Pub Date : 2025-09-05DOI: 10.1109/OJIM.2025.3605224
Tareq Almustafa;Bilel Ben Atitallah;Khaldon Lweesy;Mohammed Ibbini;Olfa Kanoun
This study investigates the potential of impedimetric measurements providing deep muscle information for recognizing 36 American Sign Language (ASL) hand signs. Two measurement methods are considered together for the first time: electrical impedance myography (EIM) and electrical impedance tomography (EIT). EIM was measured along the anterior forearm, while 8-electrode EIT was recorded around the forearm below the elbow. Data were acquired from three volunteers, with each hand sign performed ten times. A correlation analysis was conducted to identify the relevant EIM frequencies to distinguish between hand signs. Among all evaluated algorithms, the random forest classifier achieves the highest classification performance. Classification based on the resistance and reactance at the selected EIM frequencies achieved $61.54~{pm }~0.85$ %, while classification based on EIT boundary voltages achieved 91.04% ${pm }~0.46$ %. Combining the results from both classifiers into an EIM-EIT hybrid classifier improved the accuracy to $92.57~{pm }~0.41$ %, effectively reducing ambiguities between similar hand signs. Achieved results considerably outperform state-of-the-art works, which typically classify fewer hand signs or achieve lower accuracy.
{"title":"Hand Signs Recognition by Deep Muscle Impedimetric Measurements","authors":"Tareq Almustafa;Bilel Ben Atitallah;Khaldon Lweesy;Mohammed Ibbini;Olfa Kanoun","doi":"10.1109/OJIM.2025.3605224","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3605224","url":null,"abstract":"This study investigates the potential of impedimetric measurements providing deep muscle information for recognizing 36 American Sign Language (ASL) hand signs. Two measurement methods are considered together for the first time: electrical impedance myography (EIM) and electrical impedance tomography (EIT). EIM was measured along the anterior forearm, while 8-electrode EIT was recorded around the forearm below the elbow. Data were acquired from three volunteers, with each hand sign performed ten times. A correlation analysis was conducted to identify the relevant EIM frequencies to distinguish between hand signs. Among all evaluated algorithms, the random forest classifier achieves the highest classification performance. Classification based on the resistance and reactance at the selected EIM frequencies achieved <inline-formula> <tex-math>$61.54~{pm }~0.85$ </tex-math></inline-formula>%, while classification based on EIT boundary voltages achieved 91.04% <inline-formula> <tex-math>${pm }~0.46$ </tex-math></inline-formula>%. Combining the results from both classifiers into an EIM-EIT hybrid classifier improved the accuracy to <inline-formula> <tex-math>$92.57~{pm }~0.41$ </tex-math></inline-formula>%, effectively reducing ambiguities between similar hand signs. Achieved results considerably outperform state-of-the-art works, which typically classify fewer hand signs or achieve lower accuracy.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-9"},"PeriodicalIF":1.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210080","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 : 2025-09-05DOI: 10.1109/OJIM.2025.3604983
Ivan Struzhko;Marc García-Bermúdez;Jordi Solé-Lloveras;Manuel Añón-Cancela;Tom Hartman;Marco A. Azpúrua;Frank Leferink
Although the time-domain approach to electromagnetic interference evaluation offers numerous advantages, including shorter test duration and multichannel acquisition, its practical adoption remains limited. This is mainly because existing standards, such as CISPR 16-1-1, do not explicitly address direct sampling time-domain measuring receivers or define specific calibration and validation procedures for them. While several studies have demonstrated successful use cases, a comprehensive validation of such systems has not yet been performed. This article presents multilevel experimental validations of time-domain measuring receivers, focusing on the direct sampling approach and oscilloscope-based implementations. First, meta-comparisons of FFT-based receivers are made using calibration data obtained from certificates of accredited laboratories. Then, controlled signal sources with known time and spectral characteristics are used to cross-check with different measuring receiver models. Finally, several instruments are benchmarked with respect to their standard detector outputs when measuring the emissions of a power converter while spread spectrum techniques are used. The results show good agreement between the measuring receivers in the time domain and the tested conventional receivers in the frequency domain within the standard error, even though the complexity of the measured signals is different.
