the Dimension Learning-based Hunting (DLH) search process. The proposed IGWONL-UWSN technique uses the Improved Grey Wolf Optimization Based (IGWO) algorithm to calculate the optimal location of the nodes in the UWSN. Moreover, the IGWONL-UWSN technique incorporates the DLH search process to improve the convergence and accuracy. The simulation results of the IGWONL-UWSN technique are validated using a set of performance measures. The simulation results show the improvements of the IGWONL-UWSN method over other approaches with respect to various metrics.</p> </abstract>ARTICLEtrue on the Measurement System and Remote Calibration Technology of a Dual Linear Array Camera<abstract> <title style='display:none'>Abstract</title> <p>In order to accurately measure the flight trajectory of the projectile in a long-distance target range, it is important to establish a vertical target measurement system with a two-line array camera at several positions along the range. In the present work, a calibration system using dual theodolites is proposed to calibrate the vertical target measurement system of a dual linear array camera at different positions along a long target range. The present study investigates the principle of intersection measurement in the vertical target measurement system with a double linear array camera and presents the projectile’s target coordinate measurement formula. The calibration method of the measurement system is developed based on an analysis of the parameters in the projectile coordinate measurement formula. The calibration method only requires calibration of the camera’s distortion coefficient, the optical axis angle, and the principal point, ensuring a simplified and expedited calibration process. After calibration, the vertical target measurement system with a 3 m × 3 m measurement area is simulated and analyzed, yielding an error distribution diagram and identifying the factors that influence the measurement accuracy of the projectile’s target coordinates.</p> </abstract>ARTICLEtrue on Oil-Water Two-phase Flow in the Invisible Measuring Pipeline of the Horizontal Tri-electrode Capacitive Sensor<abstract> <title style='display:none'>Abstract</title> <p>Based on the well logging requirements of horizontal stripper wells, the flow characteristics of the oil-water two-phase flow in the invisible horizontal tri-electrode capacitive sensor (HTCS) measurement pipeline are studied. First, an experimental device and a numerical validation model of a horizontal 20 mm glass pipeline are established to study the flow characteristics of the oil-water two-phase flow. Then, the flow characteristics of the horizontal oil-water two-phase flow in the measurement pipeline under different horizontal inclination angles are studied and the flow patterns and inclination angles suitable for the new tri-electrode capacitive sensor are discussed. Finally, using the horizontal oil-water two-phase flow loop platform of the largest oil and gas testing center in China, the dynamic response of the new capacitive sensor is studied under different inclination angles, flow rates, and water-cut conditions, and the dynamic response law is analyzed based on the simulation results.</p> </abstract>ARTICLEtrue Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification<abstract> <title style='display:none'>Abstract</title> <p>The brain’s Electroencephalogram (EEG) signals contain essential information about the brain and are widely used to support the analysis of epilepsy. By analyzing brain behavioral patterns, an accurate classification of different epileptic states can be made. The behavioral pattern analysis using EEG signals has become increasingly important in recent years. EEG signals are boisterous and non-linear, and it is a demanding mission to design accurate methods for classifying different epileptic states. In this work, a method called Quadrature Response Spectra-based Gaussian Kullback Deep Neural (QRS-GKDN) Behavioral Pattern Analytics for epileptic seizures is introduced. QRS-GKDN is divided into three processes. First, the EEG signals are preprocessed using the Quadrature Mirror Filter (QMF) and the Power Frequency Spectral (PFS) and Response Spectra (RS)-based Feature Extraction is applied for Behavioral Pattern Analytics. The QMF function is applied to the preprocessed EEG input signals. Then, relevant features for behavioral pattern analysis are extracted from the processed EEG signals using the PFS and RS function. Finally, Gaussian Kullback–Leibler Deep Neural Classification (GKDN) is implemented for epileptic seizure identification. Furthermore, the proposed method is analyzed and compared with dissimilar samples. The results of the Proposed method have superior prediction in a computationally efficient manner for identifying epileptic seizure based on the analyzed behavioral patterns with less error and validation time.