Daniel Corregidor, I. Masmitja, Juan-Manuel López-Navarro, S. Gomáriz, J. Navarro, G. Arcas
{"title":"Analysis and initial design of bidirectional acoustic tag modulation schemes and communication protocol","authors":"Daniel Corregidor, I. Masmitja, Juan-Manuel López-Navarro, S. Gomáriz, J. Navarro, G. Arcas","doi":"10.1109/I2MTC50364.2021.9460108","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9460108","url":null,"abstract":"Acoustic underwater tags are key devices to study marine animals and obtain information regarding their behaviour. This information is essential to increase our knowledge of oceanic species and implement efficient conservation policies. At present, all the acoustic tags have a unidirectional communication protocol, which introduces important limitations for their localization such as range measurement, and in situ reconfiguration. To solve these issues and improve the current state-of-the-art of acoustic tags, a new bidirectional tag device has been developed. This new tag will allow new studies and will open a wide tracking capability by using autonomous underwater vehicles and range-based algorithms. The main characteristics of the tag communications scheme such as the chosen modulation, its implementation and the communication protocol are presented on this paper. Also, the simulation software and the conclusions that lead to the initial prototype design are presented.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"27 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82198203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ying Wang, Lijun Xu, Shijie Sun, Xupeng Lu, Jiangtao Sun
{"title":"Influence of Parameters in Kalman-filter-based Method on Image Quality for Electrical Capacitance Tomography","authors":"Ying Wang, Lijun Xu, Shijie Sun, Xupeng Lu, Jiangtao Sun","doi":"10.1109/I2MTC50364.2021.9459913","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459913","url":null,"abstract":"As a powerful tool to get a recursive solution of least squares estimation, the Kalman filter has been used for image reconstruction in Electrical Capacitance Tomography (ECT). In the Kalman-filter-based image reconstruction method, some key parameters, e.g., initial guess, observation noise covariance and initial estimate error covariance, greatly influence the performance of the method. Inappropriate values of these parameters may cause a series of problems, such as lower convergence rate, artifacts, or filter divergence. This paper aims to analyze the influence of the parameters on the image quality for ECT and guide the selection of the parameters. Numerical simulation and experiment were carried out and the results show that with an initial guess obtained by linear back projection (LBP) method and a good match of observation noise covariance and initial estimate error covariance, the performance of the Kalman-filter-based method can be improved.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"5 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83509730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive Variance Estimation of Sensor Noise within a Sensor Data Fusion Framework","authors":"Dominik Schneider, Bernhard Liebhart, C. Endisch","doi":"10.1109/I2MTC50364.2021.9459790","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459790","url":null,"abstract":"In the field of signal filtering sensor noise variance estimation is of high interest. Various approaches exist for different applications. Within this work, we propose a novel online noise variance estimation scheme based on an online algorithm with exponential forgetting. The approach serves as an extension of a sensor data fusion algorithm that was presented earlier for the application within multi-cell battery systems equipped with cell-individual sensors. Utilizing measurements of electrical-linked sensors the signals and their noises are separated, and the noise variance is adaptively determined. Experiments show that sensor data fusion is equivalent to common methods like low-pass filtering to gain the target signal. Consequently, the variance is estimated with high accuracy especially with regard to signals featuring high dynamic range. Moreover, the results are on a par with difference-based noise estimation. Furthermore, the influence of relevant parameters on the method is investigated namely the adaptivity of the algorithm and the necessary number of involved sensors. As a result, with just eight sensors decent results are achieved within an exemplary application.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81700813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suman Biswas, A. Mandal, Moupali Chakraborty, K. Biswas