{"title":"Multilevel Validation of Direct Sampling Time-Domain Measuring Receivers","authors":"Ivan Struzhko;Marc García-Bermúdez;Jordi Solé-Lloveras;Manuel Añón-Cancela;Tom Hartman;Marco A. Azpúrua;Frank Leferink","doi":"10.1109/OJIM.2025.3604983","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3604983","url":null,"abstract":"Although the time-domain approach to electromagnetic interference evaluation offers numerous advantages, including shorter test duration and multichannel acquisition, its practical adoption remains limited. This is mainly because existing standards, such as CISPR 16-1-1, do not explicitly address direct sampling time-domain measuring receivers or define specific calibration and validation procedures for them. While several studies have demonstrated successful use cases, a comprehensive validation of such systems has not yet been performed. This article presents multilevel experimental validations of time-domain measuring receivers, focusing on the direct sampling approach and oscilloscope-based implementations. First, meta-comparisons of FFT-based receivers are made using calibration data obtained from certificates of accredited laboratories. Then, controlled signal sources with known time and spectral characteristics are used to cross-check with different measuring receiver models. Finally, several instruments are benchmarked with respect to their standard detector outputs when measuring the emissions of a power converter while spread spectrum techniques are used. The results show good agreement between the measuring receivers in the time domain and the tested conventional receivers in the frequency domain within the standard error, even though the complexity of the measured signals is different.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-13"},"PeriodicalIF":1.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141588","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 : 2025-09-01DOI: 10.1109/OJIM.2025.3604985
Swathi Muthyala Ramesh;Doyle T. Motes;Kristen M. Donnell
frequency selective surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters, such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This article presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature ($23~^{circ }$ C–$223~^{circ }$ C). The sensor integrates two-unit cells: 1) a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity (~16–18 MHz/0.1%) and 2) a loop-based cell with a temperature-sensitive dielectric for temperature measurements, achieving a sensitivity of ~0.54 MHz/°C. The dual-measurand capability is achieved by designing the sensor with two distinct resonant frequencies, each corresponding to a specific parameter. Simulation and measurement results demonstrate that the proposed sensor achieves greater strain sensitivity as compared to existing FSS-based strain sensors while maintaining temperature sensitivity on par with existing FSS temperature sensors. The study also characterizes thermal expansion-induced errors through simulation and proposes a compensation approach that successfully improves sensitivity. Overall, this work demonstrates the potential of FSS-based sensors as compact, multimeasurand solutions for SHM applications, offering high sensitivity and reliability with minimal cross-sensitivity effects.