</p> </abstract>ARTICLEtrue Performance Analysis of Metaheuristic Feature Selection Methods for Speech Emotion Recognition<abstract> <title style='display:none'>Abstract</title> <p>Emotion recognition systems from speech signals are realized with the help of acoustic or spectral features. Acoustic analysis is the extraction of digital features from speech files using digital signal processing methods. Another method is the analysis of time-frequency images of speech using image processing. The size of the features obtained by acoustic analysis is in the thousands. Therefore, classification complexity increases and causes variation in classification accuracy. In feature selection, features unrelated to emotions are extracted from the feature space and are expected to contribute to the classifier performance. Traditional feature selection methods are mostly based on statistical analysis. Another feature selection method is the use of metaheuristic algorithms to detect and remove irrelevant features from the feature set. In this study, we compare the performance of metaheuristic feature selection algorithms for speech emotion recognition. For this purpose, a comparative analysis was performed on four different datasets, eight metaheuristics and three different classifiers. The results of the analysis show that the classification accuracy increases when the feature size is reduced. For all datasets, the highest accuracy was achieved with the support vector machine. The highest accuracy for the EMO-DB, EMOVA, eNTERFACE’05 and SAVEE datasets is 88.1%, 73.8%, 73.3% and 75.7%, respectively.</p> </abstract>ARTICLEtrue an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systems<abstract> <title style='display:none'>Abstract</title> <p>In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.</p> </abstract>ARTICLEtrue Cloud-Connected Digital System for Type-1 Diabetes Prediction using Time Series LSTM Model<abstract> <title style='display:none'>Abstract</title> <p>Millions of people worldwide suffer from diabetes, a medical condition that is spreading at an accelerating pace. Numerous studies show that risk factors that may arise from diabetes can be avoided if the disease is detected early. The health-care monitoring system has benefited greatly from early diabetes prediction made possible by the integration of Deep Learning (DL) and Machine Learning (ML) algorithms. The objective of many early studies was to increase the prediction model accuracy. However, DL algorithms often cannot fully exploit the potential of the available datasets because they are too small. This study includes a very accurate DL model as well as a novel system that integrates cloud services and allows users to directly enhance an existing data set, which can increase the accuracy of DL techniques. Therefore, the Long Short-Term Memory (LSTM) model with controller is chosen for efficient type-1 diabetes prediction. Experimental validation of the proposed Nonlinear Model Predictive Control (NMPC)_LSTM algorithm method is compared with other conventional DL algorithms. The proposed controller method achieves excellent blood glucose set point tracking and the proposed algorithms achieves 98.95% accuracy for the obtained data. It outperforms other existing methods with an increase in percentage accuracy compared to the Benchmark Pima Indian Diabetes Datasets (PIDD).</p> </abstract>ARTICLEtrue of a 2 kN·m Torque Transducer Tested at GUM and PTB, Including Creep Behaviour<abstract> <title style='display:none'>Abstract</title> <p>This article presents a study carried out on a 2 kN·m torque transducer at the Central Office of Measures (GUM) and the Physikalisch-Technische Bundesanstalt (PTB). The weighted least squares method was used to generate the linear regression equations for this torque transducer. The Monte Carlo method and the law of uncertainty propagation were used to calculate the expanded uncertainty. In addition, a creep study was carried out at eight measurement points ranging from 200 N·m to 2000 N·m. The investigations showed that the highest readings of the torque transducer, expressed in electrical units as mV/V, occur within the initial few seconds of the test after the removal of the maximum reference torque.</p> </abstract>ARTICLEtrue of Component Assembly in Automotive Industry<abstract> <title style='display:none'>Abstract</title> <p>This article is devoted to the positioning of glued parts by robots in the process of manufacturing automotive headlights, with the possibility of generalization to the mutual positioning of any 3D object. The authors focused on the description of the mathematical method that leads to the optimization of the robot arm setting and ensures the closest contact of the glued parts. The contact surfaces of the two joined parts are, in the ideal case, identical in shape and their optimal alignment is considered to best align the position of the nominal points on the base part with the position of the control (measured) points on the part manipulated by the robot.