No. 1

       Measurement of Physical Quantities

        No. 1

      Pages: 

Bo Tang, Jiangen Yang, Wei Chen, Xu Ming:

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Abstract: 

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.

Hua Zhuo, Yan Xu, Weihu Zhou, Feng Li, Yikun Zhao:

Design of Calibration System for Multi-Channel Thermostatic Metal Bath

Abstract:

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.

Jaromír Křepelka, Petr Schovánek, Pavel Tuček, Miroslav Hrabovský, František Jáňe:

Optimization of Component Assembly in Automotive Industry

Abstract:

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.

42–46

Marek Doršic:

Comparison of Low-Cost GNSS Receivers for Time Transfer using Zero-Length Baseline

Abstract:

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.

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Venkatramanan M,. Chinnadurai M.:

Modeling an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systems

Abstract:

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 EMCA-AOADL 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.

Lei Li, Ming Wang, Dahai Wang, Xuewei Gao, Qianhui Zhu:

Study on Oil-Water Two-phase Flow in the Invisible Measuring Pipeline of the Horizontal Tri-electrode Capacitive Sensor

Abstract:

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.

Vishalakshi R., Mangai S., Sharmila C., Kamalraj S.:

Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification

Abstract:

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.

Turgut Ozseven, Mustafa Arpacioglu::

Abstract:

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.

 K. Priyadarshini, Alanoud Al Mazroa, Mohammad Alamgeer, V. Subashree:

A Cloud-Connected Digital System for Type-1 Diabetes Prediction using Time Series LSTM Model

Abstract:

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 dataset, 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).

Janusz D. Fidelus, Anna Trych-Wildner, Jacek Puchalski, Paula Weidinger:

Study of a 2 kN·m Torque Transducer Tested at GUM and PTB, Including Creep Behaviour

Abstract:

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.

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Salem Jeyaseelan WR., Vinoth Kumar K., Jayasankar T.,  Ponni R.:

Improved Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks

Abstract:

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

 Santhakumar G., Muthukumar R.:

Design and Development of Dual Band Millimeter Wave Substrate Integrated Waveguide Antenna Array

Abstract:

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.

Bin Feng, Zaiming Liu, Haofei Zhang, Haozhe Fan:

Research on the Measurement System and Remote Calibration Technology of a Dual Linear Array Camera

Abstract:

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.

Senthil Kumar S., Nada Alzaben, Sridevi A., Ranjith V.:

Improving Quality of Service (QoS) in Wireless Multimedia Sensor Networks using Epsilon Greedy Strategy

Abstract:

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%.

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 Ajitha Gladis K. P., Ahilan A., Muthukumaran N., Jenifer L.:

ECG Arrhythmia Measurement and Classification for Portable Monitoring

Abstract:

Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

 Wenfei Tao, Chen Chen, Kejia Zhang:

Adaptive Proportional Derivative Control for Magnetic Bearing in Full Maglev Left Ventricular Assist Device

Abstract:

In this paper, adaptive proportional derivative (APD) parameter control was proposed to solve the problem of high power consumption caused by the unclear mechanism of liquid disturbance during the lifting-up of magnetic bearing in left ventricular assist devices. A mathematical model was derived that describes how the rotor operates in liquid filling. The disturbance caused by the liquid in the lifting-up process was analyzed, and an adaptive control system was developed to improve dynamic performance and reduce power consumption. The experimental results show that APD control requires a shorter rise time without overshoot of rotor displacement compared to traditional fixed configurations. When using the APD controller, the peak current dropped by 8%. The duration in which the current is greater than 1A was reduced by 10.2 ms, and the average current also dropped by 34%.

Faiza Boukazouha, Hamza Barkat, Abdesselam Rouabha, Abderahim Herbadji, Mohamed Rguiti:

Influence of the Electrical Test Setup on the Voltage Gain Measurement of an Unloaded Rosen-Type Piezoelectric Transformer Vibrating in the First Three Modes

Abstract:

In recent years, Piezoelectric Transformers (PTs) have become a great success due to their excellent properties, especially in applications requiring high voltage. The Rosen-type PT is well known for this performance, as its voltage gain at the resonant frequency can reach few thousands. However, the high output impedance of this device can make an accurate electrical measurement of the output voltage difficult, hence the need to ensure good impedance matching along the measuring electrical test setup. For this purpose, two high impedance oscilloscope probes were successively added to the secondary side to further emulate the measurement chain and match the experiments as closely as possible with the developed 1D model. Accordingly, for an unloaded Rosen type piezoelectric transformer, made of hard ceramic (pz26) with corresponding dimensions 2L×w×t =25 mm×3 mm×2 mm and operating in the first three modes, the corresponding input impedances Zin were evaluated at 665 Ω - 225 Ω and 1974 Ω, while the output impedances Zout were evaluated at 19.2 MΩ - 15.4 MΩ, and 1.8 MΩ. A voltage gain of 164, 179 and 23 at frequencies of 69.4 kHz, 136 kHz and 204.6 kHz, respectively was successfully measured, with a precision of less than 5%. In addition, a detailed equivalent circuit of the transformer was built and all its lumped RLC components were experimentally identified using the Nyquist diagram showing, on the whole, a well-accepted agreement with the expected results..

 Jansirani G, Gandhi Raj R.:

Metamaterial Inspired Stub-Incorporated Quad-Band Diamond Shaped Monopole Antenna for Satellite and Wireless Application

Abstract:

This study presents a quad-band stub-incorporated split octagonal ring antenna specifically designed for wireless applications that rely on satellite communication. The antenna is fabricated on an FR4 substrate with dimensions of 26×21×1.6 mm³ and its performance is simulated using the CST EM Studio software. The device operates in the frequency range from 2.15 GHz to 6.35 GHz, using stub integration and gap modification to achieve resonant bands. The antenna has resonant frequencies of 2.23 GHz, 3.28 GHz, 4.77 GHz, and 5.89 GHz, with corresponding bandwidths of 153 MHz, 9011 MHz, 7692 MHz, and 6813 MHz, respectively. The parametric analysis optimizes the values of the design parameters, while the experimental validation shows the consistency between the measured and simulated results. The antenna is characterized by a small size, a consistent radiation pattern and a wide range of applications including ISM, WIFI, WLAN, WIMAX, 5G, and C-Band Satellite. The device is capable of operating in two frequency bands and consistently maintains a gain of over 1 dBi across its resonating range.

 Chen Zhe, Fan Baixing:

Accurate Solution of Adjustment Models of 3D Control Network

Abstract:

The spatial Three-Dimensional (3D) edge network is one of the typical rank-lossless networks. The current network adjustment usually uses Least Squares (LS) algorithm, which has the complexity of linearization derivation, computational volume and other problems. It is based on high-precision ranging values. This study aims to minimize the sum of the difference between the inverse distance of the control point coordinates and the observation distance, the composition of the non-linear system of equations to build a functional model. Considering the advantages of the intelligent optimization algorithm in the non-linear equation system solving method, such as no demand derivation and simple formula derivation, the Particle Swarm Optimization (PSO) algorithm is introduced and the improved PSO algorithm is constructed; at the same time, the improved Gauss-Newton (G-N) algorithm is studied for the calculation of the 3D control network adjustment function model to solve the problems of computational volume and poor convergence performance of the algorithm with large residuals of the unknown parameters. The results show that the improved PSO algorithm and the improved G-N algorithm can guarantee the accuracy of the solution results. Compared with the traditional PSO algorithm, the improved PSO algorithm has a faster optimization speed. When the residuals of the unknown parameters are too large, the improved G-N algorithm is more stable than the improved PSO algorithm, which not only provides a new way to solve the spatial 3D network, but also provides theoretical support for the establishment of the spatial 3D network.

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 Sebahattin Babur, Sanam Moghaddamnia, Mehmet Recep Bozkurt:

Estimation of Blood Calcium and Potassium Values from ECG Records

Abstract:

The identification of diseases caused by changes in ion concentration is quite difficult and yet plays a decisive role in the success of clinical care, diagnosis and treatment. The clinically proven approach to diagnosing electrolyte concentration imbalance is blood tests. There is a need to provide a non-invasive diagnostic method that is not of a temporary nature. Bio-signals such as the electrocardiogram (ECG) can be used to meet this demand and become diagnostic tools that facilitate home monitoring of electrolyte concentration on a permanent basis. This study investigates the feasibility and efficiency of methods based on machine learning (ML) and ECG recordings in monitoring critical levels of existing potassium and calcium concentration. Morphological, frequency and frequency-time domain features were extracted to automatically estimate calcium and potassium levels. Furthermore, the potential of estimates based on modeling approaches will be demonstrated to gain insights into relevant clinical findings and improve the performance of monitoring approaches. Using the hold-out validation method, the best results in terms of mean square error (MSE) and R for estimating the calcium value are 0.7157 and 0.57347, using fuzzy inference systems (FIS). Here, R represents the proportion of the variance in the calcium value that is explained by the model.

