Pub Date : 2025-03-17DOI: 10.1109/OJIM.2025.3551837
Niels F. Cleymans;Mark Van De Casteele;Julie Vandewalle;Aster K. Desouter;Frans K. Gorus;Kurt Barbé
Type 1 diabetes (T1Ds) is a chronic, for now, incurable multifactorial disease caused by the immune-mediated destruction of insulin-producing pancreatic $beta $ -cells, causing devastating and costly acute and chronic complications, despite lifelong insulin treatment. Abrupt clinical onset is preceded by an asymptomatic disease phase of highly variable duration which is marked by the sequential appearance of various types of $beta $ -cell autoantibodies (AAbs). Optimized predictions of time to clinical onset facilitate early diagnosis which is also key to reducing the incidence of inaugural life-threatening diabetic ketoacidosis and planning novel prevention trials in the asymptomatic stage. Research in first-degree relatives of known T1D patients has shown that disease progression can be predicted by genetic and immune biomarkers, but these predictions are limited by using the traditional statistical approaches such as Cox regression models. This explorative study aims to uncover the potential of random forest machine learning algorithms as survival models within the biomedical context of T1D. Two random forest survival models were constructed in R. The first constructed model predicts how long it will take for individuals to go from single to multiple AAb positivity (AAb+), a crucial step in T1D development. The second model predicts the transition from multiple AAb+ to the onset of T1D. This article demonstrates that our random forest survival models outperform traditional Cox regression methods; we conduct a detailed analysis of variable importance to uncover novel biomarker interactions; and we establish a refined framework for precise measurement and risk stratification of T1D, paving the way for earlier and more targeted intervention.
{"title":"Analyzing Random Forest’s Predictive Capability for Type 1 Diabetes Progression","authors":"Niels F. Cleymans;Mark Van De Casteele;Julie Vandewalle;Aster K. Desouter;Frans K. Gorus;Kurt Barbé","doi":"10.1109/OJIM.2025.3551837","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3551837","url":null,"abstract":"Type 1 diabetes (T1Ds) is a chronic, for now, incurable multifactorial disease caused by the immune-mediated destruction of insulin-producing pancreatic <inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-cells, causing devastating and costly acute and chronic complications, despite lifelong insulin treatment. Abrupt clinical onset is preceded by an asymptomatic disease phase of highly variable duration which is marked by the sequential appearance of various types of <inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-cell autoantibodies (AAbs). Optimized predictions of time to clinical onset facilitate early diagnosis which is also key to reducing the incidence of inaugural life-threatening diabetic ketoacidosis and planning novel prevention trials in the asymptomatic stage. Research in first-degree relatives of known T1D patients has shown that disease progression can be predicted by genetic and immune biomarkers, but these predictions are limited by using the traditional statistical approaches such as Cox regression models. This explorative study aims to uncover the potential of random forest machine learning algorithms as survival models within the biomedical context of T1D. Two random forest survival models were constructed in R. The first constructed model predicts how long it will take for individuals to go from single to multiple AAb positivity (AAb+), a crucial step in T1D development. The second model predicts the transition from multiple AAb+ to the onset of T1D. This article demonstrates that our random forest survival models outperform traditional Cox regression methods; we conduct a detailed analysis of variable importance to uncover novel biomarker interactions; and we establish a refined framework for precise measurement and risk stratification of T1D, paving the way for earlier and more targeted intervention.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10929762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1109/OJIM.2025.3569358
Jurgen Vanhamel;Kin Chio Chao;Yifeng Chen
In space applications, CubeSats are used for all kinds of commercial and research purposes. These small satellites are launched in such large numbers that from a pollution point of view it makes sense to return them intact to Earth. To establish this, a dedicated reentry is needed. During this reentry process, the CubeSat has to make use of a heatshield which deforms due to multiple forces acting upon the structure. In order to monitor this heatshield, this study aims at investigating, designing, simulating, and testing the integration and readout of fiber Bragg gratings (FBGs) into a mock-up heatshield, in order to monitor its shape. Therefore, a mock-up of a CubeSat and its accompanying heatshield is constructed to simulate the realistic use of FBGs. Based on the external dimensions of the heatshield, the study gives a practical installation pattern for the FBGs. A 3-D simulation model of the heatshield and accompanying CubeSat is built. To achieve deformation in this 3-D model, the study proposes an algorithm based on single-point data. Using existing OROCOS/ROS middleware, the study establishes a comprehensive system for setting up and reading out FBGs in order to gather information on the heatshield’s status. Finally, after testing the mock-up heatshield set, the system can reflect the deformation of the heatshield in real-time in the 3-D model. Additionally, the system can save the entire deformation process of the heatshield as a series of model files, which can be used for sophisticated static analysis.