</p> </abstract>ARTICLEtrue High-Performance Method Based on Features Fusion of EEG Brain Signal and MRI-Imaging Data for Epilepsy Classification<abstract> <title style='display:none'>Abstract</title> <p>A 1-dimensional (1D) and 2-dimensional (2D) biomedical signal analysis based on the Discrete Cosine Transform (DCT) feature extraction method was performed to diagnose epilepsy disorders with high accuracy. For this purpose, Electroencephalogram (EEG) data were used for 1D signal analysis and Magnetic Resonance Imaging (MRI) data were used for 2D signal analysis. The feature vectors were obtained by applying 1D DCT together with statistical methods such as mean, variance, standard deviation, kurtosis, and skewness for EEG data and by applying 2D DCT together with the statistical method of mean for MRI data. The most useful features were selected by applying Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward Selection and Backward Selection methods to the obtained feature vectors. Using EEG stand-alone features, MRI stand-alone features and EEG-MRI fused features, the classification of healthy and epileptic subjects was performed in the form of two clusters. The result of epilepsy classification in this work is 96% success of 1D EEG data by using the features selected by the PCA method, 94% success of 2D MRI data using the selected features by applying the Forward Method, 100% classification accuracy of 1D EEG and 2D MRI datasets by LDA method using the obtained fused features . The article shows that the fused features of EEG-MRI can be used very effectively for the diagnosis of epilepsy.</p> </abstract>ARTICLEtrue of Calibration System for Multi-Channel Thermostatic Metal Bath<abstract> <title style='display:none'>Abstract</title> <p>The use of the thermostatic metal bath is becoming more and more extensive and the requirements for its precision and reliability are also increasing. To meet the needs of the metal bath calibration, a 12-channel thermostatic metal bath temperature field calibration system based on a four-wire PT100 has been designed. The system design includes a front-end temperature measurement component, which consists of a four-wire PT100 and a thermostatic block, and a signal processing component, which consists of a bidirectional constant current source excitation unit, a signal conditioning unit and a high-precision acquisition unit. The STM32f407 is used as the main control chip, and the analog channel selector is used for 12-channel selection. The constant current source is used for signal excitation, the proportional method is used to measure the PT100 resistance, and an acquisition circuit with a high-precision 32-bit ADS1263 analog-to-digital converter is used to amplify, filter and convert the analog signal. After piecewise linear fitting and system calibration, the temperature measurement accuracy can reach 0.4 mK, which meets the calibration requirements of the thermostatic metal bath.</p> </abstract>ARTICLEtrue Approach to Evaluation of Ultra-Low-Voltage Amplifier ASICs<abstract> <title style='display:none'>Abstract</title> <p>This article presents measurement circuits and a test board developed for the experimental evaluation of prototype chip samples of the Fully Differential Difference Amplifier (FDDA). The Device Under Test (DUT) is an ultra low-voltage, high performance integrated FDDA designed and fabricated in 130nm CMOS technology. The power supply voltage of the FDDA is 400mV. The measurement circuits were implemented on the test board with the fabricated FDDA chip to evaluate its main parameters and properties. In this work, we focus on evaluation of the following parameters: the input offset voltage, the common-mode rejection ratio, and the power supply rejection ratio. The test board was developed and verified. The test board error was measured to be 38.73mV. The offset voltage of the FDDA was −0.66mV. The measured FDDA gain and gain bandwidth were 48dB and 550kHz, respectively. In addition to the measurement board, a graphical user interface was also developed to simplify the control of the device under test during measurements.</p> </abstract>ARTICLEtrue of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification<abstract> <title style='display:none'>Abstract</title> <p>A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.</p> </abstract>ARTICLEtrue of Low-Cost GNSS Receivers for Time Transfer using Zero-Length Baseline<abstract> <title style='display:none'>Abstract</title> <p>A comparison between low-cost single-frequency and dual-frequency Global Navigation Satellite System (GNSS) receiver timing modules is presented, focusing on their suitability for time transfer applications. The study uses a zero-length baseline measurement approach to assess their performance and highlights the advantages of dual-frequency receivers. The clock comparison residuals between these low-cost devices and a reference receiver are analyzed. In particular, it is shown that the use of averages longer than 200s can effectively mitigate the quantization error inherent in pulse per second outputs of the timing modules. The results showcase sub-nanosecond time deviation instabilities between the reference receiver and the dual-frequency timing module. In contrast, the single-frequency module exhibits time deviations of 3.3ns at a one-day averaging interval. This research provides insights into the selection and utilization of GNSS timing modules for time transfer applications, where such modules can serve as attractive, cost-effective alternatives for applications requiring moderate accuracy.