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Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model. Firstly, the model utilized the bidirectional feature pyramid network (BiFPN) to achieve multi-scale feature fusion efficiently. Then, the transformer and convolutional block attention module (CBAM) attention mechanisms were added to reduce the interference from invalid background information, improving disease characteristics’ expression ability and increasing the accuracy and recall of the model. Experimental results showed that the proposed BTC-YOLOv5s model (with a model size of 15.8M) can effectively detect four types of apple leaf diseases in natural scenes, with 84.3% mean average precision (mAP). With an octa-core CPU, the model could process 8.7 leaf images per second on average. Compared with classic detection models of SSD, Faster R-CNN, YOLOv4-tiny, and YOLOx, the mAP of the proposed model was increased by 12.74%, 48.84%, 24.44%, and 4.2%, respectively, and offered higher detection accuracy and faster detection speed. Furthermore, the proposed model demonstrated strong robustness and mAP exceeding 80% under strong noise conditions, such as exposure to bright lights, dim lights, and fuzzy images. In conclusion, the new BTC-YOLOv5s was found to be lightweight, accurate, and efficient, making it suitable for application on mobile devices. The proposed method could provide technical support for early intervention and treatment of apple leaf diseases.

The cycloid gear wear of RV reducers leads to the degradation of the industrial robots’ transmission accuracy, but the degradation law with respect to the wear volume is still unclear. In this paper, a method for determining transmission error (TE) through a combination of numerical and simulation analysis is proposed. The wear model of cycloid gear was ascertained based on the theory of Archard. Then, the full rigid body and rigid–flexible coupling model of RV reducers were established using the multibody dynamics theory. Finally, the static transmission error (STE) and dynamic transmission error (DTE) were investigated. The results show that as working hours increase, the cycloid gear wear volume increases, and transmission accuracy deteriorates, but the rate tends to slow down.

Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are prevailing eye diseases that can lead to blindness. In order to provide a non-invasive diagnostic method for glaucoma, we investigated the feasibility of using drop-coating deposition Raman spectroscopy (DCDRS) to discriminate glaucoma patients from healthy individuals based on tear samples. Tears from 27, 19 and 27 POAG patients, PACG patients and normal individuals, respectively, were collected for Raman measurement. For high-dimension data analysis, principal component analysis–linear discriminant analysis (PCA-LDA) was used to discriminate the features of the Raman spectra, followed by a support vector machine (SVM) used to classify samples into three categories, which is called a PCA-LDA-based SVM. The differences in the characteristic peaks of Raman spectra between glaucoma patients and normal people were related to the different contents of various proteins and lipids. For the PCA-LDA-based SVM, the total accuracy reached 93.2%. With the evaluation of 30% test dataset validation, the classification accuracy of the model was 90.9%. The results of this work reveal that tears can be used for Raman detection and discrimination by combining the process with the PCA-LDA-based SVM, supporting DCDRS being a potential method for the diagnosis of glaucoma in the future.

This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilizes the chopping, correlated double sampling, and dynamic element matching techniques to mitigate the effects of process bias and nonideal device characteristics on measurement accuracy. Furthermore, based on the principle of charge conservation, the dynamic range utilization of the ADC increases. We propose a neural network that uses a multilayer convolutional perceptron to calibrate the sensor output results. Using the algorithm, the sensor achieves an inaccuracy of ±0.11 °C (3σ), exceeding the accuracy of ±0.23 °C (3σ) achieved without calibration. We implement the sensor in a 0.18 µm CMOS process, occupying an area of 0.42 mm2. It achieves a resolution of 0.01 °C and has a conversion time of 24 ms.