Shengdan Zhang, Yonglin Bai, Weiwei Cao, Junjun Qin, Jiarui Gao, Le Chang, Beihong Liu, Zexun Hu, Zhujun Chu, Xiaoqing Cong, Yongwei Dong, Zhigang Wang:

Equivalent Mechanical Model of a Microchannel Plate

Abstract:

The microchannel plate (MCP) is an electron multiplier with millions of micro through-holes that must withstand strong vibrations and enormous shocks in spaceborne detectors. To ensure the reliability and robustness of the MCP in space applications, we proposed an equivalent mechanical model of the MCP to investigate its mechanical properties, since the direct creation of the finite element model using the finite element method (FEM) is not feasible. Then, we developed a test system to verify the effectiveness of the equivalent mechanical model. The results show that the error of harmonic response analysis and the test result is less than 10 %, which is acceptable. Finally, we conducted parametric studies of the MCPs and obtained the equivalent mechanical model of the MCPs with different geometric parameters. This study will help researchers to optimize the MCP for aerospace-grade detectors.

 Jayarani R., Narmadhai N.:

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract:

Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

Bharathi V., Natraj N. A., Gopinath S., Kiruthikaa R.:

Advanced Computer Vision Techniques for Accurate Measurement in Unmanned Mobile Robots

Abstract:

For years, researchers have been studying computer vision, i.e. the ability of artificial intelligence (AI) systems to perceive and interpret visual data like humans. This study is gaining increasing attention as researchers aim to develop tools that automate visual tasks and replicate human visual awareness. However, the interpretation of images is very complex due to the vast amount of multi-resolution information they contain, making the development of AI technologies for visual recognition particularly challenging. This article provides an overview of digital image processing, highlighting the main concepts and introducing key algorithms. These methods are designed to capture, process, and interpret digital images and enable the extraction of important data from real-world environments. We conduct rigorous image processing tests and compare AI-driven recognition systems with human analysis. The results show that computer vision technology significantly outperforms human observation in terms of accuracy and consistency. These results highlight the potential of computer vision to revolutionize various industries by automating complex visual tasks and offer promising future applications in areas such as healthcare, security, and manufacturing. The paper provides valuable insights into current advances in digital image processing and the role of AI in improving visual recognition capabilities, paving the way for further innovation in this area.

Nevena Vukadinović, Marko Šmrkić, Života Stefanović, Vladimir Ilić, David Nikolić, Igor Zlatović, Milivoj Dopsaj:

Metric Stability of One Month Handgrip Maximal and Explosive Isometric Strength Measured by Classic and Impulse Contractions

Abstract:

The aim of this study is to determine the metric stability of the one-month handgrip test (HGT) in order to define the contractile characteristics of the biological variation of maximal isometric strength (F_max) and maximal isometric rate of force development (RFD_max) of the handgrip in two different testing regimes (classic and impulse). The study was conducted with a total of 16 participants (11 men and 5 women). Testing was performed using an isometric handgrip probe with a standardized test protocol and equipment sports medical solutions (SMS). The results of F_max showed a low relative standard error of the mean (RS = 1.33 %), a high value of inter-class correlation (ICC = 0.996), and no statistically significant change in trend (p > 0.05) during the testing period. Therefore, can conclude that the HGT procedure in classic mode can be used as a stable parameter in a human subject sample. However, the RFD_max results showed a low RS (2.13 %) and a high ICC value (0.996), but a statistically significant change of trend (p < 0.05) during the measurement period. The regression constant (RCO) trend was 42.629 N/s, which can be attributed to learning or to the adaptive effects of the test procedure, which triggered similar adaptation processes as the training. In general, it can be concluded that the handgrip can be used to sensitively measure the effects of different long-term health improvements, or the effects of different medical/health exercises, rehabilitation programs, effects of medication applications, or dietary supplements for F_max. However, further research should be conducted for the RFD_max considering the metric stability parameters.

