Backscatter communications will play an important role in connecting everything for beyond 5G (B5G) and 6G systems. One open challenge for backscatter communications is that the signals suffer a round-trip path loss so that the communication distance is short. In this paper, we first calculate the communication distance upper bounds for both uplink and downlink by measuring the tag sensitivity and reflection coefficient. It is found that the activation voltage of the envelope detection diode of the downlink tag is the main factor limiting the backscatter communication distance. Based on this analysis, we then propose to implement a low-noise amplifier (LNA) module before the envelope detection at the tag to enhance the incident signal strength. Our experimental results on the hardware platform show that our method can increase the downlink communication range by nearly 20 m.

This paper proposes a universal framework, termed as Multi-Task Hybrid Convolutional Neural Network (MHCNN), for joint face detection, facial landmark detection, facial quality, and facial attribute analysis. MHCNN consists of a high-accuracy single stage detector (SSD) and an efficient tiny convolutional neural network (T-CNN) for joint face detection refinement, alignment and attribute analysis. Though the SSD face detectors achieve promising results, we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes. By multi-task training, our T-CNN aims to provide five facial landmarks, facial quality scores, and facial attributes like wearing sunglasses and wearing masks. Since there is no public facial quality data and facial attribute data as we need, we contribute two datasets, namely FaceQ and FaceA, which are collected from the Internet. Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark (FDDB), and gets reasonable results on AFLW, FaceQ and FaceA.