2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)最新文献

{"title":"A fast feature point extraction method for optical tracking system","authors":"Songlin Bi, Yonggang Gu, Zhihong Zhang, Jiaqi Zou, C. Zhai, Ming Gong","doi":"10.1109/I2MTC50364.2021.9459808","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459808","url":null,"abstract":"Optical tracking systems (OTS) mainly consist of multiple cameras and optical target. A number of markers, at least three, are attached to the target. Position and motion posture of the target are obtained by photographing those markers. A small part of the image is occupied by marker. If markers are searched by traversing the whole image, huge expenditure and extravagant computing resources will be caused. A fast feature point extraction method is described in this paper to reduce computational burden. It combines marker prediction, perspective projection, nearest neighbor fast seed point search algorithm, region growing algorithm, and gray centroid method. Compared with the traditional whole image traversal method, the marker extraction speed is improved by hundreds or even thousands of times, which is verified by trinocular vision tracking experiment. The method is suitable for OTS.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"109 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":"73531961","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":"Windowing Compensation in Fourier Based Surrogate Analysis","authors":"Manouane Caza-Szoka, D. Massicotte","doi":"10.1109/I2MTC50364.2021.9460063","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9460063","url":null,"abstract":"This paper shows how adding a second step of windowing after each phase randomization can reduce the False Rejection Rate in Fourier based Surrogate Analysis. Windowing techniques improve the resolution of the Power Spectrum estimation by reducing the sampling gap caused by the periodic extension of the Fourier Series. However, it adds a time domain non-stationarity which affects the Surrogate Analysis. This effect is particularly problematic for short lowpass signals. Applying the same window to the surrogate data allows having the same non-stationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte Carlo simulations. Previous methods were not able to yield good performances for left-sided and right-sided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests as well as bilateral tests.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"11 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":"73817161","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":"Deep Transfer Learning Strategy for Invasive Lung Adenocarcinoma Classification Appearing as Ground Glass Nodules","authors":"Chen Ma, Shihong Yue, Qi Li","doi":"10.1109/I2MTC50364.2021.9459841","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459841","url":null,"abstract":"Lung cancer is one of the deadliest diseases in which adenocarcinoma account for nearly 40%. To make an effective treatment and diagnosis, it is vital to accurately discriminate invasive adenocarcinoma (IA) from non-IA by analyzing ground glass nodules (GGNs) from patient's CT images. Compared with solid nodules and normal lung parenchyma, the contours of GGN are blurred and the gray scale is little changed. So far, the problem to accurately discriminate IA and non-IA remains unsolved due to insufficient labeled GGN images. In this paper, considering the generalization of convolutional neural network (CNN) and various flexible transfer strategies, we proposed a lung adenocarcinoma classification method after combining transfer learning and CNN, where the use of transfer learning strategies aims at overcoming the problem of insufficient GGN samples. Firstly, the CT image on IA and non-IA patients were collected which were labeled by surgical pathology. Secondly, two transfer learning strategies that consist of CNN feature extractor and fine-tuning network were applied to classify IA and non-IA. Finally, in the fine-tuning network process, a Progressive Fine-Tuning (PFT) strategy was combined to determine the effective depth of fine-tuning to avoid inaccurate induction of GGNs. In the CNN feature extractor experiment, four comparable models were used including linear discrimination, Support Vector Machines, K-nearest neighbor, and subspace discrimination. The indicators of sensitivity, specificity, accuracy, and AUC (area under curve) were used to quantitatively assess the performance of the two transfer strategies. Experiments show that the strategy of CNN feature extractor based on transfer learning had the highest accuracy, which was significantly higher than fine-tuning network strategy with PFT. In the experiment of CNN feature extractor, the model of linear discrimination to predict the invasiveness of GGNs has 94% accuracy whereas the other three models have 92.9%, 93.1% and 92.9%, respectively.