{"title":"Determination of Fat, SNF and Protein Content in Cow Milk from the Voltage Output of ‘MilkTester’","authors":"Suman Biswas, A. Mandal, Moupali Chakraborty, K. Biswas","doi":"10.1109/I2MTC50364.2021.9459873","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459873","url":null,"abstract":"In this work, we report estimation of fat, protein and solid not fat (SNF) of cow milk using the output voltage obtained from the ‘MilkTester’, developed by the authors at Indian Institute of Technology Kharagpur (IIT Kharagpur). The estimation is carried out in three phases named as “Training”, “Interrelation”, and “Validation”. In the “Training Phase”, output voltage from the “MilkTester” is expressed as multivariate equation of fat, SNF and protein. The data sets of fat, SNF and protein are collected using the commercial instrument, “MilkoScreen”(from FOSS, Denmark). This instrument is installed in National Dairy Research Institute Kalyani, India to measure the constituents of milk. Interrelations between “protein & SNF” and “SNF & fat” are estimated by linear regression analysis using the software, OriginPro 8.5, which return the value of the coefficients of the equations. Finally, relation between output voltage and fat is obtained. Once the value of fat percentage is known, the other two parameters can be found out by using the interrelation equations. In the ‘Validation Phase’, fat, SNF and protein are regarded as unknown components and estimated using voltage data (from the ‘MilkTester’). The error between the estimated value (from regression analysis) and true value (obtained from the “MilkoScreen’) is also evaluated for all the three parameters for randomly chosen samples. The maximum error, 12.21 %, is found for estimation of protein. But the difference of absolute value is only 0.59. Maximum error for fat estimation is 10.01 %, where absolute difference is 0.63. The SNF estimation shows error of 4.61 % with absolute error of 0.45.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83512333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reconstruction of Galvanic Skin Response Peaks via Sparse Representation","authors":"Grazia Iadarola, A. Poli, S. Spinsante","doi":"10.1109/I2MTC50364.2021.9459905","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459905","url":null,"abstract":"Continuous and long-term measurement of physiological signals out of clinical settings may face different processing requirements resulting in higher costs or reduced performance. Improved techniques of signal reconstruction from compressed representation may be a solution. This paper presents an approach based on Compressed Sensing to reconstruct peaks of Galvanic Skin Response measured by a wrist-worn device. Specifically, a random measurement matrix is employed in the reconstruction phase. Results show that the proposed approach detects the correct number of peaks better than the Ledalab automatic toolbox, even with high compression rates.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"68 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91206500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Murliky, G. Oliveira, Gabriel Gosmann, V. Brusamarello, F. Sousa
{"title":"Study of a Wireless Energy Transmission System for an Endoscopy Capsule with Dynamic Tuning","authors":"L. Murliky, G. Oliveira, Gabriel Gosmann, V. Brusamarello, F. Sousa","doi":"10.1109/I2MTC50364.2021.9460042","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9460042","url":null,"abstract":"Wireless Power Transfer (WPT) is a technique employed to transmit energy from a source to one or multiple loads, usually located over short distances by near-field inductive coupling. An inductive link is connected to each load through a matching capacitive network for tuning the system and optimizing the whole power transfer process. However, when the frequency of the source is constant and the capacitive compensation network is fixed, the optimized operating points can only be guaranteed for fixed loads and for fixed positions between the transmitting and receiving coils. When the coils are nonstationary or the load is variable, the tuning of the inductive link must be dynamically adjusted to maintain a constant output power. This work presents the development of an optimized WPT control method for the application of an endoscopy procedure, guaranteeing sufficient energy to power a camera and a communication system allocated inside a small capsule of $approx 26.1times 9$ mm. A 3D receiver coil is located inside the capsule moving freely inside a transmitter coil with 400 mm diameter through translations and revolutions. The proposed system compensates disturbances such as the mechanical misalignments between the transmitter and three receiver coils in quadrature and load variation, by the dynamic adjustment of both the frequency and the matching compensation network. Preliminary results have shown reasonable power levels on the load even with low magnetic coupling coefficients ($k approx 0.002$), featured by the experimental endoscopic arrangement, providing energy to power the electronics to collect and transmit data to an external receiver.