Pub Date : 2023-09-25DOI: 10.1109/OJIM.2023.3318678
Bikshan Ghosh;Sharmistha Mandal
Estimating the parameters of solar photovoltaic (PV) panels is crucial for effectively managing operations in solar-based microgrids. Various techniques have been developed for this purpose, and one accurate approach is solar cell modeling using metaheuristic algorithms from current–voltage ( ${I}$ – ${V}$ ) data of the PV panel. However, this method relies on experimental datasets, which may not be readily available for most industrial PV panels. Hence, this research proposes a new technique for estimating the parameters of different types of PV modules using only manufacturer datasheets. Additionally, three metaheuristic optimization techniques, namely, particle swarm optimization (PSO), artificial bee colony (ABC) optimization, and Harris Hawks optimization (HHO), are investigated for solving this problem. The obtained results using these optimizers indicate that PSO mostly outperforms other algorithms, in terms of accuracy, while demonstrating faster computation. The proposed method is evaluated for three different PV units. Under 1000W/m2 of irradiance and a specified temperature, the method has been validated with available experimental datasets. Furthermore, a comparative analysis with some other existing methods in the literature reveals the model’s competitiveness despite not relying on experimental datasets. Also, an uncertainty analysis for the extracted parameters has shown that the obtained results are reliable enough to predict the actual dynamics of PV units. This study holds significance for other research on the basis of PV panel parameters, managing commercial PV power plant operation with with maximum power point tracking controller, etc.
{"title":"A New Approach for Solar Photovoltaic Parameter Extraction Using Metaheuristic Algorithms From Manufacturer Datasheet","authors":"Bikshan Ghosh;Sharmistha Mandal","doi":"10.1109/OJIM.2023.3318678","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3318678","url":null,"abstract":"Estimating the parameters of solar photovoltaic (PV) panels is crucial for effectively managing operations in solar-based microgrids. Various techniques have been developed for this purpose, and one accurate approach is solar cell modeling using metaheuristic algorithms from current–voltage (\u0000<inline-formula> <tex-math>${I}$ </tex-math></inline-formula>\u0000–\u0000<inline-formula> <tex-math>${V}$ </tex-math></inline-formula>\u0000) data of the PV panel. However, this method relies on experimental datasets, which may not be readily available for most industrial PV panels. Hence, this research proposes a new technique for estimating the parameters of different types of PV modules using only manufacturer datasheets. Additionally, three metaheuristic optimization techniques, namely, particle swarm optimization (PSO), artificial bee colony (ABC) optimization, and Harris Hawks optimization (HHO), are investigated for solving this problem. The obtained results using these optimizers indicate that PSO mostly outperforms other algorithms, in terms of accuracy, while demonstrating faster computation. The proposed method is evaluated for three different PV units. Under 1000W/m2 of irradiance and a specified temperature, the method has been validated with available experimental datasets. Furthermore, a comparative analysis with some other existing methods in the literature reveals the model’s competitiveness despite not relying on experimental datasets. Also, an uncertainty analysis for the extracted parameters has shown that the obtained results are reliable enough to predict the actual dynamics of PV units. This study holds significance for other research on the basis of PV panel parameters, managing commercial PV power plant operation with with maximum power point tracking controller, etc.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10261504.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50416435","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 : 2023-09-14DOI: 10.1109/OJIM.2023.3315349
Ziad Hatab;Michael Ernst Gadringer;Ahmad Bader Alothman Alterkawi;Wolfgang Bösch
This article presents a new technique for evaluating the consistency of the reference impedance in multiline thru–reflect–line (TRL) calibration. During the calibration process, it is assumed that all transmission line standards have the same characteristic impedance. However, these assumptions are prone to errors due to imperfections, which can affect the validity of the reference impedance after calibration. Our proposed method involves using multiple stepped impedance lines of different lengths to extract the broadband reflection coefficient of the impedance transition. This reflection coefficient can be used to validate the reference impedance experimentally without requiring fully defined standards. We demonstrate this method using multiline TRL based on microstrip structures on a printed circuit board (PCB) with an on-wafer probing setup.
