IEEE Open Journal of Instrumentation and Measurement最新文献

{"title":"Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework","authors":"Basab Bijoy Purkayastha;Shovan Barma","doi":"10.1109/OJIM.2023.3332344","DOIUrl":"10.1109/OJIM.2023.3332344","url":null,"abstract":"This study proposes a multiclass classification technique based on multifractal spectra for different types of cardiac arrhythmias which are associated with irregularity and/or complex dynamics of the heart. Indeed, the degree of complexity of such dynamics is diverse for different states of cardiac condition. Certainly, such physiological responses of the heart dynamics can be discriminated by analyzing electrocardiogram (ECG) signals through different channels. Earlier, ECG-based works for discriminating cardiac arrhythmias consider the heart as a black box system and the analysis is mostly surrounded with time domain statistical averages or spectral analysis. The works ignore one of the key parameters, i.e., the presence of time-localized irregularities which are strongly associated with different kinds of arrhythmias and contribute to subtle variations in the amplitude and shape of the signal dynamical system while analyzing the signal. Therefore, in this work, we proposed a new method based on multifractal analysis to classify different kinds of cardiac conditions. Here, we followed the dynamical systems approach and computed the multifractal spectrum of the embedded phase space structure of the ECG signal. We performed the classification task by an echo state network to reduce the computational burden. For validation, three well-known datasets (Shaoxing Peoples’ Hospital dataset, PTB diagnostic ECG database v1.0.0, and 2017 PhysioNet/CinC Challenge dataset) have been considered. The results and analysis show that the proposed method can achieve a maximum accuracy of up to 96%, which is significantly high. Further, an optimum number of channels/leads has also been evaluated in multichannel ECG analysis. The result and analysis reveal that the effectiveness of the model in classifying various categories of cardiac disorders from ECG.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10317876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135662210","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}

{"title":"The Impact of Cross-Talk in a Flat Panel Detector on CT Image Quality","authors":"Seemeen Karimi;James Hall;Jerel A. Smith;Joseph Tringe","doi":"10.1109/OJIM.2023.3332342","DOIUrl":"10.1109/OJIM.2023.3332342","url":null,"abstract":"Spatial resolution and image noise are two aspects of image quality of an X-ray computed tomography (CT) system and are determined by the X-ray source, the detector, and mathematical operations for image reconstruction. In CT scanners with flat panel detectors (FPDs), there is cross-talk (signal leakage) between detector pixels. The contribution of the cross-talk to spatial resolution and noise in reconstructed images has not been adequately modeled. Previously, we estimated cross-talk from autocovariance measurements in air, and modeled the impact of cross-talk on spatial resolution. We have extended that work to calculate the impact of cross-talk on signal-to-noise ratio in radiographs and to reconstructed image noise. We modeled the spatial resolution and noise of a CT scanner that uses a flat-panel detector with 0.2-mm pixels and a gadolinium oxysulfide scintillator, and a 450 kVp, dual-focus X-ray tube. Our noise model agrees with measurements from experimental data and simulations to within 10%. We show that cross-talk in FPDs can reduce resolution by over 30%, reduce noise by approximately a factor of two, and introduce correlation in the noise, and therefore, cannot be disregarded when assessing CT image quality.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10317879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135662230","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}

{"title":"Cardiac Signature Detection and Study Using Contactless Technology: Millimeter-Wave FMCW Radar","authors":"Mélanie Brulc;Thibaut Deleruyelle;Alain Loussert;Pierre Laurent;Rémi Grisot;Jean-Paul Caruana","doi":"10.1109/OJIM.2023.3327483","DOIUrl":"10.1109/OJIM.2023.3327483","url":null,"abstract":"The work presented in this article aims to detect the cardiac movement of a person in a noninvasive way and correlate it with a reference signal in the medical field: the electrocardiogram. To achieve this goal, a measurement campaign was carried out on 20 consenting individuals. On the one hand, the mechanical signal, the movement of the subject’s chest induced by the heartbeat, is recorded via an FMCW radar, and on the other hand, the electrical signal of the heart is recorded via an ECG acquisition board. Signal processing functions and different filtering will allow the correlation of the radar and ECG signals. This study is conducted on apnea recordings in order to remove the impact of breathing on the movement of the chest. When the subject holds his or her breath, the two important phases of cardiac movement via radar capture can be detected: 1) the systole and 2) the diastole. The delay between the mechanical signal of the heart and the electrical signal of the heart, already explained by medicine, is well noted. The accuracy of motion detection provided by the radar allowed us to highlight the reproducibility of the chest movements detected during a capture. Their correlation with ECG data validates the proposed hypotheses.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10297997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135211744","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}

