Continuous monitoring of electrocardiogram (ECG) signals plays an important role in the prevention and diagnosis of cardiovascular diseases. Conventional ECG signal measurement requires skin-contact electrodes, which are unfriendly to skin-sensitive, burn patients and infants. In this paper, we propose a non-contact ECG signal measurement method based on Frequency Modulated Continuous Wave (FMCW) radar, facilitated by a signal amplification network named RadarNet. Firstly, a multi-domain joint modulation micromotion signal disentanglement module (MSDM) is introduced to extract cardiac mechanical motion (CMM) signal from complex radar waveforms. Then, a signal reconstruction network RadarNet based on time-frequency domain transformation is proposed to learn the nonlinear relationship between CMM and ECG signal. Finally, the first annotated dataset with synchronized FMCW radarphysiological signal RadarPhys-30 is established, which contains 30 subjects in two physiological states (sitting and lying down). Experimental results on the RadarPhys-30 dataset demonstrate that the mean average error (MAE) and the root mean square error (RMSE) of the heart rate (HR) estimated from the reconstructed ECG signal are less than 0.20 beats per minute (bpm) and 0.53 bpm in the sitting state, and 0.15 bpm and 0.46 bpm in the lying down state, respectively. These results indicate that the proposed method achieves accurate non-contact ECG signal monitoring.

Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. However, it is possible that Eves are in the same direction as LUs in practical applications. As a result, signals received by Eves will be approximately the same or even in better quality than those received by LUs. To address the neighbor security issue, we introduce a multiple antenna arrays model at the transmitter side with the help of the artificial noise (AN)-aided DM technique to achieve secure and precise DM transmission in this paper. Meanwhile, to recover the mixed useful signals, two novel DM schemes based on single- and multi-carrier multiple antenna arrays model are proposed, respectively. In addition, the symbol error rate (SER), secrecy rate, and robustness performance of the proposed DM schemes were analyzed and simulated. Simulations validate the effectiveness of the proposed DM schemes and demonstrate that multiple antenna arrays model based DM methods outperform single antenna array model aided DM methods in security.

A dual-band broadband absorber based on frequency selective surface (FSS) is proposed. This double-layer absorber consists of two layers of FSS structure; each layer has a unique construction and plays its wave-absorbing function in different frequency bands. In the low-frequency band, layer I is a transmission channel, and layer II effectively absorbs electromagnetic waves. Furthermore, layer I exhibits significant absorption characteristics in the high-frequency band, whereas layer II provides effective reflection. The combination of the two layers results in a remarkably effective absorption. Its reflectivity below − 10 dB covers a bandwidth of 5.5–14.8 GHz and 22.9–33.1 GHz, and its fractional bandwidth is 128%. The structure prevents external electromagnetic interference from affecting the antenna’s performance, ensuring communication quality in critical frequency bands while reducing unwanted absorption in unwanted frequency bands. It is worth mentioning that the structure still has good stability under 45° oblique incidence. The physical mechanism is analyzed in detail by using an equivalent circuit model (ECM). The measurement results are in good agreement with those of simulation and ECM.

UAV is difficult to detect by visual methods at a long distance, so a UAV detection and tracking algorithm is proposed based on image super-resolution. Firstly, a saliency transformation algorithm is built to focus on the suspected area. Then, a generative adversarial network is established on the basis of ROI to realize the super-resolution of weak targets and restore the high-resolution details of target features. Finally, the cooperative attention module is built to recognize and track UAV. Our experiments show that the proposed algorithm has strong robustness.