{"title":"Validation of the Reference Impedance in Multiline Calibration With Stepped Impedance Standards","authors":"Ziad Hatab;Michael Ernst Gadringer;Ahmad Bader Alothman Alterkawi;Wolfgang Bösch","doi":"10.1109/OJIM.2023.3315349","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3315349","url":null,"abstract":"This article presents a new technique for evaluating the consistency of the reference impedance in multiline thru–reflect–line (TRL) calibration. During the calibration process, it is assumed that all transmission line standards have the same characteristic impedance. However, these assumptions are prone to errors due to imperfections, which can affect the validity of the reference impedance after calibration. Our proposed method involves using multiple stepped impedance lines of different lengths to extract the broadband reflection coefficient of the impedance transition. This reflection coefficient can be used to validate the reference impedance experimentally without requiring fully defined standards. We demonstrate this method using multiline TRL based on microstrip structures on a printed circuit board (PCB) with an on-wafer probing setup.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10251578.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417563","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 : 2023-09-01DOI: 10.1109/OJIM.2023.3311049
Marco Carratù;Salvatore Dello Iacono;Vincenzo Paciello;Antonio Espírito-Santo;Gustavo Monte
This article introduces a novel smart sensor that employs an advanced algorithm for earthquake early warning (EEW). The sensor utilizes a smart sampling technique to extract significant signal information, simplifying the process of inferring knowledge. The main objective is to assess the potential destructiveness of an incoming earthquake by analyzing the initial moments of the pressure wave and to generate an alert for prompt action, if necessary. This study includes the development and presentation of the proposed method, as well as performance evaluations using real seismic data obtained from freely accessible databases. These evaluations confirm the effectiveness of the proposed method in accurately estimating earthquake magnitudes. Furthermore, this article includes a comparison with a widely used EEW algorithm. The real-time functionality and interoperability of devices are crucial considerations in earthquake detection applications. The suitability and compatibility of the proposed method with the IEEE1451 family of standards are demonstrated and emphasized in this article.
{"title":"An IEEE21451-001 Compliant Smart Sensor for Early Earthquake Detection","authors":"Marco Carratù;Salvatore Dello Iacono;Vincenzo Paciello;Antonio Espírito-Santo;Gustavo Monte","doi":"10.1109/OJIM.2023.3311049","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3311049","url":null,"abstract":"This article introduces a novel smart sensor that employs an advanced algorithm for earthquake early warning (EEW). The sensor utilizes a smart sampling technique to extract significant signal information, simplifying the process of inferring knowledge. The main objective is to assess the potential destructiveness of an incoming earthquake by analyzing the initial moments of the pressure wave and to generate an alert for prompt action, if necessary. This study includes the development and presentation of the proposed method, as well as performance evaluations using real seismic data obtained from freely accessible databases. These evaluations confirm the effectiveness of the proposed method in accurately estimating earthquake magnitudes. Furthermore, this article includes a comparison with a widely used EEW algorithm. The real-time functionality and interoperability of devices are crucial considerations in earthquake detection applications. The suitability and compatibility of the proposed method with the IEEE1451 family of standards are demonstrated and emphasized in this article.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10237301.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50416391","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 : 2023-09-01DOI: 10.1109/OJIM.2023.3311053
Huaizheng Mu;Liangqi Yuan;Jia Li
Human sensing is significantly improving our lifestyle in many fields, such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This article proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed sensing humans among PRF (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental findings demonstrate that the SHAPR system, in conjunction with the random forest (RFR) algorithm, achieves human authentication accuracies of 95.6% and 98.7% in laboratory and living room scenarios, respectively. In a vehicular setting, grid-level localization accuracy reaches 99.1%, and in a laboratory environment, activity recognition accuracy is attained at 99.1%. Moreover, within a classroom scenario, the SHAPR system, when integrated with the Gaussian process regression (GPR) model, can realize coordinate-level localization with an error margin of merely 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability.
