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The measurement for levitation gap is a very important part of levitation control system. The traditional levitation gap sensors have some shortcomings such as small measurement range and specific installation requirements, and usually need nonlinear correction and temperature compensation to meet the control requirements of the magnetic levitation system. In our work, two novel measurement methods for levitation gap based on computer vision are proposed. First, the levitation gap is measured by calculating the image pixel area of the region of interest. The error of the pixel area model can be limited within ±0.250 mm, and the mean absolute error (MAE) is 0.095 mm for full scale (FS). Second, the model named SelfConvNet based on convolutional neural network is designed for measuring the levitation gap. The error of SelfConvNet model can be limited within ±0.048 mm, and the MAE is 0.013 mm for FS. The measurement results show that the SelfConvNet model is better than SqueezeNet and Visual Geometry Group 16 models, which has high measurement accuracy and strong anti-interference ability. The method based on pixel area has lower measurement accuracy but higher processing speed. Finally, the proposed gap measurement methods have been verified in closed-loop experiment of maglev ball control system. [ABSTRACT FROM AUTHOR]

Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%.

A small volume, digital display and nondestructive instrument based on laser backscattering imaging was designed and developed to determine the internal quality of peaches; namely the firmness and soluble solids content. According to the relationship between backscattering image parameters and internal quality parameters of the peaches, we found that pixel area, uniformity and entropy were correlated with the physical and chemical parameters of the peaches. The partial least squares discrimination analysis (PLS-DA) and support vector classification (SVC) models were built with the peach image feature parameters as inputs and the results showed that the grading accuracy of SVC models on firmness and soluble solids content were better than the PLS-DA models. For Hujingmilu and Zaobaihua peaches, the overall classification accuracies of soluble solids content were 94 and 92% and the prediction accuracy were 94 and 91%, respectively. Additionally, the overall classification accuracies of firmness were 95 and 93%, and the prediction accuracy were 94 and 93% respectively. This research demonstrates the feasibility of developing portable quality classification instruments based on laser backscattering imaging for fruits. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bone fragments in pork could be detected by using single-band images at 780 nm. • The area library of bone regions segmented from images at 780 nm was built. • A new method for detecting bone was established by comparing the pixel areas. • The visualization results could provide objective evidences for test results. This paper was concerned with the detection of hazardous bone fragments embedded in lean pork slices (LPS-BFs) by comparing the pixel area of bone regions segmented from single-band image. All images were collected using a Videometer A/S multispectral imaging system with 19 bands (405–970 nm). An area library of bone regions was established by processing 60 images captured from LPS-BFs and the minimum value of the library was defined as a boundary point for judgement. The accuracy of 90 % for 120 test samples (60 LPSs and 60 LPS-BFs) was achieved and all results could be visualized for monitoring the geometrical morphology of bones. Results of satisfactory identification of bone fragments embedded in LPSs indicated that image features at single-band could serve as a feasible approach for online industrial applications. [ABSTRACT FROM AUTHOR]

The article discusses the progress of integrated electronics in terms of Gordon Moore's observation that the number of components on a single piece of silicon had doubled every year since the invention. This observation has become known as Moore's law. The contributions of device miniaturization and chip size to the increasing content of a chip were also discussed. The number of devices on a chip was found to increase faster than the chip area per pixel area. [ABSTRACT FROM PUBLISHER]