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"28 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":"74231324","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":"A Calibration Method for 77GHz Millimeter-Wave Radar Based on Virtual Instrument Technology","authors":"Tianqi Xu, Lei Du","doi":"10.1109/I2MTC50364.2021.9459852","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459852","url":null,"abstract":"77GHz millimeter-wave (MMW) radar is mature in technology and widely used in intelligent vehicle environment perception, vehicle safety distance detection, etc. In order to evaluate and ensure its working performances in practical use, the target kinematics parameters of 77GHz MMW radar must be calibrated before installation. A simulated calibration method for target kinematics parameters of 77GHz MMW radar based on virtual instrument technology is proposed and a simulated calibration instrument based on virtual instrument is developed. The basic principle of the simulated calibration method and the main design ideas of the simulated calibration instrument are analyzed. A 77GHz MMW radar sample is chosen for speed and range simulated calibration, and the uncertainty of the calibration results is analyzed and evaluated from the aspects of measurement repeatability, MMW radar resolution and accuracy of the simulated calibration instrument. The expand uncertainty of the simulated speed and range are 0.7km/h and 0.12m respectively, where k=2. The simulation and uncertainty evaluation results preliminarily verify the feasibility of the simulated calibration method and the performance of the simulated calibration instrument.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"24 3 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":"77091935","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":"Microstrip Patch Antenna Bioinspired in Primrose Flower for WLAN and Bluetooth Applications","authors":"Dailan de Jesus Pereira Bernardes, E. Santana, P. D. S. Júnior, M. A. Oliveira, A. Serres, R. Freire, Marlo Andrade Santos, I. M. F. D. Santana, P. F. Silva","doi":"10.1109/I2MTC50364.2021.9459856","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459856","url":null,"abstract":"In this paper is developed a microstrip patch antenna bioinspired in Primrose Flower (Primula vulgaris), generated by a polar equation, built in the low-cost substrate, fiberglass, operating in the wireless local area network at IEEE 802.11b, g, n, Bluetooth standards, and IEEE 802.15. The results present good agreement with simulated and measured values, with the difference between the simulated and measured resonance frequency of 0.36%, measured bandwidth of 51 MHz, the half-power beamwidth of 110 degrees, and maximum gain of 6.18 dBi.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"26 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74721940","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":"A Simple Digitization Scheme for Resistive Sensors and its Adaptation for Remote Measurements","authors":"K. Elangovan, C. Anoop","doi":"10.1109/I2MTC50364.2021.9459812","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459812","url":null,"abstract":"A simple digitization for resistive sensors is proposed in this article. The scheme uses readily available electronic components and provides a digitized indication of resistance. The non-ideal parameters such as bias current and offset voltage of Op-amps and drift of power supply, do not affect the output measurements. Later, an improved version of this digitizer is proposed to measure remotely-located sensors, with the help of the three-wire measurement technique. This enhanced circuit also provides added advantages like immune to drifts of the capacitor and the mismatch between the high and low-level voltages of the components used in the scheme. The operation and analysis of the digitization schemes are elaborated in this article. The performance of the proposed schemes is also verified using simulation studies. The maximum nonlinearity is merely 0.02 %.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"1 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":"77150091","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":"Inter-Batch Gap Filling Using Compressive Sampling for Low-Cost IoT Vibration Sensors","authors":"B. Ooi, S. Liew, W. Beh, S. Shirmohammadi","doi":"10.1109/I2MTC50364.2021.9460080","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9460080","url":null,"abstract":"To measure machinery vibration, a sensor system consisting of a 3-axis accelerometer, ADXL345, attached to a self-contained system-on-a-chip with integrated Wi-Fi capabilities, ESP8266, is a low-cost solution. In this work, we first show that in such a system, the widely used direct-read-and-send method which samples and sends individually acquired vibration data points to the server is not effective, especially using Wi-Fi connection. We show that the micro delays in each individual data transmission will limit the sensor sampling rate and will also affect the time of the acquired data points not evenly spaced. Then, we propose that vibration should be sampled in batches before sending the acquired data out from the sensor node. The vibration for each batch should be acquired continuously without any form of interruption in between the sampling process to ensure the data points are evenly spaced. To fill the data gaps between the batches, we propose the use of compressive sampling technique. Our experimental results show that the maximum sampling rate of the direct-read-and-send method is 350Hz with a standard uncertainty of 12.4, and the method loses more information compared to our proposed solution that can measure the vibration wirelessly and continuously up to 633Hz. The gaps filled using compressive sampling can achieve an accuracy in terms of mean absolute error (MAE) of up to 0.06 with a standard uncertainty of 0.002, making the low-cost vibration sensor node a cost-effective solution.