{"title":"Reentry CubeSat Heatshield Monitoring System Using Fiber Bragg Gratings","authors":"Jurgen Vanhamel;Kin Chio Chao;Yifeng Chen","doi":"10.1109/OJIM.2025.3569358","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3569358","url":null,"abstract":"In space applications, CubeSats are used for all kinds of commercial and research purposes. These small satellites are launched in such large numbers that from a pollution point of view it makes sense to return them intact to Earth. To establish this, a dedicated reentry is needed. During this reentry process, the CubeSat has to make use of a heatshield which deforms due to multiple forces acting upon the structure. In order to monitor this heatshield, this study aims at investigating, designing, simulating, and testing the integration and readout of fiber Bragg gratings (FBGs) into a mock-up heatshield, in order to monitor its shape. Therefore, a mock-up of a CubeSat and its accompanying heatshield is constructed to simulate the realistic use of FBGs. Based on the external dimensions of the heatshield, the study gives a practical installation pattern for the FBGs. A 3-D simulation model of the heatshield and accompanying CubeSat is built. To achieve deformation in this 3-D model, the study proposes an algorithm based on single-point data. Using existing OROCOS/ROS middleware, the study establishes a comprehensive system for setting up and reading out FBGs in order to gather information on the heatshield’s status. Finally, after testing the mock-up heatshield set, the system can reflect the deformation of the heatshield in real-time in the 3-D model. Additionally, the system can save the entire deformation process of the heatshield as a series of model files, which can be used for sophisticated static analysis.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1109/OJIM.2025.3548817
Zeyu Zhang;Xiaojun Tang;Xiaoshan Li;Chongzhi Liu
Time difference of arrival (TDOA) and direction of arrival (DOA) are, respectively, applied to detect partial discharge (PD) target with ultrasonic signal. The reason why these two methods cannot be applied at the same time is that the required parameters, such as arrival time differences and directions, cannot be extracted simultaneously. This article solves the problem of calculating these two parameters simultaneously, and then proposes the hybrid TDOA/DOA localization with two $2times 2$ sensor arrays in PD localization first. This innovation can help PD positioning to obtain better accuracy and lower hardware requirement. Our previous research, which is kernel principal component analysis modified noncircular fast independent component analysis (KPCA-mnc-FastICA), is further studied to calculate arrival time difference and direction of multiple sources simultaneously. To tackle the problem of slow calculation speed, this article explores a combined method of hybrid TDOA/DOA positioning model and sparrow search algorithm (SSA) with two arrays which is sufficient to meet the requirement of multiple PD localization. The spatial accuracy of the discharge model measurement in the laboratory and the numerous PD signals mixed simulation experiment are supported by empirical evidence. The outcomes demonstrate the suggested method’s superior performance in terms of hardware reduction and multiple target localization.
