The development of stretchable smart electronics has attracted great attentions due to their potential applications in human motions energy collection systems and self-powered biomechanical tracking technologies. Here, we present a newly stretchable all-rubber-based thread-shaped triboelectric nanogenerator (TENG) composed of the silver-coated glass microspheres/silicone rubber as the stretchable conductive thread (SCT) and the silicone rubber-coated SCT (SSCT) as the other triboelectric thread. The stretchable all-rubber-based thread-shaped TENG (SATT) generates an open-circuit voltage of 3.82 V and short-circuit current of 65.8 nA under the 100% strain and can respond to different finger motion states. Furthermore, the self-powered smart textile (SPST) woven by the SCT and SSCT units has two kinds of working mechanisms about stretch-release and contact-separation modes. The stretching-releasing interaction between knitting units can generate an open-circuit voltage of 8.1 V and short-circuit current of 0.42 μA, and the contacting-separating mode occurs between cotton and two types material outside the SPST producing peak voltage of 150 V and peak current of 2.45 μA. To prove the promising applications, the SPST device is capable to provide electrical energy to commercial electronics and effectively scavenge full-range biomechanical energy from human joint motions. Therefore, this work provides a new approach in the applications of stretchable wearable electronics for power generation and self-powered tracking. Electronic supplementary material The online version of this article (10.1186/s11671-019-3085-9) contains supplementary material, which is available to authorized users.

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This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation.

The process of missile launch training was confined to the virtual scene in the past. So, cooperating with an artillery college, the group makes the moving target detection technology to be applied in missile training equipment, so as to make the training apply to the field operations. This paper presents the frame difference mapping algorithm, which is used to detect the moving target in the background of moving video frame. According to the target region which is given out by the system in the graphical interface, the students do the launching missile training. The moving target detection algorithm which is provided with the low complexity and the high accuracy, i.e. proposed by the paper, is based on Gauss mixture model and frame difference mapping. The mechanism of layered-graphics and the message agent which makes the modules in the system be independent of each other are used in the system designing. So, the module coupling degree in terms of this mechanism is lower than before. This mechanism brings convenience to system maintenance and upgrade, especially for the system’s transplanting to the real missile launch system in future.

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