The security threats caused by the popularity of Unmanned Aerial Vehicles (UAVs) have received much attention. In this paper, a UAV detection and identification system based on WiFi signal and radio frequency (RF) fingerprint is proposed. The system firstly conducts UAV detection and after the UAV is detected, fractal dimension (FD), axially integrated bispectra (AIB), and square integrated bispectra (SIB) are extracted as UAV RF fingerprints due to their applicability and reliability. Furthermore, we propose weighted AIB and SIB fingerprints to identify UAVs. Since the high dimensionality of AIB and SIB features, the principal component analysis (PCA) algorithm is applied to reduce the dimensionality of these two features. Then the neighborhood component analysis (NCA) algorithm is utilized to weight the dimensions of the two features, AIB and SIB. The extracted UAV fingerprints are stored as training data and test data, respectively. Finally, machine learning algorithms are utilized to identify UAVs. The identification results are as follows: In the indoor scenario, the average identification accuracy of three features (FD, AIB, SIB) are 100%, 97.23%, and 96.11%, respectively. In outdoor scenario, the identification accuracy of three features are 100%, 95.00%, and 93.50%, respectively.

A novel low‐energy hybrid capacitor switching scheme for a low‐power successive approximation register (SAR) analogue‐to‐digital converter (ADC) is presented. The proposed switching scheme combines a new switch method and the monotonic technique. The new switch method can achieve no switching energy consumption in the first three comparison cycles. Furthermore, a low‐energy monotonic procedure is performed for the rest of the comparisons. The average switching energy is reduced by 98.83% compared with the conventional architecture, resulting in the most energy‐efficient switching scheme among the existing switching techniques. Besides the significant energy saving, the proposed switching scheme also achieves a 75% reduction of the capacitors over the conventional scheme.

Human activity recognition has been growing for decades in a variety of technological disciplines. However, in the existing WiFi-based human activity recognition systems, there are the following problems: Firstly, in the processing of channel state information (CSI) data, mainly for the removal of noise in the superimposed signal, there is no effective removal of useless multipath signals; Secondly, the data segmentation algorithm based on the empirical threshold requires manual adjustment of the threshold in different environments, resulting in poor robustness and unstable segmentation; Thirdly, simple learning classification is applied without specific design for CSI data structure and sufficiently abstracting information features. In this paper, a device-free human activity recognition system with a temporal-frequency attention mechanism is proposed, which can be deployed on commercial WiFi devices to identify human’s daily activities. Firstly, the multipath signal affected by the channel change is extracted by using the difference of the propagation delay of different multipath, thereby eliminating the delay and invalid multipath signals that have undergone multiple reflections and refractions. Secondly, a neural network model based on attention mechanism is proposed, which assigns different weights to different characteristics and sequences by imitating the human brain to dedicate more attention to important information. Then, the long short-term memory (LSTM) model is used to learn the correlation features of different dimensions to realize human activity recognition. Finally, the system performance is evaluated in different environments, and the experimental results show that our syetem holds a better performance in both line-of-sight (LOS) and non-line-of-sight (NLOS) than the existing human activity recognition systems.

With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.

With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.

An energy‐efficient capacitor‐splitting digital‐to‐analogue converter (DAC) scheme with high‐accuracy for successive approximation register (SAR) analogue‐to‐digital converters (ADCs) is presented. The proposed method uses a split‐capacitive‐array DAC structure and optimises the switching energy during conversion using energy‐efficient ‘up’ transition. The proposed switching scheme achieves a 96.91% switching energy reduction and a 75% area reduction compared with the conventional method. In addition, the third reference voltage ( V _cm ) has no effect on the accuracy of the SAR ADC except the least significant bit, resulting in a good trade‐off between the energy‐efficiency and accuracy of the ADC.