{"title":"Hybrid TDOA/DOA Multiple PD Localization Using Two Arrays With KPCA-mnc-FastICA and Sparrow Search Algorithm","authors":"Zeyu Zhang;Xiaojun Tang;Xiaoshan Li;Chongzhi Liu","doi":"10.1109/OJIM.2025.3548817","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3548817","url":null,"abstract":"Time difference of arrival (TDOA) and direction of arrival (DOA) are, respectively, applied to detect partial discharge (PD) target with ultrasonic signal. The reason why these two methods cannot be applied at the same time is that the required parameters, such as arrival time differences and directions, cannot be extracted simultaneously. This article solves the problem of calculating these two parameters simultaneously, and then proposes the hybrid TDOA/DOA localization with two <inline-formula> <tex-math>$2times 2$ </tex-math></inline-formula> sensor arrays in PD localization first. This innovation can help PD positioning to obtain better accuracy and lower hardware requirement. Our previous research, which is kernel principal component analysis modified noncircular fast independent component analysis (KPCA-mnc-FastICA), is further studied to calculate arrival time difference and direction of multiple sources simultaneously. To tackle the problem of slow calculation speed, this article explores a combined method of hybrid TDOA/DOA positioning model and sparrow search algorithm (SSA) with two arrays which is sufficient to meet the requirement of multiple PD localization. The spatial accuracy of the discharge model measurement in the laboratory and the numerous PD signals mixed simulation experiment are supported by empirical evidence. The outcomes demonstrate the suggested method’s superior performance in terms of hardware reduction and multiple target localization.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10919048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-07DOI: 10.1109/OJIM.2025.3544347
Erik Huemiller;Megan McGovern;Xinyu Du;James Salvador;Sean Wagner;William Collin
The increase in interest in lithium-ion battery cell performance indicators has been propelled by automotive manufacturers’ paradigm shift from internal combustion engine (ICE) vehicles toward electric vehicles (EVs). Accordingly, recent literature has seen a surge in the number of reviews related to nondestructive evaluation for lithium-ion batteries in EVs. Quantifying the battery cell performance is a key to successfully enabling the transition from ICE to EVs. This review seeks to provide an instrumentation and measurement perspective on the state of the art for battery cell performance indicators, including a deeper dive into their limitations and capabilities. This article will cover the most commonly employed measurement techniques, including electrical techniques, mechanical methods, and thermal analysis techniques. This article is organized by measurement technique, where each section will include an introduction to the technique, how it applies to batteries in the EV space, and will conclude with recommendations for extending the state of the art.
{"title":"Industrial EV Battery Performance Evaluation: A Review From the Instrumentation Perspective","authors":"Erik Huemiller;Megan McGovern;Xinyu Du;James Salvador;Sean Wagner;William Collin","doi":"10.1109/OJIM.2025.3544347","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3544347","url":null,"abstract":"The increase in interest in lithium-ion battery cell performance indicators has been propelled by automotive manufacturers’ paradigm shift from internal combustion engine (ICE) vehicles toward electric vehicles (EVs). Accordingly, recent literature has seen a surge in the number of reviews related to nondestructive evaluation for lithium-ion batteries in EVs. Quantifying the battery cell performance is a key to successfully enabling the transition from ICE to EVs. This review seeks to provide an instrumentation and measurement perspective on the state of the art for battery cell performance indicators, including a deeper dive into their limitations and capabilities. This article will cover the most commonly employed measurement techniques, including electrical techniques, mechanical methods, and thermal analysis techniques. This article is organized by measurement technique, where each section will include an introduction to the technique, how it applies to batteries in the EV space, and will conclude with recommendations for extending the state of the art.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1109/OJIM.2025.3548816
G. B. Krishnapriya;R. N. Ponnalagu;Sanket Goel
This study introduces a novel, computationally efficient time-domain (TD) algorithm for accurate breath rate (BR) estimation from single-lead ECG signals, designed for wearable devices. The proposed algorithm uses statistical TD parameters—mean, prominence, and distance (MPD)—to detect valid respiratory peaks in ECG-derived respiration (EDR) signals. The performance of the MPD algorithm was evaluated using two datasets: 1) a benchmark database containing ECG acquired during dynamic activities and 2) a real-time dataset comprising ECG signals from five subjects performing dynamic activities, including standing, jogging, and recovery. Comparative analysis against state-of-the-art TD methods, such as count-orig, zero-crossing detection, peak detection, and adaptive threshold techniques, demonstrates the superiority of MPD in both accuracy and computational efficiency. On the benchmark dataset, MPD achieved a mean absolute error (MAE) of 3.66 bpm and mean absolute percentage error (MAPE) of 23.69%, outperforming the Count-Orig method (MAE = 5.09 bpm, MAPE = 32.76%). For real-time data, MPD further demonstrated robust performance with an MAE of 1.53 bpm and MAPE of 7.25%. The algorithm’s design simplicity, combined with its ability to handle spurious peaks and varying signal conditions, makes it particularly suitable for resource-constrained wearable applications. Its high accuracy, low computational demands, and adaptability across activity conditions underscore its potential for continuous, real-time respiratory monitoring in diverse scenarios.
