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The reliable, high-sensitive, wireless, and affordable requirements for humidity sensors are needed in high-precision measurement fields. Quartz crystal microbalance (QCM) based on the piezoelectric effect can accurately detect the mass changes at the nanogram level. However, water-capture materials deposited on the surface of QCM generally show disadvantages in either cost, sensitivity, or recyclability. Herein, novel QCM-based humidity sensors (NQHSs) are developed by uniformly depositing green microspheres (GMs) of natural polymers prepared by the chemical synthesis of the emulsification/inner gel method on QCM as humidity-sensitive materials. The NQHSs demonstrate high accuracy and sensitivity (27.1 Hz/% RH) owing to the various hydrophilic groups and porous nano-3D deposition structure. Compared with the devices deposited with a smooth film, the frequency of the NQHSs shows almost no changes during the cyclic test and exhibits long-term stability. The NQHSs have been successfully applied to non-contact sensing human activities and remote real-time humidity monitoring via Bluetooth transmission. In addition, the deposited humidity-sensitive GMs and QCM substrate are fully recycled and reused (72% of the original value). This work has provided an innovative idea to construct environmental-friendly, high-sensitivity, and wireless humidity sensors.

Unmanned aerial vehicle (UAV) formation rendezvous path planning problem is one of the important research topics in multiple UAV (multi-UAV) coordinated path planning. Aiming at solving low computational efficiency and poor scalability of the traditional multi-UAV path planning method, the decentralized multi-UAV path planning method suitable for obstacle environments is proposed. Firstly, the UAV rendezvous path planning problem with constraints such as the kinematics of UAVs and collision-free constraints is modeled as a non-convex optimal control problem. To minimize formation rendezvous time and energy consumption, a two-layer coordinative framework is developed to solve this problem. In the coordination layer, relying only on the information of neighboring UAVs, each UAV in the decentralized communication graph negotiates the desired flight time using a consensus protocol to achieve coordination among UAVs. In the planning layer, the initial non-convex formation rendezvous path planning problem is decoupled into several sub-problems, which can be solved in parallel by path planners distributed on each UAV using sequential convex programming. Finally, numerical simulations are carried out to verify the effectiveness and scalability of the proposed method. The results show that this decentralized multi-UAV path planning method can handle the minimum-time rendezvous path planning problem and optimize the energy consumption in flight, and the computing time does not increase significantly with the enlargement of the UAV swarm. This decentralized framework scales well with the number of UAVs and can be applied for future urban flight and supplies delivery tasks.INDEX TERMS Unmanned aerial vehicle, path planning, decentralized coordination, sequential convex programming, collision-free.

In recent years, the performance of deep learning in image steganalysis applications has become more and more outstanding, but at the same time the training time has also greatly increased. Some models need to be trained for several days, and the research efficiency is very low. In this article, we propose an image steganalysis model in spatial domain based on a three-layer convolutional neural network. The model does not use a pooling layer, and uses the global average pooling layer instead of the fully connected layer. Experimental results show that the training time of the model is greatly shortened, and the accuracy of detecting the three steganography algorithms with an embedding rate of 0.4bpp exceeds 85%.

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