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.
{"title":"Comparison of Low-Cost GNSS Receivers for Time Transfer using Zero-Length Baseline","authors":"Marek Doršic","doi":"10.2478/msr-2024-0006","DOIUrl":"https://doi.org/10.2478/msr-2024-0006","url":null,"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.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"9 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"An Approach to Recognise Lung Diseases Using Segmentation and Classification","authors":"J Prabakaran, P Selvaraj","doi":"10.2478/msr-2023-0032","DOIUrl":"https://doi.org/10.2478/msr-2023-0032","url":null,"abstract":"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.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"70 7","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speech is one of the most serious manifestations of Parkinson's disease (PD). Sophisticated language/speech models have already demonstrated impressive performance on a variety of tasks, including classification. By analysing large amounts of data from a given setting, these models can identify patterns that would be difficult for clinicians to detect. We focus on evaluating the performance of a large self-supervised speech representation model, wav2vec, for PD classification. Based on the computed wav2vec embedding for each available speech signal, we calculated two sets of 512 derived features, wav2vec-sum and wav2vec-mean. Unlike traditional signal processing methods, this approach can learn a suitable representation of the signal directly from the data without requiring manual or hand-crafted feature extraction. Using an ensemble random forest classifier, we evaluated the embedding-based features on three different healthy vs. PD datasets (participants rhythmically repeat syllables /pa/, Italian dataset and English dataset). The obtained results showed that the wav2vec signal representation was accurate, with a minimum area under the receiver operating characteristic curve (AUROC) of 0.77 for the /pa/ task and the best AUROC of 0.98 for the Italian speech classification. The findings highlight the potential of the generalisability of the wav2vec features and the performance of these features in the cross-database scenarios.
{"title":"Evaluating the Performance of wav2vec Embedding for Parkinson's Disease Detection","authors":"Ondřej Klempíř, David Příhoda, Radim Krupička","doi":"10.2478/msr-2023-0033","DOIUrl":"https://doi.org/10.2478/msr-2023-0033","url":null,"abstract":"Speech is one of the most serious manifestations of Parkinson's disease (PD). Sophisticated language/speech models have already demonstrated impressive performance on a variety of tasks, including classification. By analysing large amounts of data from a given setting, these models can identify patterns that would be difficult for clinicians to detect. We focus on evaluating the performance of a large self-supervised speech representation model, wav2vec, for PD classification. Based on the computed wav2vec embedding for each available speech signal, we calculated two sets of 512 derived features, wav2vec-sum and wav2vec-mean. Unlike traditional signal processing methods, this approach can learn a suitable representation of the signal directly from the data without requiring manual or hand-crafted feature extraction. Using an ensemble random forest classifier, we evaluated the embedding-based features on three different healthy vs. PD datasets (participants rhythmically repeat syllables /pa/, Italian dataset and English dataset). The obtained results showed that the wav2vec signal representation was accurate, with a minimum area under the receiver operating characteristic curve (AUROC) of 0.77 for the /pa/ task and the best AUROC of 0.98 for the Italian speech classification. The findings highlight the potential of the generalisability of the wav2vec features and the performance of these features in the cross-database scenarios.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"47 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"An Experimental Setup for Power Loss Measurement up to 1 kHz using an Epstein Frame at CMI","authors":"Michal Ulvr","doi":"10.2478/msr-2023-0035","DOIUrl":"https://doi.org/10.2478/msr-2023-0035","url":null,"abstract":"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.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"51 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 mm3. 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.
{"title":"Modified Microstrip Feed Hybrid Rectangular Dielectric Resonator Antenna for Wireless Tri-Band Applications","authors":"B Chaitanya, K Manjunathachari","doi":"10.2478/msr-2023-0036","DOIUrl":"https://doi.org/10.2478/msr-2023-0036","url":null,"abstract":"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<jats:sup>3</jats: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.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"167 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Rajalakshmi, Ibrahim AlMohimeed, Mohamed Yacin Sikkandar, S. Sabarunisha Begum
Brain-Computer Interfaces (BCIs) facilitate the translation of brain activity into actionable commands and act as a crucial link between the human brain and the external environment. Electroencephalography (EEG)-based BCIs, which focus on motor imagery, have emerged as an important area of study in this domain. They are used in neurorehabilitation, neuroprosthetics, and gaming, among other applications. Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The proposed method includes several crucial stages to improve the precision and effectiveness of EEG-based motor imagery recognition. The pre-processing phase starts with the Variation Mode Decomposition (VMD) technique, which is used to improve EEG signals. The EEG signals are decomposed into different oscillatory modes by VMD, laying the groundwork for subsequent feature extraction. Feature extraction is a crucial component of the ODLR-EEGSM method. In this study, we use Stacked Sparse Auto Encoder (SSAE) models to identify significant patterns in the pre-processed EEG data. Our approach is based on the classification model using Deep Wavelet Neural Network (DWNN) optimized with Chaotic Dragonfly Algorithm (CDFA). CDFA optimizes the weight and bias values of the DWNN, significantly improving the classification accuracy of motor imagery. To evaluate the efficacy of the ODLR-EEGSM method, we use benchmark datasets to perform rigorous performance validation. The results show that our approach outperforms current methods in the classification of EEG motor imagery, confirming its promising performance. This study has the potential to make brain-computer interface applications in various fields more accurate and efficient, and pave the way for brain-controlled interactions with external systems and devices.