{"title":"Human Sensing via Passive Spectrum Monitoring","authors":"Huaizheng Mu;Liangqi Yuan;Jia Li","doi":"10.1109/OJIM.2023.3311053","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3311053","url":null,"abstract":"Human sensing is significantly improving our lifestyle in many fields, such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This article proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed sensing humans among PRF (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental findings demonstrate that the SHAPR system, in conjunction with the random forest (RFR) algorithm, achieves human authentication accuracies of 95.6% and 98.7% in laboratory and living room scenarios, respectively. In a vehicular setting, grid-level localization accuracy reaches 99.1%, and in a laboratory environment, activity recognition accuracy is attained at 99.1%. Moreover, within a classroom scenario, the SHAPR system, when integrated with the Gaussian process regression (GPR) model, can realize coordinate-level localization with an error margin of merely 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10237316.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417567","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 : 2023-08-22DOI: 10.1109/OJIM.2023.3303944
M. Shamim Hossain;Mohammad Shorfuzzaman
The current pandemic has necessitated rapid and automatic detection of coronavirus disease (COVID-19) infections. Various artificial intelligence functionalities coupled with biomedical images can be utilized to efficiently detect these infections and recommend a prompt response (curative intervention) to limit the virus’s spread. In particular, biomedical imaging could help to visualize the internal organs of the human body and disorders that affect them. One of them is chest X-rays (CXRs) which has widely been used for preventive medicine or disease screening. However, when it comes to detecting COVID-19 from CXR images, most of the approaches rely on standard image classification algorithms, which have limitations with low identification accuracy and improper extraction of key features. As a result, a convolutional neural network (CNN)-based fusion network has been developed for automated COVID-19 screening in this study. First, using attention networks and multiple fine-tuned CNN models, we extract key features that are resistant to overfitting. We then employ a locally connected layer to create a weighted combination of these models for final COVID-19 detection. Using a publicly available dataset of CXR images from healthy subjects as well as COVID-19 and pneumonia cases, we evaluated the predictive capabilities of our proposed model. Test results demonstrate that the proposed fusion model performs favorably compared to individual CNN models.
{"title":"Noninvasive COVID-19 Screening Using Deep-Learning-Based Multilevel Fusion Model With an Attention Mechanism","authors":"M. Shamim Hossain;Mohammad Shorfuzzaman","doi":"10.1109/OJIM.2023.3303944","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3303944","url":null,"abstract":"The current pandemic has necessitated rapid and automatic detection of coronavirus disease (COVID-19) infections. Various artificial intelligence functionalities coupled with biomedical images can be utilized to efficiently detect these infections and recommend a prompt response (curative intervention) to limit the virus’s spread. In particular, biomedical imaging could help to visualize the internal organs of the human body and disorders that affect them. One of them is chest X-rays (CXRs) which has widely been used for preventive medicine or disease screening. However, when it comes to detecting COVID-19 from CXR images, most of the approaches rely on standard image classification algorithms, which have limitations with low identification accuracy and improper extraction of key features. As a result, a convolutional neural network (CNN)-based fusion network has been developed for automated COVID-19 screening in this study. First, using attention networks and multiple fine-tuned CNN models, we extract key features that are resistant to overfitting. We then employ a locally connected layer to create a weighted combination of these models for final COVID-19 detection. Using a publicly available dataset of CXR images from healthy subjects as well as COVID-19 and pneumonia cases, we evaluated the predictive capabilities of our proposed model. Test results demonstrate that the proposed fusion model performs favorably compared to individual CNN models.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10226595.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50416388","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 : 2023-08-16DOI: 10.1109/OJIM.2023.3305658
Zili Zhang;Ziqi Chen;Jianxin Xu;Wuliang Yin
Electromagnetic tomography (EMT), also known as magnetic inductance tomography (MIT) is a tomographic modality widely employed in process industry and biomedical applications. In particular, this technique plays an important role in imaging metallic objects since it can produce conductivity and permeability distributions in the region of interest. An EMT system consists of a coil array, a data acquisition system, and an imaging reconstruction computer. Coils are used to generate electromagnetic field which interacts with the objects under investigation and measure the induced voltages. Conventionally, coils with sufficient inductance coupling (considerable number of turns or dimensions) are used to achieve high sensitivity and good SNR performance. However, this poses limitations for some applications, such as high-temperature applications and small-scale facilities. In high-temperature applications such as in steel or copper production processes, coils of the large number of turns are more likely to be damaged due to the breakdown of insulating materials between the turns, resulting in measuring errors. Besides, EMT applied in small-scale facility requires sensors with reduced dimensions, which results in weak magnetic coupling and lower SNR. In order to address these issues, this article proposes a method to transform the impedance and hence increase the sensor signal level through designing boosting transformers. Simulation and experimental results suggest that this increases the system SNR and image stability.