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"53 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":"81558801","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":"Real-time Wave Monitoring on Coastal Areas Using LPWAN-Based Embedded Systems","authors":"M. Carandell, D. Toma, Carola Artero, M. Gasulla, J. Río","doi":"10.1109/I2MTC50364.2021.9459805","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459805","url":null,"abstract":"A new embedded system is presented for real-time wave monitoring on coastal areas using SigFox communication. SigFox is a Low-Power Wide-Area Network technology that has been rarely used in coastal marine monitoring. The system is based on the low-power TD1205P module that includes a microcontroller, an accelerometer, a GNSS receiver and a SigFox transceiver. Each hour, the module estimates the wave's maximum height ($H$max) and mean period (Tz), determines the GPS position, and wirelessly transmits the data through the SigFox network. The procedure for wave parameter estimation is based on the zero-upcrossing method using the vertical acceleration data. It was experimentally validated by attaching the embedded system to a moored buoy and comparing $H$max and Tzwith that provided by a seafloor acoustic wave and current profiler, used as a reference. Results over a period of two months show a good match for $H$max but less for Tz, which crosscorrelation values at zero lag of about 0.85 and 0.5, respectively. Power tests of the embedded system were also performed resulting in a lifetime estimation of 420 days with a battery pack of 3 Ah.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"20 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":"89422839","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":"Improve the Sensing Matrix Model for Random Demodulation in the Case of Mixer With Non-Ideal Characteristics","authors":"Xiaodong Li, Ning Fu, Liyan Qiao","doi":"10.1109/I2MTC50364.2021.9459877","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9459877","url":null,"abstract":"The random demodulation structure successfully applies compressed sensing technology to analog signal sampling, and is expected to replace the Nyquist sampling frame as a new data acquisition scheme. In practical applications, the nonideal characteristics of the mixer will affect the sensing matrix in the RD sampling structure, especially when processing radio frequency signals. This paper analyzes this problem in detail, and proposes a method to modify the sensing matrix to improve the model. Simulation experiments show that the more complete sensing matrix model proposed in this paper can correct the influence caused by the non-ideal characteristics of the mixer.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"25 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":"89879174","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":"Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples","authors":"N. Stasenko, E. Chernova, Dmitrii G. Shadrin, G. V. Ovchinnikov, I. Krivolapov, M. Pukalchik","doi":"10.1109/I2MTC50364.2021.9460071","DOIUrl":"https://doi.org/10.1109/I2MTC50364.2021.9460071","url":null,"abstract":"Artificial Intelligence (AI) methods and technologies have been successfully applied for recognizing objects, detecting and segmenting RGB images. Today, such technologies are widely used in precision agriculture to estimate food quality, especially when assessing plants and fruits at various harvest stages. There are also several processes taking place in food during the postharvest stages, such as decay and moldy. However, the number of AI approaches allowing for assessing the postharvest food conditions is limited. In this work, we trained U-Net and Deeplab models based on Convolutional Neural Networks (CNNs) to detect and predict decay areas in postharvest apples stored at room temperatures. The models were trained on a dataset that includes 4440 images of apples with segmented decay areas. Images were captured by a digital camera mounted on a custom-made testbed. We achieved 99.71% of the mean Intersection over Union (mIoU) at the testing stage for the U-Net model and 99.99% of the mIoU at the testing stage for the Deeplab model trained on 651 images. We also presented the first masks for decay areas in apples predicted by U-Net. Our approach seems to be promising for improving the food storage process in precision agriculture by enabling the automatic detection and quantification of the decayed areas.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"11 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":"85040985","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}