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90496349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machine Anomaly Detection under Changing Working Condition with Syncretic Self-Regression Auto-Encoder","authors":"Jingyao Wu, Zhibin Zhao, Hongbing Shang, Chuang Sun, Ruqiang Yan, Xuefeng Chen","doi":"10.1109/I2MTC50364.2021.9460002","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9460002","url":null,"abstract":"Condition monitoring is one of the key tasks for the intelligent maintenance of high-end equipment. Facing the challenge of its changing working conditions, intelligent monitoring models that are built upon constant working conditions are not qualified for this task. To solve this problem, a syncretic self-regression variational auto-encoder (SSR-VAE) model is proposed to realize the parallel training of distribution learning and regression learning for machine anomaly detection. Among them, self-regression learning plays an auxiliary role in distribution learning. Furthermore, multi-sensor information fusion at the decision level is implemented to improve the robustness of the proposed model. The effectiveness of this model is evaluated on a gearbox test platform under changing working conditions.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78603927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julian M. Ruiz-Echeverri, Juan C. Bernal-Romero, J. Ramírez-Cortés, P. Gómez-Gil, J. Rangel-Magdaleno, H. Peregrina-Barreto
{"title":"Dorsal hand veins biometrics using NIR images with fusion of classifiers at score level","authors":"Julian M. Ruiz-Echeverri, Juan C. Bernal-Romero, J. Ramírez-Cortés, P. Gómez-Gil, J. Rangel-Magdaleno, H. Peregrina-Barreto","doi":"10.1109/I2MTC50364.2021.9459955","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459955","url":null,"abstract":"This paper presents a biometric system on dorsal hand vein images in the near infrared (NIR), with an approach based on fusion of classifiers at score level. Fiducial features containing information on texture and shape are used with two classifiers based on Chi-square distance and Dynamic Time Warping (DTW), respectively, and further fused at score level. A collection of experiments using a publicly available dataset obtained from Universidad de Las Palmas de Gran Canaria was carried out. The obtained results indicate an Equal Error Rate of EER=0.0486 and EER=0.0274 and in average with classifiers fusion using sum and multiplication of scores in verification mode, and recognition rate of RR=95.80% and RR=97.30% in identification mode, respectively. These results represent an improvement with respect to results obtained when both classifiers and features are used individually.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"52 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78668422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conceptual design and evaluation of an optical sensor for wide-area high-voltage metering and protection applications","authors":"G. Fusiek, P. Niewczas","doi":"10.1109/I2MTC50364.2021.9459883","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459883","url":null,"abstract":"This paper considers the design of a fiber-optic voltage sensor for applications in the field of wide area monitoring, protection and control of high voltage power networks. The proposed 132-kV sensor, combining a capacitive voltage divider (CVD) and an optical medium voltage transducer (MVT), was theoretically evaluated through software simulations and its expected performance was assessed based on the operational requirements specified by the IEC standards for electronic voltage transformers. The simulation results suggest that the sensor, when fabricated, should be capable of meeting the requirements for metering and protection classes specified by the IEC 60044–7 standard.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"14 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88699308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Adamo, F. Attivissimo, A. Nisio, M. Ragolia, M. Scarpetta
{"title":"A New Processing Method to Segment Olive Trees and Detect Xylella Fastidiosa in UAVs Multispectral Images","authors":"F. Adamo, F. Attivissimo, A. Nisio, M. Ragolia, M. Scarpetta","doi":"10.1109/I2MTC50364.2021.9459835","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459835","url":null,"abstract":"In this paper, a new approach for fast detection of Xylella fastidiosa bacterium symptoms on olive trees is presented. Images are taken using a multirotor unmanned aerial vehicle (UAV) equipped with a multispectral camera. A new segmentation algorithm to recognize trees is applied and images are then classified using linear discriminant analysis. It has been applied to selected sites in the Southern Italy where multispectral images of olive orchards have been acquired. The developed algorithm seems to be very promising thanks to its high mean Sørensen-Dice similarity coefficient, which demonstrates the feasibility of a correct tree individuation, and its sensitivity in detecting infected trees.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"19 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88828729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}