Skin is the largest organ in the body, and directly contact with the external environment. Articles on the role of micro-current and skin have emerged in recent years. The function of micro-current is various, including introducing various drugs into the skin locally or throughout the body, stimulating skin wounds healing through various currents, suppressing pain caused by various diseases, and promoting blood circulation for postoperative muscle rehabilitation, etc. This article reviews these efforts. Compared with various physical and chemical medical therapies, micro-current stimulation provides a relatively safe, non-invasive therapy with few side effects, giving modern medicine a more suitable treatment option. At the same time, the cost of the electrical stimulation generating device is relatively low, which makes it have wider space to and more clinical application value. The current micro-current stimulation technology has become more and more mature, but there are still many problems in its research. The design of the experiment and the selection of the current parameters not standardized and rigorous. Now, clear regulations are needed to regulate this field. Micro-current skin therapy has become a robust, reliable, and well-structured system Graphic Abstract

Augmented Reality (AR) is crucial for immersive Human-Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry. Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems.

The gradual application of deep learning in the field of computer vision and image processing has made great breakthroughs. Applications such as object detection, recognition and image semantic segmentation have been improved. In this study, to measure the distance of the vehicle ahead, a preceding vehicle ranging system based on fitting method was designed. First obtaining an accurate bounding box frame in the vehicle detection, the Mask R-CNN (region-convolutional neural networks) algorithm was improved and tested in the BDD100K (Berkeley deep derive) asymmetry dataset. This method can shorten vehicle detection time by 33% without reducing the accuracy. Then, according to the pixel value of the bounding box in the image, the fitting method was applied to the vehicle monocular camera for ranging. Experimental results demonstrate that the method can measure the distance of the preceding vehicle effectively, with a ranging error of less than 10%. The accuracy of the measurement results meets the requirements of collision warning for safe driving.

Vehicle-to-environment interactionResearches on Multi-agent Reinforcement Learning(MARL) Collaborative behavior between vehicles Sensitive to model parameters Decision risk caused by the uncertainty Key scientific issues Main research contents Distributed partial observation Markova decision processPrototype system Competitive scenarios V2V Vehicle-to-vehicle interaction Robust MARL Risk-aversion MARL All-weather types Diverse road types Complex environment Sensing task for traffic lights Sensing task for traffic lights Stop before the stopline Keep current state Joint evaluation of task difficulty and vehicle performance

Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for improving performance on limited data. However, we empirically find that due to the dataset discrepancy, directly transferring the model trained on a general object dataset to small-object datasets obtains inferior performance. In this paper, we propose TranSDet, a novel approach for effective transfer learning for small-object detection. Our method adapts a model trained on a general dataset to a small-object-friendly model by augmenting the training images with diverse smaller resolutions. A dynamic resolution adaptation scheme is employed to ensure consistent performance on various sizes of objects using meta-learning. Additionally, the proposed method introduces two network components, an FPN with shifted feature aggregation and an anchor relation module, which are compatible with transfer learning and effectively improve small-object detection performance. Extensive experiments on the TT100K, BUUISE-MO-Lite, and COCO datasets demonstrate that TranSDet achieves significant improvements compared to existing methods. For example, on the TT100K dataset, TranSDet outperforms the state-of-the-art method by 8.0% in terms of the mean average precision (mAP) for small-object detection. On the BUUISE-MO-Lite dataset, TranSDet improves the detection accuracy of RetinaNet and YOLOv3 by 32.2% and 12.8%, respectively.

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