Rotational angle and speed detection via a chip-scale GaN optoelectronic device.

In this paper, a new method for rotational angle and speed measurements is proposed by integrating a GaN optoelectronic chip with a stepped disc. The optoelectronic chip that integrates a light-emitting diode (LED) and a photodiode (PD) is fabricated by wafer-level microfabrication. The disc is designed with a spiral staircase shape, and has increasing thickness distribution along the circumferential direction. The sensing mechanism is that the optoelectronic chip measures angle-dependent intensity change of the light reflected off the stepped disc. Through a series of performance tests, the chip is highly sensitive to a continuous rotation from 0 ∘ to 360 ∘, and produces photocurrent to indicate the rotational angle and speed. A rotational speed up to 5000 rpm is measured with a relative error less than 1.27%. The developed sensing architecture provides an alternative solution for constructing a low-cost, miniaturized, and high-efficiency rotational angle and speed sensing system.

Collinear optical links based on a GaN-integrated chip for fiber-optic acoustic detection.

This Letter reports a collinear optical interconnect architecture for acoustic sensing via a monolithic integrated GaN optoelectronic chip. The chip is designed with a ring-shaped photodiode (PD) surrounding a light-emitting diode (LED) of a spectral range from 420-530 nm. The axisymmetric structure helps the coaxial propagation of light transmission and reception. By placing this multiple-quantum wells (MQW)-based device and a piece of aluminum-coated polyethylene terephthalate (Al/PET) film on fiber ends, an ultra-compact acoustic sensing system is built. The sound vibrations can be simply detected by direct measurement of the diaphragm deformation-induced power change. An average signal noise ratio (SNR) of 40 dB and a maximum sensitivity of 82 mV/Pa are obtained when the acoustic vibration frequency changes from 400 Hz to 3.2 kHz. This work provides a feasible solution to miniaturize the sensing system footprint and reduce the cost.

Grazing Sheep Behaviour Recognition Based on Improved YOLOV5.

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model's generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model's generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep's daily behaviour for precision livestock management, promoting modern husbandry development.

Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep.

Step counting is an effective method to assess the activity level of grazing sheep. However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors. Therefore, this study proposed a step-counting algorithm based on behavior classification designed explicitly for grazing sheep. The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep. It distinguished leg shaking from brisk walking behaviors through variance feature analysis. Based on the recognition results, different step-counting strategies were employed. When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running. No step counting was performed for leg-shaking behavior. For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting. Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method. In addition, the experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.556%. This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method.