A Method of Image Identification in Instrumentation
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Smart city is currently the main direction of development. The automatic management of instrumentation isone task of the smart city. Because there are a lot of old instrumentation in the city that cannot be replacedpromptly, how to makes low-cost transformation with Internet of Thing (IoT) becomes a problem. This articlegives a low-cost method that can identify code wheel instrument information. This method can effectivelyidentify the information of image as the digital information. Because this method does not require a lot ofmemory or complicated calculation, it can be deployed on a cheap microcontroller unit (MCU) with low readonlymemory (ROM). At the end of this article, test result is given. Using this method to modify the oldinstrumentation can achieve the automatic management of instrumentation and can help build a smart city.
Abstract
Smart city is currently the main direction of development. The automatic management of instrumentation isone task of the smart city. Because there are a lot of old instrumentation in the city that cannot be replacedpromptly, how to makes low-cost transformation with Internet of Thing (IoT) becomes a problem. This articlegives a low-cost method that can identify code wheel instrument information. This method can effectivelyidentify the information of image as the digital information. Because this method does not require a lot ofmemory or complicated calculation, it can be deployed on a cheap microcontroller unit (MCU) with low readonlymemory (ROM). At the end of this article, test result is given. Using this method to modify the oldinstrumentation can achieve the automatic management of instrumentation and can help build a smart city.
참고문헌 (7)
- A. Vishnuvarthanan, "Development of a combinational framework to concurrently perform tissue segmentation and tumor identification in T1-W, T2-W, FLAIR and MPR type magnetic resonance brain images", Expert Systems with Applications, 95, pp.280~311 (2018)[DOI]
- T. Go, "Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning", Biosensors and Bioelectronics, 103, pp.12~18 (2018)[DOI]
- X. Ma, "Flow pattern identification for two-phase flow in a U-bend and its contiguous straight tubes", Experimental Thermal and Fluid Science, 93, pp.218~234 (2018)[DOI]
- H. Z. M, "Butt welding joints recognition and location identification by using local thresholding", Robotics and Computer-Integrated Manufacturing, 51, pp.181~188 (2018)[DOI]
- Q. Zhao, "Flame detection using generic color model and improved block-based PCA in active infrared camera", International Journal of Pattern Recognition and Artificial Intelligence, 32(5) (2018)
- Z. Liu, "Improved sparse 3D transform-domain collaborative filter for screen content image denoising", International Journal of Pattern Recognition and Artificial Intelligence, 32(3) (2018)
- L. Lambert, "The predictive value of computed tomography in the detection of reflux esophagitis in patients undergoing upper endoscopy", Clinical Imaging, 49, pp.97~100 (2018)[DOI]