{"title":"Improving SNR and Sensitivity for Low-Coupling EMT Sensors","authors":"Zili Zhang;Ziqi Chen;Jianxin Xu;Wuliang Yin","doi":"10.1109/OJIM.2023.3305658","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3305658","url":null,"abstract":"Electromagnetic tomography (EMT), also known as magnetic inductance tomography (MIT) is a tomographic modality widely employed in process industry and biomedical applications. In particular, this technique plays an important role in imaging metallic objects since it can produce conductivity and permeability distributions in the region of interest. An EMT system consists of a coil array, a data acquisition system, and an imaging reconstruction computer. Coils are used to generate electromagnetic field which interacts with the objects under investigation and measure the induced voltages. Conventionally, coils with sufficient inductance coupling (considerable number of turns or dimensions) are used to achieve high sensitivity and good SNR performance. However, this poses limitations for some applications, such as high-temperature applications and small-scale facilities. In high-temperature applications such as in steel or copper production processes, coils of the large number of turns are more likely to be damaged due to the breakdown of insulating materials between the turns, resulting in measuring errors. Besides, EMT applied in small-scale facility requires sensors with reduced dimensions, which results in weak magnetic coupling and lower SNR. In order to address these issues, this article proposes a method to transform the impedance and hence increase the sensor signal level through designing boosting transformers. Simulation and experimental results suggest that this increases the system SNR and image stability.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10221720.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417315","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 : 2023-08-10DOI: 10.1109/OJIM.2023.3303950
Jordi Solé-Lloveras;Marco A. Azpúrua;Yasutoshi Yoshioka;Ferran Silva
Performing in-situ radiated emissions measurements, that is, in locations different from a standard test site, can be a challenging task because of the high electromagnetic noise levels in the ambient. A harsh electromagnetic environment characterizes such sites, and it usually results in difficulties when discerning between emissions from the equipment under test (EUT) and electromagnetic fields generated by surrounding devices. Moreover, communication signals from broadcasting services are generally significantly higher than the standard emission limits, making it even harder to determine compliance. In this article, we present different techniques leveraging the advantages of time-domain measurement systems to provide effective and practical solutions to mitigate ambient noise’s effect on radiated electromagnetic interference measurements. First, the test method used is described, and pragmatic considerations are given to ensure reliable and repeatable measurements. Multichannel time-domain measurement systems are introduced as the fundamental tool for the proposed strategies. Subsequently, different study cases are evaluated with real test examples, highlighting several criteria intended to reduce the impact of ambient noise on the actual emissions measures produced by the EUT. Finally, a real application of those strategies for measuring a photovoltaic system is described. Overall, the methods employed and the main advantages of using full-time-domain FFT-based receivers are reviewed. In addition, the possibility of incorporating this article’s outcomes into forthcoming electromagnetic standards about in-situ radiated emission measurements is also debated.