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200-210

Serge Ayme Kouakouo Nomvussi, Jan Mikulka:

Enhanced Image Reconstruction in Electrical Impedance Tomography using Radial Basis Function Neural Networks

Abstract:

This paper presents a novel cascade algorithm for image reconstruction in electrical impedance tomography (EIT) using radial basis function neural networks. The first subnetwork applies a density-based algorithm and k-nearest neighbors (KNN) to determine the center and width of the radial basis function neural networks, with the aim of preventing ill-conditioned connection weights between the hidden and output layers. The second subnetwork is a generalized regression neural network dedicated to functional approximation. The combined subnetworks result in a reduced mean square error and achieve an accuracy of 89.54 % without noise and an accuracy between 82.90 % and 89.53 % with noise levels ranging from 30 to 60 dB. In comparison, the original radial basis function neural networks (RBFNN) method achieves an accuracy of 85.44 % without noise and between 80.90 % and 85.31 % under similar noise conditions. The total variation (TV) method achieves 83.13 % without noise, with noise-influenced accuracy ranging from 34.28 % to 45.15 %. The Gauss-Newton method achieves 82.35 % accuracy without noise, with accuracy ranging from 33.21 % to 46.15 % in the presence of noise. The proposed method proves to be resilient to various types of noise, including white Gaussian noise, impulsive noise, and contact noise, and consistently delivers superior performance. It also outperforms the other methods in noise-free conditions. The reliability of the method in noisy environments supports its potential application in the development of new modular systems for electrical impedance tomography.

211-214

Peter Andris, Ivan Frollo:

Calculation of the Main Frequency of an NMR Signal from an Even Frequency Spectrum

Abstract:

Nuclear magnetic resonance (NMR) measurements are most often used to measure images or display the spectrum of different samples. Depending on the samples, these can be medical applications, chemistry, physics, or mineralogy. Perhaps the most beneficial use is in medicine, as it allows you to image the inside of living organs without interfering with them. When physically examining samples, the frequency spectrum of the sample is often measured, which is then converted into a map of the inhomogeneities in the layer of interest. This article addresses one of several similar problems.

215-225

Roman Pernica, Miloš Klima, Pavel Fiala:

Measurement and Evaluation of Insulating Properties of a Modified Dielectric Surface using Plasma Discharge

Abstract:

Plasma discharges under atmospheric pressure can be used to modify the electrical properties of metallic and dielectric surfaces. The aim of such a modification is to achieve an improvement in the characteristic parameters of the surface, for example in the area of the electrical strength of the surface, in order to achieve a higher ultimate level of electrical breakdown Eb when tested with pulsed or alternating electrical voltages. So far, research has focused on a set of functional experiments carried out using plasma on samples of two types of dielectric materials (thermoset, thermoplastic) with an impact on the final effect of the level of electrical breakdown voltage, electrical intensity and Eb. surface conductivity. The treatment technology requires repeatability and consideration of the industrial deployment conditions of plasma technology. The surface structure was modified in a defined and repeatable way by plasma discharge under atmospheric pressure without the presence of precursors. Methods to evaluate these modifications assessed the change in parameters related to sample type, repeatability and prediction of treatment stability. Subsequently, the surface strength of both the modified samples and the samples not affected by the plasma discharge was measured.

Teodor Toth, Miroslav Dovica, Jan Buša, Jan Buša, Jr.:

Verification of Coordinate Measuring Machine Using a Gauge Block

234-238

Senthilkumar C., Eatedal Alabdulkreem, Nuha Alruwais3, Suresh K.:

Multimodal Brain Tumor Classification using Capsule Convolution Neural Network with Differential Evolution Optimization Process

239-243

Janarthanan S., Anto Bennet M.:

Advanced Miniaturized Microstrip Patch Antenna Design for High-Efficiency 5G Applications

244-254

Xianfeng Xin, Rongye Chen, Cong Lin, Qingchan Liu, Zhaolei He, Tengbin Li, Guangrun Yang, Orest Kochan, Mykhailo Karpa:

Research of Online Error Prediction and Parallel Algorithm for Voltage Transformer in Smart Grid

255-259

Sumithra S., Sandeep Prabhu, Prabhakaran M., Thandaiah Prabu R.:

Energy Efficient Multimedia Transmission in Wireless Sensor Networks using Enhanced Adaptive Transmission Control Algorithm and Measurement Techniques

Bharathi V., Giri G Hallur, Ramarajan S., Vinoth Kumar K.:

Enhancing Cooperative Spectrum Sensing Efficiency in CBRS-Based CRN for Unmanned Mobile Robot Applications

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