{"title":"A Resource-Efficient Time-Domain-Based Algorithm to Estimate Respiration Rate From Single-Lead ECG Signal","authors":"G. B. Krishnapriya;R. N. Ponnalagu;Sanket Goel","doi":"10.1109/OJIM.2025.3548816","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3548816","url":null,"abstract":"This study introduces a novel, computationally efficient time-domain (TD) algorithm for accurate breath rate (BR) estimation from single-lead ECG signals, designed for wearable devices. The proposed algorithm uses statistical TD parameters—mean, prominence, and distance (MPD)—to detect valid respiratory peaks in ECG-derived respiration (EDR) signals. The performance of the MPD algorithm was evaluated using two datasets: 1) a benchmark database containing ECG acquired during dynamic activities and 2) a real-time dataset comprising ECG signals from five subjects performing dynamic activities, including standing, jogging, and recovery. Comparative analysis against state-of-the-art TD methods, such as count-orig, zero-crossing detection, peak detection, and adaptive threshold techniques, demonstrates the superiority of MPD in both accuracy and computational efficiency. On the benchmark dataset, MPD achieved a mean absolute error (MAE) of 3.66 bpm and mean absolute percentage error (MAPE) of 23.69%, outperforming the Count-Orig method (MAE = 5.09 bpm, MAPE = 32.76%). For real-time data, MPD further demonstrated robust performance with an MAE of 1.53 bpm and MAPE of 7.25%. The algorithm’s design simplicity, combined with its ability to handle spurious peaks and varying signal conditions, makes it particularly suitable for resource-constrained wearable applications. Its high accuracy, low computational demands, and adaptability across activity conditions underscore its potential for continuous, real-time respiratory monitoring in diverse scenarios.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10915585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1109/OJIM.2025.3566850
Bent Walther;André Froehly;Thomas Musch;Marcel van Delden
High-resolution radar systems use fast and broadband frequency chirps to enable high resolutions with short measurement times. One challenge in these radar systems is minimizing the linearity error in the generated frequency chirps, directly influencing the achievable depth resolution and precision. Current measurement methods cannot meet the bandwidth requirements of modern chirp generators, making it difficult to determine the linearity error precisely. For this reason, we present a novel measurement method that uses a frequency divider to enable linearity measurements with very high accuracy using conventional measurement equipment. Since frequency dividers generate additional artifacts, such as harmonics, we present a novel correction algorithm to prevent these artifacts in the measurement results. The algorithmic framework utilizes a virtual digital frequency chirp to detect RMS frequency errors in the sub-Hz range unprecedentedly. Furthermore, the influence of the frequency divider is systematically characterized, enabling precise correction of its contributions to the measurement results. This method improves the accuracy of chirp linearity measurements and allows for the characterization of current linearity measurement systems. The approach was validated through experimental measurements, even under noise, aliasing, and harmonic conditions.