{"title":"Strategies Using Time-Domain Measurements for Radiated Emissions Testing in Harsh Environments","authors":"Jordi Solé-Lloveras;Marco A. Azpúrua;Yasutoshi Yoshioka;Ferran Silva","doi":"10.1109/OJIM.2023.3303950","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3303950","url":null,"abstract":"Performing in-situ radiated emissions measurements, that is, in locations different from a standard test site, can be a challenging task because of the high electromagnetic noise levels in the ambient. A harsh electromagnetic environment characterizes such sites, and it usually results in difficulties when discerning between emissions from the equipment under test (EUT) and electromagnetic fields generated by surrounding devices. Moreover, communication signals from broadcasting services are generally significantly higher than the standard emission limits, making it even harder to determine compliance. In this article, we present different techniques leveraging the advantages of time-domain measurement systems to provide effective and practical solutions to mitigate ambient noise’s effect on radiated electromagnetic interference measurements. First, the test method used is described, and pragmatic considerations are given to ensure reliable and repeatable measurements. Multichannel time-domain measurement systems are introduced as the fundamental tool for the proposed strategies. Subsequently, different study cases are evaluated with real test examples, highlighting several criteria intended to reduce the impact of ambient noise on the actual emissions measures produced by the EUT. Finally, a real application of those strategies for measuring a photovoltaic system is described. Overall, the methods employed and the main advantages of using full-time-domain FFT-based receivers are reviewed. In addition, the possibility of incorporating this article’s outcomes into forthcoming electromagnetic standards about in-situ radiated emission measurements is also debated.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10214353.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417561","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 : 2023-08-10DOI: 10.1109/OJIM.2023.3303948
Rahul Dasharath Gavas;Soumya Kanti Ghosh;Arpan Pal
Denoising of interferograms is a vital step in the processing of synthetic aperture radar (InSAR) data. The primary goal is to filter the noise to the extent possible while retaining the fringes of the interferograms. Among the widely available classes of filters, the frequency-domain filters are still being used, owing to their robustness and generalizability to varying phase noise characteristics. This article deals with an enhancement to the well-known frequency-domain filter, i.e., the Goldstein filter, which is basically a phase filtering algorithm for interferometric products. The proposed extension to the Goldstein filter deals with deriving the tuning parameter based on the spatial frequency modes. This is achieved by using the mode-level characteristics rendered by the 2-D version of variational mode decomposition (2D-VMD) on the interferograms under test. The results of simulation and real interferogram data show that the proposed approach reduces the noise levels while minimizing the loss of signal.
{"title":"Enhanced Goldstein Filter for Interferometric Phase Denoising Using 2-D Variational Mode Decomposition","authors":"Rahul Dasharath Gavas;Soumya Kanti Ghosh;Arpan Pal","doi":"10.1109/OJIM.2023.3303948","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3303948","url":null,"abstract":"Denoising of interferograms is a vital step in the processing of synthetic aperture radar (InSAR) data. The primary goal is to filter the noise to the extent possible while retaining the fringes of the interferograms. Among the widely available classes of filters, the frequency-domain filters are still being used, owing to their robustness and generalizability to varying phase noise characteristics. This article deals with an enhancement to the well-known frequency-domain filter, i.e., the Goldstein filter, which is basically a phase filtering algorithm for interferometric products. The proposed extension to the Goldstein filter deals with deriving the tuning parameter based on the spatial frequency modes. This is achieved by using the mode-level characteristics rendered by the 2-D version of variational mode decomposition (2D-VMD) on the interferograms under test. The results of simulation and real interferogram data show that the proposed approach reduces the noise levels while minimizing the loss of signal.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10214398.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50416436","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 : 2023-08-08DOI: 10.1109/OJIM.2023.3302908
Ankita Dey;Ebrahim Ali;Sreeraman Rajan
With an increase in the number of breast cancer cases worldwide, there is an urgent need to develop techniques for early abnormality detection. Thermography is known for its potential to detect breast abnormalities at an early stage. A novel threshold-based non-machine learning asymmetry analysis using textural features is proposed for breast abnormality detection. Breast abnormalities are indicated by regions of elevated temperatures (hot regions), usually, indicated by red color in thermograms. In this work, the breast thermograms are segmented to extract breast tissue profiles and then the red-plane of an RGB thermogram is utilized to analyze the natural contralateral symmetry between the left and right breast of an individual. A novel textural feature based on histogram similarity along with known textural features, such as fractal dimension, hurst exponent, spectral norm, and Frobenius norm, are used as features for asymmetry analysis. Bilateral ratios (BRs) of these features indicate contralateral symmetry between the left and right breast. A BR value closer to 1 indicates such symmetry. Hard voting is done among all the BRs of the textural features to estimate asymmetry between the left and right breast and detect an individual with breast abnormality. The proposed methodology is evaluated on publicly available datasets. It outperforms the state-of-the-art and achieves an accuracy of 96.08%, sensitivity of 100%, and specificity of 93.57%. A comparative analysis of statistical and textural features has also been demonstrated. A novel singular value decomposition (SVD)-based abnormal breast detection technique has been proposed with evaluations on a limited dataset.