{"title":"Dual Parameter FSS-Based Sensing for Structural Health Monitoring Applications","authors":"Swathi Muthyala Ramesh;Doyle T. Motes;Kristen M. Donnell","doi":"10.1109/OJIM.2025.3604985","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3604985","url":null,"abstract":"frequency selective surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters, such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This article presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature (<inline-formula> <tex-math>$23~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$223~^{circ }$ </tex-math></inline-formula>C). The sensor integrates two-unit cells: 1) a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity (~16–18 MHz/0.1%) and 2) a loop-based cell with a temperature-sensitive dielectric for temperature measurements, achieving a sensitivity of ~0.54 MHz/°C. The dual-measurand capability is achieved by designing the sensor with two distinct resonant frequencies, each corresponding to a specific parameter. Simulation and measurement results demonstrate that the proposed sensor achieves greater strain sensitivity as compared to existing FSS-based strain sensors while maintaining temperature sensitivity on par with existing FSS temperature sensors. The study also characterizes thermal expansion-induced errors through simulation and proposes a compensation approach that successfully improves sensitivity. Overall, this work demonstrates the potential of FSS-based sensors as compact, multimeasurand solutions for SHM applications, offering high sensitivity and reliability with minimal cross-sensitivity effects.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-12"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11146514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090028","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 : 2025-08-01DOI: 10.1109/OJIM.2025.3594383
Joseph T. Filbert;Matthew R. Dvorsky;Reza Zoughi
Accurate, fast, and in-line determination of the microwave properties of free-flowing materials, such as gases, liquids, and powders, remains a challenge. This work details the development of a novel microwave resonant sensor based on a circular open-cavity design using a circular waveguide feed operating in the TE01 circular waveguide mode, to address this issue. This open-cavity design allows a free-flowing material under test (MUT), to pass through the cavity enabling in-line measurement of its reflection coefficient, which can then be used to estimate its effective dielectric and magnetic properties. A forward model describing the electromagnetic (EM) behavior of the sensor is derived using modal analysis and subsequently validated using full-wave simulation. The forward model facilitates accurate inversion of the measured reflection coefficient (i.e., S-parameter, S11) data for determining the effective dielectric or magnetic properties of the MUT. To demonstrate the efficacy of the sensor, measurements of an offline (static, not flowing) powder, as well as a flowing metal powder, are presented.
{"title":"Circular Open-Cavity Resonator for Microwave Characterization of Free-Flowing Materials","authors":"Joseph T. Filbert;Matthew R. Dvorsky;Reza Zoughi","doi":"10.1109/OJIM.2025.3594383","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3594383","url":null,"abstract":"Accurate, fast, and in-line determination of the microwave properties of free-flowing materials, such as gases, liquids, and powders, remains a challenge. This work details the development of a novel microwave resonant sensor based on a circular open-cavity design using a circular waveguide feed operating in the TE01 circular waveguide mode, to address this issue. This open-cavity design allows a free-flowing material under test (MUT), to pass through the cavity enabling in-line measurement of its reflection coefficient, which can then be used to estimate its effective dielectric and magnetic properties. A forward model describing the electromagnetic (EM) behavior of the sensor is derived using modal analysis and subsequently validated using full-wave simulation. The forward model facilitates accurate inversion of the measured reflection coefficient (i.e., S-parameter, S11) data for determining the effective dielectric or magnetic properties of the MUT. To demonstrate the efficacy of the sensor, measurements of an offline (static, not flowing) powder, as well as a flowing metal powder, are presented.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-13"},"PeriodicalIF":1.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918322","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 : 2025-07-31DOI: 10.1109/OJIM.2025.3594381
Ernesto Serrano-Finetti;Gemma Hornero;Oscar Casas
There is a growing interest in developing impedance sensors able to work at low power and with a small footprint. The analog lock-in amplifier (an amplitude-modulated (AM) demodulator) is a common solution to recover the baseband signal from modulating sensors while avoiding low-frequency noise. However, it uses several active components whose total power consumption might shorten battery life. In this work, we propose a simple AM demodulator based on a fully differential switched-gain amplifier. Using op amps with shutdown enables the gain switching between 0 and 1, which recovers the baseband signal in a similar way to a conventional +1/−1 switched gain amplifier, but with a 50% amplitude decrease in the demodulated signal. By using a power-down signal synchronized with the carrier, it is possible to program a 0° or 90° phase that enables in-phase and quadrature demodulation, ultimately allowing the measurement of complex impedances. Tests were performed in two different situations: static and time-varying impedances, and with two different op amp models, the OPA363 and the ADA4806-1. In the former test, several resistors and capacitors were measured, yielding deviations from a reference instrument below 0.5% for resistors and below 2.7% for capacitors when using the OPA363. In the latter test, the electrical bioimpedance changes of the hand-to-hand body segment of a number of healthy volunteers were recorded, enabling the detection of the respiratory and pulse rate.