{"title":"High-Accuracy Linearity Measurement of Broadband Frequency Chirps","authors":"Bent Walther;André Froehly;Thomas Musch;Marcel van Delden","doi":"10.1109/OJIM.2025.3566850","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3566850","url":null,"abstract":"High-resolution radar systems use fast and broadband frequency chirps to enable high resolutions with short measurement times. One challenge in these radar systems is minimizing the linearity error in the generated frequency chirps, directly influencing the achievable depth resolution and precision. Current measurement methods cannot meet the bandwidth requirements of modern chirp generators, making it difficult to determine the linearity error precisely. For this reason, we present a novel measurement method that uses a frequency divider to enable linearity measurements with very high accuracy using conventional measurement equipment. Since frequency dividers generate additional artifacts, such as harmonics, we present a novel correction algorithm to prevent these artifacts in the measurement results. The algorithmic framework utilizes a virtual digital frequency chirp to detect RMS frequency errors in the sub-Hz range unprecedentedly. Furthermore, the influence of the frequency divider is systematically characterized, enabling precise correction of its contributions to the measurement results. This method improves the accuracy of chirp linearity measurements and allows for the characterization of current linearity measurement systems. The approach was validated through experimental measurements, even under noise, aliasing, and harmonic conditions.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work investigated the effects of polymer films [chitosan (CS), Nafion (NF), and polyvinyl alcohol (PVA)] on the performances of acetylcholinesterase (AChE) biosensors for the selectivity of pesticide types and their concentration levels using principal component analysis (PCA). AChE was immobilized on montmorillonite/gold nanoparticles (Mt/AuNPs). The surface charge of the polymer films significantly influenced sensor performance: NF and PVA films, with negative charges, enhanced the preconcentration of positively charged acetylthiocholine chloride (ATCh), resulting in increased electroactive surface area and current response. In contrast, the positively charged CS film impeded mass diffusion of ATCh, reducing electroactive surface area and current response. Sensor/PVA showed the lowest limit of detection (LOD) for chlorpyrifos and pirimiphos-methyl, while Sensor/CS showed the lowest LOD for carbaryl. The unique response from three different biosensors demonstrated the successful discrimination of the pesticide group and their concentration levels by PCA. The total contribution variance was 99.8%. PC1 suggested the concentration levels, while PC2 was explicitly realized for organophosphate pesticides (negative PC2) and carbaryl (positive PC2). These findings demonstrate that the simple application of polymer coatings, combined with PCA, can significantly improve the selectivity and storage stability of AChE-based biosensors.
{"title":"Selectivity Enhancement of Pesticide Biosensors via Polymer Coating","authors":"Angkana Phongphut;Seeroong Prichanont;Chanchana Thanachayanont;Hsin-Yi Tsai;Yu-Hsuan Lin;Keng-Ku Liu;Ruey-An Doong;Bralee Chayasombat","doi":"10.1109/OJIM.2025.3545978","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3545978","url":null,"abstract":"This work investigated the effects of polymer films [chitosan (CS), Nafion (NF), and polyvinyl alcohol (PVA)] on the performances of acetylcholinesterase (AChE) biosensors for the selectivity of pesticide types and their concentration levels using principal component analysis (PCA). AChE was immobilized on montmorillonite/gold nanoparticles (Mt/AuNPs). The surface charge of the polymer films significantly influenced sensor performance: NF and PVA films, with negative charges, enhanced the preconcentration of positively charged acetylthiocholine chloride (ATCh), resulting in increased electroactive surface area and current response. In contrast, the positively charged CS film impeded mass diffusion of ATCh, reducing electroactive surface area and current response. Sensor/PVA showed the lowest limit of detection (LOD) for chlorpyrifos and pirimiphos-methyl, while Sensor/CS showed the lowest LOD for carbaryl. The unique response from three different biosensors demonstrated the successful discrimination of the pesticide group and their concentration levels by PCA. The total contribution variance was 99.8%. PC1 suggested the concentration levels, while PC2 was explicitly realized for organophosphate pesticides (negative PC2) and carbaryl (positive PC2). These findings demonstrate that the simple application of polymer coatings, combined with PCA, can significantly improve the selectivity and storage stability of AChE-based biosensors.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1109/OJIM.2025.3527536