{"title":"Optimal Deep Learning-Based Recognition Model for EEG Enabled Brain-Computer Interfaces Using Motor-Imagery","authors":"S. Rajalakshmi, Ibrahim AlMohimeed, Mohamed Yacin Sikkandar, S. Sabarunisha Begum","doi":"10.2478/msr-2023-0031","DOIUrl":"https://doi.org/10.2478/msr-2023-0031","url":null,"abstract":"Brain-Computer Interfaces (BCIs) facilitate the translation of brain activity into actionable commands and act as a crucial link between the human brain and the external environment. Electroencephalography (EEG)-based BCIs, which focus on motor imagery, have emerged as an important area of study in this domain. They are used in neurorehabilitation, neuroprosthetics, and gaming, among other applications. Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The proposed method includes several crucial stages to improve the precision and effectiveness of EEG-based motor imagery recognition. The pre-processing phase starts with the Variation Mode Decomposition (VMD) technique, which is used to improve EEG signals. The EEG signals are decomposed into different oscillatory modes by VMD, laying the groundwork for subsequent feature extraction. Feature extraction is a crucial component of the ODLR-EEGSM method. In this study, we use Stacked Sparse Auto Encoder (SSAE) models to identify significant patterns in the pre-processed EEG data. Our approach is based on the classification model using Deep Wavelet Neural Network (DWNN) optimized with Chaotic Dragonfly Algorithm (CDFA). CDFA optimizes the weight and bias values of the DWNN, significantly improving the classification accuracy of motor imagery. To evaluate the efficacy of the ODLR-EEGSM method, we use benchmark datasets to perform rigorous performance validation. The results show that our approach outperforms current methods in the classification of EEG motor imagery, confirming its promising performance. This study has the potential to make brain-computer interface applications in various fields more accurate and efficient, and pave the way for brain-controlled interactions with external systems and devices.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"62 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"New Measurement Method of Oil-Water Two-Phase Flow with High Water Holdup and Low Rate by Phase State Regulation","authors":"Lianfu Han, Ming Chen, Xingbin Liu, Changfeng Fu","doi":"10.2478/msr-2023-0034","DOIUrl":"https://doi.org/10.2478/msr-2023-0034","url":null,"abstract":"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.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"29 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract High-speed tracked vehicles have complex powertrains that, in addition to power transfer and transformation, also perform the functions of vehicle steering and braking systems, as well as power supply system for various subsystems on the vehicle. Analyzing the power balance of a tracked vehicle, especially in specific moving scenarios such as the turning process, is of great importance for understanding the power requirements and workload of the powertrain components and their optimization. A simulation model was developed, based on the construction parameters of an experimentally tested high-speed tracked vehicle to reduce the time and material resources required for experimental testing. Both the simulation and experimental tests were conducted using the same input parameters and driving conditions for different vehicle turning scenarios. Simulation and experimental test results are compared to verify the accuracy of the simulation model. The analysis of the obtained results shows that the average value of the relative rpm error is about 5%, the average value of the relative torque error is about 7%, while the average value of the relative power error is about 6.5%.
