Chromatic confocal sensors are widely used in various precision measurement fields because of their high measurement accuracy, fast response speed, and good stability. Unlike traditional fiber-coupled structures, we p...
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Chromatic confocal sensors are widely used in various precision measurement fields because of their high measurement accuracy, fast response speed, and good stability. Unlike traditional fiber-coupled structures, we propose an integrated compact chromatic confocal sensing system that can overcome the device-integrating constraints met in industrial environments. Aiming at the distortion of the peak waveform caused by the inconsistent spectral response of the system and to accurately extract the peak wavelength, a spectral characteristic compensation algorithm and a peak wavelength extraction method based on Gaussian curve fitting are proposed. Based on these methods, a segmented curve calibration algorithm is applied to achieve accurate mapping between peak wavelength and position. For the thickness measurement of transparent objects, a simple thickness measurement model and its calibration procedure are proposed, which do not need to obtain previous parameters, such as incident angle or refractive index. Finally, the performance of the proposed sensing system is tested by displacement measurement and thickness measurement experiments. The experimental results show that the root mean square error (RMSE) of displacement measurement is less than 0.1 mu m, and the RMSE of thickness measurement is less than 1 mu m, which verifies the effectiveness and feasibility of the proposed sensing system.
In this study, a weighted estimation algorithm is proposed for food transporting two-axis magnetic compass. This method is based on ellipse fitting algorithm and compensates the combined effort of all linear time-inva...
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In this study, a weighted estimation algorithm is proposed for food transporting two-axis magnetic compass. This method is based on ellipse fitting algorithm and compensates the combined effort of all linear time-invariant distortions, namely bias, scale factor, hard iron, non-orthogonality and so on. In contrast to the direct ellipse fitting method for estimating ellipse coefficient, which achieves best estimate based on minimizing the mean square algebraic distance from collected data points to ellipse in mathematical model, this procedure presents a new estimator in least-square sense where the weighted approximate distance is presented. The algorithm is simulated to verify robustness and further validated on collected experimental data using a low-cost fluxgate compass. The results indicate that the calibration algorithm is effective and superior to the direct ellipse fitting method, the heading error after calibration is less than.
In this paper, the non-ideal factors, which include spatial noise and temporal noise, are analyzed and suppressed in the high-speed spike-based image sensor, which combines the high-speed scanning sequential format wi...
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In this paper, the non-ideal factors, which include spatial noise and temporal noise, are analyzed and suppressed in the high-speed spike-based image sensor, which combines the high-speed scanning sequential format with the method that uses the interspike time interval to indicate the scene information. In this imager, spatial noise contains device mismatch, which results in photo response non-uniformity (PRNU) and the non-uniformity of dark current. By multiplying the measured coefficient matrix the photo response non-uniformity is suppressed, and the non-uniformity of dark current is suppressed by correcting the interspike time interval based on the time interval of dark current. The temporal noise is composed of the shot noise and thermal noise. This kind of noise can be eliminated when using the spike frequency to restore the image. The experimental results show that, based on the spike frequency method, the standard deviation of the image decreases from 18.4792 to 0.5683 in the uniform bright light by using the calibration algorithm. While in the relatively uniform dark condition, the standard deviation decreases from 1.5812 to 0.4516. Based on interspike time interval method, because of time mismatch and temporal noise, the standard deviation of the image changes from 27.4252 to 27.4977 in the uniform bright light by using the calibration algorithm. While in the uniform dark condition, the standard deviation decreases from 2.361 to 0.3678.
The air2water model is a physically-based model in which major physical processes are parameterized. It allows to predict the surface lake temperature based solely on the time series of the air temperature. Due to its...
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The air2water model is a physically-based model in which major physical processes are parameterized. It allows to predict the surface lake temperature based solely on the time series of the air temperature. Due to its simplicity and very limited data requirements, it has found numerous applications around the globe. The air2water model defines the normalized depth of the well-mixed surface layer using a fixed parameter. This parameter is a threshold value that is set to 4 degrees C - the temperature of water with maximum density - for dimictic lakes, and to the minimum or to the maximum water temperature for warm or cold monomictic lakes, respectively. In this paper we propose to calibrate the threshold value as the ninth parameter of the model, instead of setting it fixed. We test the proposed approach on a set of 30-years long daily data from 22 lakes located in the lowland part of Poland and 25-years long daily data from the two Great Lakes - Huron and Eire. The proposed modification is very simple and improves the performance of the model, especially for winter-spring season, for the vast majority of lakes not only for the calibration, but also for the independent 8-10 years long validation data.
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