Baoqiang Du;Yangfan Su;Zerui Yang
To meet the requirements of high-precision measurement of time-frequency multiparameters, a high-accuracy frequency standard comparison technology combining adaptive frequency and Lissajous figure is proposed. This technology uses only one reference frequency source to realize the frequency standard comparison and frequency measurement between any frequency signals without frequency normalization. First, a new frequency standard comparison signal is obtained by using an adaptive frequency standard generation module to roughly measure the measured frequency. Second, the turning period is measured by observing the Lissajous figure. Third, via the turning period and the function relation of frequency deviation, the relative frequency difference between the measured and frequency standard signals can be obtained. Finally, the phase relation between the measured and frequency standard signals is determined by oscilloscope, and then the high-accuracy measurement of the measured frequency can be realized. The testing results indicate that the accuracy of the frequency measurement in the radiofrequency range can achieve the $10^{-12}$ order of magnitude. Compared with the traditional frequency standard comparison technology, this technology has many characteristics, such as simple operation, low cost, low noise, and high measurement accuracy.
{"title":"High-Accuracy Frequency Standard Comparison Technology Combining Adaptive Frequency and Lissajous Figure","authors":"Baoqiang Du;Yangfan Su;Zerui Yang","doi":"10.1109/OJIM.2025.3527536","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3527536","url":null,"abstract":"To meet the requirements of high-precision measurement of time-frequency multiparameters, a high-accuracy frequency standard comparison technology combining adaptive frequency and Lissajous figure is proposed. This technology uses only one reference frequency source to realize the frequency standard comparison and frequency measurement between any frequency signals without frequency normalization. First, a new frequency standard comparison signal is obtained by using an adaptive frequency standard generation module to roughly measure the measured frequency. Second, the turning period is measured by observing the Lissajous figure. Third, via the turning period and the function relation of frequency deviation, the relative frequency difference between the measured and frequency standard signals can be obtained. Finally, the phase relation between the measured and frequency standard signals is determined by oscilloscope, and then the high-accuracy measurement of the measured frequency can be realized. The testing results indicate that the accuracy of the frequency measurement in the radiofrequency range can achieve the <inline-formula> <tex-math>$10^{-12}$ </tex-math></inline-formula> order of magnitude. Compared with the traditional frequency standard comparison technology, this technology has many characteristics, such as simple operation, low cost, low noise, and high measurement accuracy.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10883668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1109/OJIM.2025.3540122
Farzaneh Ahmadi;Reza Zoughi
This study presents the results of using a millimeter-wave reflectometer system, operating at 150 GHz, for demonstrating the basic efficacy of measuring electromagnetic scattering of metal powder used in laser powder bed fusion (LPBF) additive manufacturing (AM). Metal spatter (spatial) properties—particles ejected during laser interaction with metal powder—are potential indicators of process deviations (from a prescribed manner) or defect formation in a printed part. Electromagnetic modeling of scattering properties of metal powder has shown to be a potentially viable tool for assessing metal powder cloud spatial distribution and other properties. This work takes the next natural step by measuring the scattering properties of a cloud of metal powder. This investigation begins with samples of stationary powder, demonstrating a strong correlation between packing density and the measured output voltage of the reflectometer. The study progresses into detecting the flow of relatively large metal particles (i.e., solder balls) in air and measuring responses of flowing metal powder blown inside a nitrogen-filled chamber. Results crucially confirm that this method can distinguish a cloud of metal powder from the baseline condition where no powder is present. While promising, this investigation represents an initial step in the long journey toward optimizing millimeter-wave methods for integration into real-world LPBF AM systems.
