Controllable preparation of ultrathin MXene nanosheets and their excellent QCM humidity sensing properties enhanced by fluoride doping.

A 2D ultrathin MXene nanosheet was prepared under controlled conditions and employed as a sensitive film to construct a QCM (quartz crystal microbalance) humidity sensor by a dip coating method. The MXene nanosheets were obtained by dislodging the element A from the MAX phase by a facile liquid phase etching method. The morphology and composition of the MXene nanosheets were characterized by means of a number of advanced instruments. It was found that the sample is an ultrathin graphene-like nanosheet. The sensing test results showed that the sensor has a 12.8 Hz/% RH sensitivity, 6 s and 2 s (@ 90%) response/recovery time, maximum humidity hysteresis of 1.16% RH, good stability, and selectivity. Finally, the enhanced humidity response mechanism of the MXene nanosheets was explored by density-functional theory (DFT) calculation and experimental verification. The DFT simulation together with comparison of fluoride-free sample revealed that F elements on the surface of the MXene nanosheets play a more important role in improving humidity responses than OH groups. The results present a new strategy to enhance humidity sensing performance of sensing materials by F- doping or decoration. Thus, the sensor has bright potential for humidity sensing.

A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration.

Radio frequency interference, which makes it difficult to produce high-quality radar spectrograms, is a major issue for micro-Doppler-based human activity recognition (HAR). In this paper, we propose a deep-learning-based method to detect and cut out the interference in spectrograms. Then, we restore the spectrograms in the cut-out region. First, a fully convolutional neural network (FCN) is employed to detect and remove the interference. Then, a coarse-to-fine generative adversarial network (GAN) is proposed to restore the part of the spectrogram that is affected by the interferences. The simulated motion capture (MOCAP) spectrograms and the measured radar spectrograms with interference are used to verify the proposed method. Experimental results from both qualitative and quantitative perspectives show that the proposed method can mitigate the interference and restore high-quality radar spectrograms. Furthermore, the comparison experiments also demonstrate the efficiency of the proposed approach.

Research and Verification of Convolutional Neural Network Lightweight in BCI.

With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.