Abstract:To reduce the stitching error of circularity measurement of small cylindrical workpieces (Diameter less than 3 mm) by the segmenting-stitching method, arc contour extraction is analyzed in this paper. The coordinates of cross-sectional circle of a small cylindrical part are segmented into several equal arcs to be obtained by a two-dimensional coordinate measuring machine. The circularity contour of the small cylindrical part can be formed by stitching a series of arc contours which are calculated by the obtained arc coordinate data. Due to the different measuring pressure angles of different measuring positions, the accuracy of obtained arc coordinate points is different. The bigger the pressure angle is, the accurate the obtained arc coordinate data are. The ex-periments show that the accuracy of two ends of the arc data is not as good as the central part. Therefore, the two ends of the obtained arc data are appropriately to be cut off, namely, only the central part of the arc data are extracted to be used for the stitching. As a result, the mean value of the matching coefficient is enhanced by 12%, the deviation between the overlap part of the neighbouring arc contour is reduced by 26%, and the average curvature of the arc contours is improved with the extraction method. Thus, the accuracy of the stitched cir-cularity contour can be improved by this extraction procedure in the segmenting-stitching method for the cir-cularity measurement of the small cylindrical parts.

Abstract:This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice, which is an important factor affecting human health. While laboratory-grade refractometers are expensive and unsuitable for personal use, existing low-cost commercial options lack stability and accuracy. To address this gap, we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS sensor. By analyzing the output waveform pattern of the CMOS sensor, we achieve high precision with a personal-use-appropriate accuracy of 0.1%. We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples, and the results demonstrate its reliability and consistency. Running on a 48 MHz ARM processor, the algorithm can acquire data within 0.2 seconds. Our low-cost refractometer is suitable for personal health management and small-scale production, providing an affordable and reliable method for measuring sucrose concentration in fruit juice. It improves upon the existing low-cost options by offering better stability and accuracy. This accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.

Abstract:Rotational Vision System (RVS) is a common active vision system with only rotational degrees of freedom. Usually, the degree of freedom for rotation is provided by the turntable and pan head. Or the hand to eye (EIH) structure in articulated arm robots. Due to assembly deviations and manufacturing accuracy limitations, the ideal assumption that the rotation axis is fully aligned with the coordinate axis of the local camera is mostly violated. To address this issue, we propose a generalized deviation model that specifies a rotation axis that connects the rotational motion of the platform with the external orientation (EO) of the camera. On this basis, we propose a heuristic estimation algorithm to minimize global reprojection errors and fit circles in space under constraints of global optimization. The experiment shows that the translation and tilt average reprojection errors of dynamic EO reconstruction based on the reprojection error method are 0.14 and 0.08 pixels, respectively. In the absence of angle measurement, the results of the circle fitting method are similar to them (with a relative error of about 2%), meeting the application requirements of general visual measurement.

Abstract:In order to allow the guardians to monitor the physiological parameters of the infant more intuitively and to be able to respond to sudden irregularities in the pulse rate, abnormal blood oxygen, high or low body temperature and other conditions, and to facilitate communication with the medical staff or to request assistance in treatment, an STM32 microcontroller-based infant health monitoring system is designed. The digital signal acquisition module for pulse, blood oxygen and body temperature acquire the raw data, and the microcontroller performs algorithmic processing to display the physiological parameters such as pulse, blood oxygen and body temper-ature of the infant, and configures the threshold alarms for the physiological parameters by means of a keypad module. Finally, the test results are compared and tested against the standard physiological parameters of infants and children to verify that the system meets the requirements of medical precision and accuracy.

Abstract:In order to address the issues of the current sheep face pain detection algorithm under complex environments with poor detection accuracy and complex models, this paper proposes a two-stage method of sheep face pain detection based on light yolov5s. First, the weights of the yolov5s model are reduced by combining the Ghostnet structure, feature fusion is performed using the BiFPN structure and the ODConv join to improve detection accuracy. The experimental results show that the number of parameters and complexity of the optimized model are reduced by 38.03% and 50.94%, respectively, compared with the original model, and the accuracy of recognizing sheep faces is improved by 0.2% and the recall rate is increased by 1%. Compared to current mainstream algorithms such as yolov4 tiny and SSD, it not only significantly reduces the number of parameters but also has a better detection performance. Second, pain detection of sheep faces recognised by lightweight yolov5s was carried out by MobileNetV2, and experimental results showed that MobileNetV2 achieved a mean classification accuracy of over 98% for painful sheep, and this illustrates that this two-stage sheep face pain recognition research method has great application value for sheep health breeding.

Abstract:With the improvement of the degree of aging, the traditional pension model can no longer meet the growing needs of the elderly. Therefore, it is necessary to use the intelligent means of information technology to improve the level of pension services. This paper will integrate multi-sensor fusion technology, NB-IoT communication technology and cloud platform technology to develop and design a smart pension online monitoring system to realize real-time collection of human health and motion status information and realize monitoring platform management. In this system, STM32 microcontroller will be used as the main control module, and MAX30102, ADXL345 and DS18B20 sensors will be used to collect the heart rate, blood oxygen, displacement and body temperature of the human body in real time. On the one hand, the communication part is completed by the BC20 Internet of Things module. The data transmission between the terminal detection device and the cloud platform, on the other hand, the HC-42 Bluetooth module is used to complete the data communication with the mobile phone. The test results show that the system can collect and process data accurately in real time and maintain good communication with the cloud platform and mobile phone. The designed system has strong pertinence, easy operation, high reliability and broad development prospects.