{"title":"Microwave Reflectometry for Online Monitoring of Metal Powder Used in Laser Powder Bed Fusion Additive Manufacturing","authors":"Farzaneh Ahmadi;Reza Zoughi","doi":"10.1109/OJIM.2025.3540122","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3540122","url":null,"abstract":"This study presents the results of using a millimeter-wave reflectometer system, operating at 150 GHz, for demonstrating the basic efficacy of measuring electromagnetic scattering of metal powder used in laser powder bed fusion (LPBF) additive manufacturing (AM). Metal spatter (spatial) properties—particles ejected during laser interaction with metal powder—are potential indicators of process deviations (from a prescribed manner) or defect formation in a printed part. Electromagnetic modeling of scattering properties of metal powder has shown to be a potentially viable tool for assessing metal powder cloud spatial distribution and other properties. This work takes the next natural step by measuring the scattering properties of a cloud of metal powder. This investigation begins with samples of stationary powder, demonstrating a strong correlation between packing density and the measured output voltage of the reflectometer. The study progresses into detecting the flow of relatively large metal particles (i.e., solder balls) in air and measuring responses of flowing metal powder blown inside a nitrogen-filled chamber. Results crucially confirm that this method can distinguish a cloud of metal powder from the baseline condition where no powder is present. While promising, this investigation represents an initial step in the long journey toward optimizing millimeter-wave methods for integration into real-world LPBF AM systems.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unattended luggage or containers in public areas, such as railway stations and buildings, like airports, may trigger bomb disposal operations. While these instances frequently involve harmless forgotten bags, they can also signal the presence of unconventional explosives and incendiary devices, which may include chemical, biological, radiological, nuclear, and explosive (CBRNe) elements. Research aspects of this work include the development of a mobile laser cutting system (LCS) to enhance the capabilities of police bomb disposal units in neutralizing improvised explosive devices (IEDs) and forensic evidence collection, thereby improving the safety of the public and defusing experts. This article presents the results of the development of a breakthrough detection system using an appropriate sensor technology. Parameters are determined by means of sensory monitoring to cut through various materials without interacting with the layer behind them. The investigation includes real cutting tests with the mobile LCS on various materials. For example, breakthrough times for polystyrene ranged from 75 to 250 s depending on geometry, while sensor accuracy in detecting cutting progress exceeded 90%. Additionally, explosive residues as low as 10 ng were successfully detected post-cutting, highlighting the system’s forensic compatibility. The results show that sensor-based breakthrough detection is feasible for the laser cutting of IED-relevant objects.
{"title":"Sensory Monitoring for Breakthrough Detection in Mobile Laser Cutting of Various Materials in the Context of Improvised Explosive Device Disposal","authors":"Emre Ünal;Matthias Muhr;Dominik Wild;Cathrin Theiss;Moritz Schumacher;Gerhard Holl;Peter Kaul","doi":"10.1109/OJIM.2025.3540127","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3540127","url":null,"abstract":"Unattended luggage or containers in public areas, such as railway stations and buildings, like airports, may trigger bomb disposal operations. While these instances frequently involve harmless forgotten bags, they can also signal the presence of unconventional explosives and incendiary devices, which may include chemical, biological, radiological, nuclear, and explosive (CBRNe) elements. Research aspects of this work include the development of a mobile laser cutting system (LCS) to enhance the capabilities of police bomb disposal units in neutralizing improvised explosive devices (IEDs) and forensic evidence collection, thereby improving the safety of the public and defusing experts. This article presents the results of the development of a breakthrough detection system using an appropriate sensor technology. Parameters are determined by means of sensory monitoring to cut through various materials without interacting with the layer behind them. The investigation includes real cutting tests with the mobile LCS on various materials. For example, breakthrough times for polystyrene ranged from 75 to 250 s depending on geometry, while sensor accuracy in detecting cutting progress exceeded 90%. Additionally, explosive residues as low as 10 ng were successfully detected post-cutting, highlighting the system’s forensic compatibility. The results show that sensor-based breakthrough detection is feasible for the laser cutting of IED-relevant objects.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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