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3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR–camera fusion 3D object detection. Firstly, a 2D backbone is designed to extract features from camera images. Secondly, an attention-based fusion sub-network is designed to fuse the features extracted by the 2D backbone and the features extracted from 3D LiDAR point clouds by PointNet++. Besides, the FuDNN, which uses the RPN and the refinement work of PointRCNN to obtain 3D box predictions, was tested on the public KITTI dataset. Experiments on the KITTI validation set show that the proposed FuDNN achieves AP values of 92.48, 82.90, and 80.51 at easy, moderate, and hard difficulty levels for car detection. The proposed FuDNN improves the performance of LiDAR–camera fusion 3D object detection in the car category of the public KITTI dataset.

Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively.

Micro-droplet generation is related to liquid dispensing technology that has potential applications in many fields. Specifically, pneumatic micro-droplet generation is controlled by a solenoid valve being briefly turned on, so that high pressure gas enters the liquid reservoir, forming a gas pressure pulse waveform , forcing the liquid out through a tiny nozzle to form a micro-droplet. For each ejection, is acquired by a high speed pressure sensor, and the ejection state is obtained by machine vision methods. A prediction model based on BP neural network is established, with as input and the droplet ejection state as output. Experiments show that the BP neural network can predict the number of droplets with an accuracy higher than 99%. It is also shown that the BP neural network can improve the prediction accuracy for the position of droplets relative to the nozzle, at a given moment. Under typical working conditions, is not consistent. As a result, the ejection state is not consistent either. These prediction models may be used for real time monitoring and control of the pneumatic micro-droplet generator.

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