A wide-output, power-efficient, high-voltage charge pump with a variable number of stages is proposed and realized in a 0.18 μm 1.8 V/3.3 V CMOS process. The proposed stage selection circuit changes the node voltages in the charge pump circuit in a domino effect to ensure that the maximum voltages across the terminals of each transistor are kept within the normal supply voltage (VDD). The stage selection circuit is able to bypass or activate each stage of the charge pump. Experimental results indicate that the proposed charge pump provides a wide-output voltage range: 3.3-12.6 V from a 3.3 V input source. A peak efficiency of 70% was reached in the charge pump at a current loading of 3.5 mA. By selecting the optimal number of active stages, the overall power efficiencies can be greater than 60% under the output voltages of 4.8 V, 8.1 V, and 10.8 V, respectively. By optimizing the number of active stages, an increase of up to 35% power efficiency can be gained. The proposed stage selection circuit is applicable to other on-chip wide-output charge-pumps.

In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.

A high-precision and low-temperature-coefficient bandgap voltage reference is proposed in 0.11 μm CMOS process. A temperature piecewise compensation circuit is added to a traditional bandgap reference to decrease the temperature coefficient (TC). The digital trimming technology has been used to solve the deviation of TC and output voltage resulting from process corner and mismatch. Simulation result shows that the bandgap reference achieves TC of 2.18 ppm/°C from −40°C to 125°C. Bandgap reference output voltage is 1.2 V with in the error of ±5 mV.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.