{"title":"Experimental Validation of a High-Speed Tracked Vehicle Powertrain Simulation Model","authors":"Luka Ponorac, Ivan Blagojević","doi":"10.2478/msr-2023-0025","DOIUrl":"https://doi.org/10.2478/msr-2023-0025","url":null,"abstract":"Abstract High-speed tracked vehicles have complex powertrains that, in addition to power transfer and transformation, also perform the functions of vehicle steering and braking systems, as well as power supply system for various subsystems on the vehicle. Analyzing the power balance of a tracked vehicle, especially in specific moving scenarios such as the turning process, is of great importance for understanding the power requirements and workload of the powertrain components and their optimization. A simulation model was developed, based on the construction parameters of an experimentally tested high-speed tracked vehicle to reduce the time and material resources required for experimental testing. Both the simulation and experimental tests were conducted using the same input parameters and driving conditions for different vehicle turning scenarios. Simulation and experimental test results are compared to verify the accuracy of the simulation model. The analysis of the obtained results shows that the average value of the relative rpm error is about 5%, the average value of the relative torque error is about 7%, while the average value of the relative power error is about 6.5%.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135811255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The present study focuses on quantitative phase imaging of erythrocytes with the aim to evaluate the effects of high-dose methylprednisolone (HDMP) on erythrocytes in vivo under physiological conditions in human blood samples. Samples from ten patients, prescribed to be treated with 1000 mg/day intravenous methylprednisolone for 5 days, were analyzed by white light diffraction phase microscopy (WDPM) for quantitative imaging. WDPM, an optical measurement technique, enables single shot measurement and low speckle noise using white light. Quantitative phase imaging performed with this experimental setup allowed the determination of erythrocyte morphology with 9 different parameters. In vivo quantitative analysis of erythrocytes by WDPM, which is a simple and reliable method, shows that HDMP treatment has no significant effect on erythrocyte morphology. With the developing technology, interdisciplinary studies on individuals under treatment should play an important role in elucidating the interaction between steroids and erythrocytes.
{"title":"Novel Approach to Investigate the Effect of High-Dose Methylprednisolone on Erythrocyte Morphology: White Light Diffraction Microscopy","authors":"Cigdem Deniz, Ozlem Kocahan, Bengu Altunan, Aysun Unal","doi":"10.2478/msr-2023-0026","DOIUrl":"https://doi.org/10.2478/msr-2023-0026","url":null,"abstract":"Abstract The present study focuses on quantitative phase imaging of erythrocytes with the aim to evaluate the effects of high-dose methylprednisolone (HDMP) on erythrocytes in vivo under physiological conditions in human blood samples. Samples from ten patients, prescribed to be treated with 1000 mg/day intravenous methylprednisolone for 5 days, were analyzed by white light diffraction phase microscopy (WDPM) for quantitative imaging. WDPM, an optical measurement technique, enables single shot measurement and low speckle noise using white light. Quantitative phase imaging performed with this experimental setup allowed the determination of erythrocyte morphology with 9 different parameters. In vivo quantitative analysis of erythrocytes by WDPM, which is a simple and reliable method, shows that HDMP treatment has no significant effect on erythrocyte morphology. With the developing technology, interdisciplinary studies on individuals under treatment should play an important role in elucidating the interaction between steroids and erythrocytes.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135811469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The article describes the development and testing of a special prototype wearable device consisting of three optical photoplethysmography (PPG) sensors. The functionality of the developed triple PPG sensor was tested under normal laboratory conditions and in a running magnetic resonance imaging (MRI) scanner working with a low magnetic field. The results of the first measurements under normal laboratory conditions show that the obtained mutual positions of systolic/diastolic blood pressure values and heart pulse transmission parameters determined from the PPG waves can be fitted by a line segment with a sufficiently high slope. Measurement experiments inside the open-air MRI tomograph show the practical influence of vibrations and acoustic noise on the cardiac system of the examined persons, which was confirmed by a slight increase in the heart pulse rate and changes in pulse transmission time and pulse wave velocity. We plan to perform further measurements inside the whole-body MRI device producing more intensive vibrations and noise with expected higher stress impact on an exposed person.
{"title":"Analysis of Heart Pulse Transmission Parameters Determined from Multi-Channel PPG Signals Acquired by a Wearable Optical Sensor","authors":"Jiří Přibil, Anna Přibilová, Ivan Frollo","doi":"10.2478/msr-2023-0028","DOIUrl":"https://doi.org/10.2478/msr-2023-0028","url":null,"abstract":"Abstract The article describes the development and testing of a special prototype wearable device consisting of three optical photoplethysmography (PPG) sensors. The functionality of the developed triple PPG sensor was tested under normal laboratory conditions and in a running magnetic resonance imaging (MRI) scanner working with a low magnetic field. The results of the first measurements under normal laboratory conditions show that the obtained mutual positions of systolic/diastolic blood pressure values and heart pulse transmission parameters determined from the PPG waves can be fitted by a line segment with a sufficiently high slope. Measurement experiments inside the open-air MRI tomograph show the practical influence of vibrations and acoustic noise on the cardiac system of the examined persons, which was confirmed by a slight increase in the heart pulse rate and changes in pulse transmission time and pulse wave velocity. We plan to perform further measurements inside the whole-body MRI device producing more intensive vibrations and noise with expected higher stress impact on an exposed person.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135811251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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