{"title":"Compact, Fully Differential Analog Amplitude Demodulator by Power Supply Voltage Switching","authors":"Ernesto Serrano-Finetti;Gemma Hornero;Oscar Casas","doi":"10.1109/OJIM.2025.3594381","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3594381","url":null,"abstract":"There is a growing interest in developing impedance sensors able to work at low power and with a small footprint. The analog lock-in amplifier (an amplitude-modulated (AM) demodulator) is a common solution to recover the baseband signal from modulating sensors while avoiding low-frequency noise. However, it uses several active components whose total power consumption might shorten battery life. In this work, we propose a simple AM demodulator based on a fully differential switched-gain amplifier. Using op amps with shutdown enables the gain switching between 0 and 1, which recovers the baseband signal in a similar way to a conventional +1/−1 switched gain amplifier, but with a 50% amplitude decrease in the demodulated signal. By using a power-down signal synchronized with the carrier, it is possible to program a 0° or 90° phase that enables in-phase and quadrature demodulation, ultimately allowing the measurement of complex impedances. Tests were performed in two different situations: static and time-varying impedances, and with two different op amp models, the OPA363 and the ADA4806-1. In the former test, several resistors and capacitors were measured, yielding deviations from a reference instrument below 0.5% for resistors and below 2.7% for capacitors when using the OPA363. In the latter test, the electrical bioimpedance changes of the hand-to-hand body segment of a number of healthy volunteers were recorded, enabling the detection of the respiratory and pulse rate.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-10"},"PeriodicalIF":1.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11105398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914441","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}
Atomic force microscopy (AFM) has emerged as a powerful tool for nanoscale imaging and quantitative characterization of organic (e.g., live cells, proteins, DNA, and lipid bilayers) and inorganic (e.g., silicon wafers and polymers) specimens. However, image artifacts in AFM height and peak force error images directly affect the precision of nanomechanical measurements. Experimentalists face considerable challenges in obtaining high-quality AFM images due to the requirement of specialized expertise and constant manual monitoring. Another challenge is the lack of high-quality AFM datasets to train machine learning models for automated defect detection. In this work, we propose a two-step AI framework that combines a vision-based deep learning (DL) model for classifying AFM image defects with a large language model (LLM)-based conversational assistant that provides real-time corrective guidance in natural language, making it particularly valuable for non-AFM experts aiming to obtain high-quality images. We curated an annotated AFM defect dataset spanning organic and inorganic samples to train the defect detection model. Our defect classification model achieves 91.43% overall accuracy, with a recall of 93% for tip contamination and 60% not-tracking defects. We further develop an intuitive user interface that enables seamless interaction with the DL model and integrates an LLM-based guidance feature to support users in understanding defects and improving future experiments. We then evaluate the performance of multiple state-of-the-art LLMs on AFM-related queries, offering users flexibility in LLM selection based on their specific needs. LLM evaluations and the benchmark questions are available at: https://github.com/idealab-isu/AFM-LLM-Defect-Guidance.