{"title":"Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions","authors":"Ankita Dey;Ebrahim Ali;Sreeraman Rajan","doi":"10.1109/OJIM.2023.3302908","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3302908","url":null,"abstract":"With an increase in the number of breast cancer cases worldwide, there is an urgent need to develop techniques for early abnormality detection. Thermography is known for its potential to detect breast abnormalities at an early stage. A novel threshold-based non-machine learning asymmetry analysis using textural features is proposed for breast abnormality detection. Breast abnormalities are indicated by regions of elevated temperatures (hot regions), usually, indicated by red color in thermograms. In this work, the breast thermograms are segmented to extract breast tissue profiles and then the red-plane of an RGB thermogram is utilized to analyze the natural contralateral symmetry between the left and right breast of an individual. A novel textural feature based on histogram similarity along with known textural features, such as fractal dimension, hurst exponent, spectral norm, and Frobenius norm, are used as features for asymmetry analysis. Bilateral ratios (BRs) of these features indicate contralateral symmetry between the left and right breast. A BR value closer to 1 indicates such symmetry. Hard voting is done among all the BRs of the textural features to estimate asymmetry between the left and right breast and detect an individual with breast abnormality. The proposed methodology is evaluated on publicly available datasets. It outperforms the state-of-the-art and achieves an accuracy of 96.08%, sensitivity of 100%, and specificity of 93.57%. A comparative analysis of statistical and textural features has also been demonstrated. A novel singular value decomposition (SVD)-based abnormal breast detection technique has been proposed with evaluations on a limited dataset.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10210667.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417430","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 : 2023-08-04DOI: 10.1109/OJIM.2023.3301905
Brett S. Sicard;Quade Butler;Youssef Ziada;Ethan Hughey;Stephen A. Gadsden
Ball screw preload is an important factor in maintaining repeatably, rigidity, and in reducing or eliminating backlash in feed drive systems. Ball screw feeds drives are used in computer numerical control (CNC) machine tools to manufacture high-quality, precision parts. Many fault detection and condition monitoring (CM) methods have been proposed for measuring and detecting loss of preload, however, most of these methods require external sensors. Ideally, sensors, measurements, and methods integral to a CNC machine tool could be used to eliminate the extra cost and complexity of external sensors. A sensor-less method of estimating levels of preload using the mode probability of interacting multiple models (IMMs) is proposed. This method calculates a weighted sum which utilizes the mode probability of models representing different levels of preload, along with an activation function and weighing factor, to estimate the current level of preload. Unlike many other methods used for detecting levels of preload, this method requires only a system model and data collected by the CNC systems, while requiring no external sensors. The proposed method was shown to be robust and able to accurately and quickly predict preload levels under many different testing conditions. This method demonstrated a high degree of prediction accuracy (95%) which is comparable to, or better than other methods in the literature. In addition to being a novel method for preload detection, this work is also a novel implementation of IMM for fault detection, as it has not yet been applied to fault detection in feed drives.
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