</p> </abstract>ARTICLEtrue Approach to Recognise Lung Diseases Using Segmentation and Classification<abstract> <title style='display:none'>Abstract</title> <p>Lung cancer is one of the most common causes of death in people worldwide. One of the key procedures for early detection of cancer is segmentation or analysis and classification or assessment of lung images. Radiotherapists have to invest a lot of effort into the manual segmentation of medical images. To solve this issue, early-stage lung cancer is detected using Computed Tomography (CT) scan images. The proposed system for diagnosing lung cancer is divided into two main components: the first part is an analyser component built on the upper layer of the U-shaped Network Transformer (UNT), and the second component is an assessment component built on the upper layer of the self-supervised network, which is used to categorise the output segmentation component as benign or cancerous. The proposed method provides a powerful tool for the early detection and treatment of lung cancer by combining CT scan data with 2D input. Numerous experiments are conducted to improve the analysis and evaluation of the findings. Using the public dataset, both test and training experiments were conducted. New state-of-the-art performances were achieved with experimental results: an analyser accuracy of 96.9% and an assessment accuracy of 96.98%. The proposed approach provides a new powerful tool for leveraging 2D-input CT scan data for early detection and treatment of lung cancer using a variety of methods.</p> </abstract>ARTICLEtrue Microstrip Feed Hybrid Rectangular Dielectric Resonator Antenna for Wireless Tri-Band Applications<abstract> <title style='display:none'>Abstract</title> <p>This article reports the Modified Microstrip Feed Hybrid Rectangular Dielectric Resonator Antenna (RDRA). The proposed structure has a ground plane with a plus-shaped slot on an FR4 substrate with a height of 1.6 mm and dimensions of 38 mm x 35 mm. The proposed Dielectric resonator antenna is made of a material with 10 as its dielectric constant, and the dimensions of the DR are 19 x 20 x 18 mm<sup>3</sup>. The DR is connected to a modified microstrip feed with an octagonal ring through the plus-shaped slot in the ground. The proposed structure operates in frequencies from 2.60 GHz - 2.74 GHz, 3.12 GHz - 3.37 GHz, and 4.25 GHz - 4.37 GHz. The resonant frequency of the final proposed RDRA is 2.68 GHz, 3.26 GHz, and 4.31 GHz, which covers WLAN, WIMAX and Wireless Avionics Intra-Communications (WAIC) applications, respectively. The entire structure was simulated using the CST microwave studio. The simulated results agree with the measured results and both are presented. The compact size, stable radiation pattern and reasonable gain make this antenna suitable for the proposed applications.</p> </abstract>ARTICLEtrue Experimental Setup for Power Loss Measurement up to 1 kHz using an Epstein Frame at CMI<abstract> <title style='display:none'>Abstract</title> <p>This paper describes an experimental setup used at the Czech Metrology Institute (CMI) to measure the specific power loss of oriented and non-oriented electrical steel sheets up to 1 kHz using an Epstein frame. Special attention is given to a) a description of the hardware that is used, b) a description of the feedback control and measurement software, and c) an analysis of the sources of uncertainty and validation. Calibration expanded uncertainty of (0.5 up to 1.6)% for k = 2 can be achieved with this setup.</p> </abstract>ARTICLEtrue Measurement Method of Oil-Water Two-Phase Flow with High Water Holdup and Low Rate by Phase State Regulation<abstract> <title style='display:none'>Abstract</title> <p>Flow rate and holdup are two essential parameters to describe oil-water two-phase flow. The distribution of oil-water two-phase flow in the pipeline is very uneven, and there is a significant slippage between the phases. This makes it difficult to measure these two flow parameters. In this paper, a new measurement method of flow rate and holdup based on phase state regulation is proposed. The oil-water two-phase flow is adjusted to oil or water single-phase flow according to the time sequence by the phase state regulation, and the oil-water phase interface is measured with a conductance sensor. A wavelet transform based phase inflection point detection model is proposed to detect the oil-water phase change point. The experimental results show that the maximum measurement error of the flow rate of water is 3.73%, the maximum measurement error of the flow rate of oil is 3.68%, and the flow rate measurement repeatability is 0.0002. The accuracy of the measurement holdup is better than 3.23%, and the repeatability of the measurement holdup is 0.0003. The prototype designed based on this method has two advantages. One is that it is small in size, the other is that it does not depend on the accuracy of the sensor. Therefore, it can be widely used in oilfield ground measurement.</p> </abstract>ARTICLEtrue