{"title":"Conversational LLM-Based Decision Support for Defect Classification in AFM Images","authors":"Angona Biswas;Jaydeep Rade;Nabila Masud;Md Hasibul Hasan Hasib;Aditya Balu;Juntao Zhang;Soumik Sarkar;Adarsh Krishnamurthy;Juan Ren;Anwesha Sarkar","doi":"10.1109/OJIM.2025.3592284","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3592284","url":null,"abstract":"Atomic force microscopy (AFM) has emerged as a powerful tool for nanoscale imaging and quantitative characterization of organic (e.g., live cells, proteins, DNA, and lipid bilayers) and inorganic (e.g., silicon wafers and polymers) specimens. However, image artifacts in AFM height and peak force error images directly affect the precision of nanomechanical measurements. Experimentalists face considerable challenges in obtaining high-quality AFM images due to the requirement of specialized expertise and constant manual monitoring. Another challenge is the lack of high-quality AFM datasets to train machine learning models for automated defect detection. In this work, we propose a two-step AI framework that combines a vision-based deep learning (DL) model for classifying AFM image defects with a large language model (LLM)-based conversational assistant that provides real-time corrective guidance in natural language, making it particularly valuable for non-AFM experts aiming to obtain high-quality images. We curated an annotated AFM defect dataset spanning organic and inorganic samples to train the defect detection model. Our defect classification model achieves 91.43% overall accuracy, with a recall of 93% for tip contamination and 60% not-tracking defects. We further develop an intuitive user interface that enables seamless interaction with the DL model and integrates an LLM-based guidance feature to support users in understanding defects and improving future experiments. We then evaluate the performance of multiple state-of-the-art LLMs on AFM-related queries, offering users flexibility in LLM selection based on their specific needs. LLM evaluations and the benchmark questions are available at: <uri>https://github.com/idealab-isu/AFM-LLM-Defect-Guidance</uri>.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-12"},"PeriodicalIF":1.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11096088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880632","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 : 2025-07-24DOI: 10.1109/OJIM.2025.3592283
Reza Zoughi;Jayaram Kizhekke Pakkathillam;Jason G. Xie
additive manufacturing (AM) or 3-D printing is the process of rapidly manufacturing complex parts that are used in a wide range of applications encompassing nearly unlimited types of critical and noncritical components. When considering metal AM, one of the more prominent processes involves layer-by-layer melting of fine metal powder into the desired part geometry, using an electron or a laser beam. The latter is referred to as the laser powder bed fusion (LPBF). The quality of the final printed part is directly impacted by the properties of the feedstock powder. This includes but is not limited to the metal powder size distribution, surface condition (i.e., oxidation), new or recycled powder, powder distribution surface nonuniformities, and streaks. The ability to determine metal powder properties prior to melting provides significant manufacturing quality control capability. Millimeter-wave nondestructive evaluation (NDE) techniques, spanning a frequency range of 30–300 GHz, offer several advantageous features for this purpose. These methods are noncontact, provide a high degree of measurement sensitivity to the metal powder properties of interest, and can provide real-time information. In addition, the reflection properties of the powder are the result of complex electromagnetic interactions among the powder particles and the irradiating wave. This article provides the results of a comprehensive investigation into the millimeter-wave reflection properties of several different types of metal powder at 32–40 GHz. The results demonstrate the ability to distinguish among metal powder types as a function of size distribution, powder stratification, alloy composition, recycled versus new and compacted powder using an open-ended circular waveguide probe, operating in its $TE_{01}$ mode.
{"title":"Millimeter-Wave Reflectometry for Distinction Among Critical Metal Powder Properties Used in Additive Manufacturing (AM)","authors":"Reza Zoughi;Jayaram Kizhekke Pakkathillam;Jason G. Xie","doi":"10.1109/OJIM.2025.3592283","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3592283","url":null,"abstract":"additive manufacturing (AM) or 3-D printing is the process of rapidly manufacturing complex parts that are used in a wide range of applications encompassing nearly unlimited types of critical and noncritical components. When considering metal AM, one of the more prominent processes involves layer-by-layer melting of fine metal powder into the desired part geometry, using an electron or a laser beam. The latter is referred to as the laser powder bed fusion (LPBF). The quality of the final printed part is directly impacted by the properties of the feedstock powder. This includes but is not limited to the metal powder size distribution, surface condition (i.e., oxidation), new or recycled powder, powder distribution surface nonuniformities, and streaks. The ability to determine metal powder properties prior to melting provides significant manufacturing quality control capability. Millimeter-wave nondestructive evaluation (NDE) techniques, spanning a frequency range of 30–300 GHz, offer several advantageous features for this purpose. These methods are noncontact, provide a high degree of measurement sensitivity to the metal powder properties of interest, and can provide real-time information. In addition, the reflection properties of the powder are the result of complex electromagnetic interactions among the powder particles and the irradiating wave. This article provides the results of a comprehensive investigation into the millimeter-wave reflection properties of several different types of metal powder at 32–40 GHz. The results demonstrate the ability to distinguish among metal powder types as a function of size distribution, powder stratification, alloy composition, recycled versus new and compacted powder using an open-ended circular waveguide probe, operating in its <inline-formula> <tex-math>$TE_{01}$ </tex-math></inline-formula> mode.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-12"},"PeriodicalIF":1.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11096082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007850","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 study presents an efficient attention-guided dual-knowledge distillation (D-KD) framework for classifying Alzheimer’s disease (AD) stages using magnetic resonance imaging (MRI) scans based on detection of the subtle anatomical differences. Current challenges involve identifying precise discriminating features in low computational complexity without compromising classification accuracy. In this work, a dual-teacher model consisting of vision transformer (ViT) and swin transformer (ST) for capturing global and local features, respectively, is utilized to distill comprehensive knowledge into a lightweight ViT-based student model, ensuring accurate classification efficacy with reduced computational demands. For validation of the proposed idea, two well-known benchmark MRI datasets, Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AIBL, have been considered for multiclass classification, using an online-training knowledge distillation approach, where teacher and student networks are trained concurrently. The proposed model has achieved accuracies (Ac) up to 98.24% and 97.07% on ADNI and AIBL, respectively, with a significant performance improvement of 15.6% with respect to existing works. The analysis shows that by leveraging the complementary strengths of ViT and ST, the D-KD strategy enhances generalization in data-limited scenarios and provides a reliable, resource-efficient solution for MRI-based AD diagnosis.
{"title":"Attention-Based Dual-Knowledge Distillation for Alzheimer’s Disease Stage Detection Using MRI Scans","authors":"Chandita Barman;Sudhanshu Singh;Manob Jyoti Saikia;Shovan Barma","doi":"10.1109/OJIM.2025.3589698","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3589698","url":null,"abstract":"This study presents an efficient attention-guided dual-knowledge distillation (D-KD) framework for classifying Alzheimer’s disease (AD) stages using magnetic resonance imaging (MRI) scans based on detection of the subtle anatomical differences. Current challenges involve identifying precise discriminating features in low computational complexity without compromising classification accuracy. In this work, a dual-teacher model consisting of vision transformer (ViT) and swin transformer (ST) for capturing global and local features, respectively, is utilized to distill comprehensive knowledge into a lightweight ViT-based student model, ensuring accurate classification efficacy with reduced computational demands. For validation of the proposed idea, two well-known benchmark MRI datasets, Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AIBL, have been considered for multiclass classification, using an online-training knowledge distillation approach, where teacher and student networks are trained concurrently. The proposed model has achieved accuracies (Ac) up to 98.24% and 97.07% on ADNI and AIBL, respectively, with a significant performance improvement of 15.6% with respect to existing works. The analysis shows that by leveraging the complementary strengths of ViT and ST, the D-KD strategy enhances generalization in data-limited scenarios and provides a reliable, resource-efficient solution for MRI-based AD diagnosis.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-10"},"PeriodicalIF":1.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082332","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891193","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}
Motion-induced eddy current (MIEC) testing method has been successfully applied in speed measurement, where the performance of the sensor is highly dependent on the dimensions of the permanent magnet. In this article, an optimization method based on the gradient projection algorithm is proposed to determine the optimal magnet dimensions and enhance measurement sensitivity. First, the principle of MIEC is analyzed based on a 2-D theoretical model. Subsequently, an optimization-based mathematical model employing the gradient projection algorithm is developed, and the corresponding optimized results are obtained. Finally, experimental validation is conducted using a rotating aluminum disk platform. The results indicate that, within the practical dimensional ranges of 40 mm in width and 30 mm in height, the optimized permanent magnet dimensions are with a width of 27.6 mm and a height of 30 mm, which yield the best measurement sensitivity.