{"title":"Using Phased Array Ultrasound to Localize Probes During the Inspection of Welds","authors":"Adam Gilmour;Alexander Ulrichsen;William Jackson;Morteza Tabatabaeipour;Gordon Dobie;Charles N. Macleod;Paul Murray;Benjamin Karkera","doi":"10.1109/OJIM.2023.3327484","DOIUrl":"10.1109/OJIM.2023.3327484","url":null,"abstract":"In this article, an image processing-based localization system is developed for remote nondestructive evaluation of welds within industrial assets. Manual ultrasonic inspection of large-scale structures is often repetitive, time-consuming, and benefits greatly from robotic support, however, these robotic systems are often fixed to a single purpose, lack self-awareness of their surrounding environment, and can be limited to simple geometry. For the inspection of welds, which are often carried out using phased array ultrasonic testing, there is a reliance on the use of surface features for automated tracking such as the laser profiling of a weld cap. For the inspection of more complex geometry such as nonlinear or saddle welds, a more positionally sensitive method is required. The proposed system utilizes information already available to a nondestructive inspector in the form of live phased array ultrasonic images to estimate the location of the weld using nonsurface, volumetric data. Data is captured using a 64-element, 10-MHz phased array probe mounted to the end effector of a small robotic manipulator which increases the scope of applications due to its heightened flexibility when compared to on-the-market alternatives. Morphological operations are applied to the ultrasonic data to reduce the noise apparent from regions of parent material and promote the data reflected from grain boundaries within the weld material. Through a series of image processing techniques, it is possible to predict the position of a weld under inspection with an absolute mean positional error of \u0000<inline-formula> <tex-math>$mathrm {0.8 text {m} text { m} }$ </tex-math></inline-formula>\u0000. From this study, the localization system is to be embedded within a remote system for extensive data acquisition of welds on large structures.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10296089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134979743","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}

{"title":"Multi-Cell and Wide-Frequency In-Situ Battery Impedance Spectroscopy","authors":"Arne Sandschulte;Roberto Ferrero","doi":"10.1109/OJIM.2023.3322492","DOIUrl":"10.1109/OJIM.2023.3322492","url":null,"abstract":"The use of dc–dc converters for in-situ electrochemical impedance spectroscopy has been investigated by several works in recent years, with different implementation strategies and promising results. There are, however, two important limitations that still hinder a commercial application of this technique: first, the need to deal with the battery discharge during the measurement, particularly critical at very low frequencies; second, the difficulty of accurately measuring the small ac voltage response of several cells in a pack, with common-mode dc voltages that can be five (or more) orders of magnitude higher. This article addresses both challenges, from an instrumentation and measurement perspective, presenting a solution for impedance measurements down to 10 mHz, on a system composed of 16 lithium-iron-phosphate cells or modules connected in series. A dc–dc boost converter is used to inject a multisine current perturbation on all batteries, with closed-loop control, and all cell voltages are conditioned to optimize the measurement resolution and accuracy of their ac components. Suitable signal processing compensates for the voltage drift caused by the battery discharge, and evaluates the residual distortion in the signal, to assess the validity of the impedance estimate. Experimental tests confirm that the obtained results are sufficiently precise (or repeatable) to allow detecting impedance variations occurring during the battery discharge or after repeated charge/discharge cycles.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10273720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136008283","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}

{"title":"Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases","authors":"Tanmay Sinha Roy;Joyanta Kumar Roy;Nirupama Mandal","doi":"10.1109/OJIM.2023.3320765","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3320765","url":null,"abstract":"Cardiovascular diseases are growing rapidly in this world. Around 70% of the world’s population is suffering from the same. The entire research work is grouped into the classification and analysis of heart sound. We defined a new squeeze network-based deep learning model—convolutional random forest (RF) for real-time valvular heart sound classification and analysis using industrial Raspberry Pi 4B. The proposed electronic stethoscope is Internet enabled using ESP32, and Raspberry Pi. The said Internet of Things (IoT)-based model is also low cost, portable, and can be reachable to distant remote places where doctors are not available. As far as the classification part is concerned, the multiclass classification is done for seven types of valvular heart sounds. The RF classifier scored a good accuracy among other ensemble methods in small training set data. The CNN-based squeeze net model achieved a decent accuracy of 98.65% after its hyperparameters were optimized for heart sound analysis. The proposed IoT-based model overcomes the drawbacks faced individually in both squeeze network and RF. CNN-based squeeze net model and RF classifier combined together improved the performance of classification accuracy. The squeeze net model plays a pivotal part in the feature extraction of heart sound, and an RF classifier acts as a classifier in the class prediction layer for predicting class labels. Experimental results on several datasets like the Kaggle dataset, the Physio net challenge, and the Pascal Challenge showed that the Conv-RF model works the best. The proposed IoT-based Conv-RF model is also applied on the selected subjects with different age groups and genders having a history of heart diseases. The Conv-RF method scored an accuracy of 99.37 ± 0.05% on the different test datasets with a sensitivity of 99.5 ± 0.12% and specificity of 98.9 ± 0.03%. The proposed model is also examined with the current state-of-the-art models in terms of accuracy.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10268240.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417566","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}

{"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}

{"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}

{"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}

{"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}