{"title":"Optimization of Magnet Dimension for MIEC Speed Sensor Using Gradient Projection Algorithm","authors":"Yiru Xiao;Jikai Zhang;Yaoting Han;Yanting Chen;Kangxuan Deng;Grzegorz Tytko;Yihua Kang;Bo Feng","doi":"10.1109/OJIM.2025.3589696","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3589696","url":null,"abstract":"Motion-induced eddy current (MIEC) testing method has been successfully applied in speed measurement, where the performance of the sensor is highly dependent on the dimensions of the permanent magnet. In this article, an optimization method based on the gradient projection algorithm is proposed to determine the optimal magnet dimensions and enhance measurement sensitivity. First, the principle of MIEC is analyzed based on a 2-D theoretical model. Subsequently, an optimization-based mathematical model employing the gradient projection algorithm is developed, and the corresponding optimized results are obtained. Finally, experimental validation is conducted using a rotating aluminum disk platform. The results indicate that, within the practical dimensional ranges of 40 mm in width and 30 mm in height, the optimized permanent magnet dimensions are with a width of 27.6 mm and a height of 30 mm, which yield the best measurement sensitivity.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-7"},"PeriodicalIF":1.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810707","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 : 2025-07-16DOI: 10.1109/OJIM.2025.3589706
Md. Azahar Ali;Brittany R. Howell;Liqing Zhang
Livestock health monitoring stands as a linchpin in ensuring both the welfare of animals and the optimization of productivity. As we navigate toward meeting current and future food crises, the role of biosensors in this context cannot be overstated. Such biosensors serve as indispensable tools, offering real-time insights into the health status of livestock, thereby enabling early detection of diseases and prompt intervention. In addressing the challenges and potential of biosensors for livestock sensing, it is clear that while biosensors have seen extensive use in human health monitoring, their application in livestock is crucial for ensuring animal well-being and productivity, vital in meeting global food demands. To maximize effectiveness, there is a need for advanced manufacturing to develop customized, user-friendly, and cost-effective sensors. By harnessing the synergistic potential of electrochemical biosensors and advanced manufacturing, this review discusses the challenges that currently impede the widespread adoption of wearable electrochemical biosensors, advanced manufacturing techniques, and artificial intelligence in livestock sensing. This strategic approach not only bolsters animal welfare and productivity but also fortifies agricultural resilience in the face of evolving global food demands. This review highlights recent advancements in biosensors for livestock monitoring.
{"title":"3D-Printed Wearable Biosensors for Livestock Health Monitoring","authors":"Md. Azahar Ali;Brittany R. Howell;Liqing Zhang","doi":"10.1109/OJIM.2025.3589706","DOIUrl":"https://doi.org/10.1109/OJIM.2025.3589706","url":null,"abstract":"Livestock health monitoring stands as a linchpin in ensuring both the welfare of animals and the optimization of productivity. As we navigate toward meeting current and future food crises, the role of biosensors in this context cannot be overstated. Such biosensors serve as indispensable tools, offering real-time insights into the health status of livestock, thereby enabling early detection of diseases and prompt intervention. In addressing the challenges and potential of biosensors for livestock sensing, it is clear that while biosensors have seen extensive use in human health monitoring, their application in livestock is crucial for ensuring animal well-being and productivity, vital in meeting global food demands. To maximize effectiveness, there is a need for advanced manufacturing to develop customized, user-friendly, and cost-effective sensors. By harnessing the synergistic potential of electrochemical biosensors and advanced manufacturing, this review discusses the challenges that currently impede the widespread adoption of wearable electrochemical biosensors, advanced manufacturing techniques, and artificial intelligence in livestock sensing. This strategic approach not only bolsters animal welfare and productivity but also fortifies agricultural resilience in the face of evolving global food demands. This review highlights recent advancements in biosensors for livestock monitoring.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"4 ","pages":"1-9"},"PeriodicalIF":1.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917243","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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