一种6~18 GHz宽带高精度有源移相器

设计了一种6 bit 6~18 GHz工作频段的宽带高精度有源移相器。片上集成了输入无源巴伦、逻辑编码器、RC多相滤波器、矢量合成单元、数控单元等。该移相器的设计采用55 nm CMOS工艺实现,芯片尺寸为1.29 mm×0.9 mm,移相器核心尺寸为1.02 mm×0.58 mm。后仿结果表明,在6~18 GHz频率范围内,增益误差RMS值小于1 dB,相位误差RMS值小于0.75°,输入回波损耗、输出回波损耗分别小于-8.5 dB、-8.9 dB,芯片总功耗为20.7 mW。该6 bit移相器的相对带宽为100%,覆盖C、X和Ku波段,适用于雷达探测等领域。     

A Novel Routing Algorithm with Bernoulli Sampling-based Link Quality Estimation in Wireless Sensor Networks

Wireless Personal Communications - Long term evolution (LTE) and LTE—Advanced are the promising broadband wireless technology that supports variety of services with higher data rate. To...     

视频合成孔径雷达双域联合运动目标检测方法

视频合成孔径雷达(SAR)具有高帧率成像能力,可作为地面运动目标探测的重要技术手段。经典SAR地面动目标显示(SAR-GMTI)依靠目标回波能量来实现动目标检测,同时动目标阴影亦可作为视频SAR动目标检测的重要途径。然而,由于动目标能量和阴影的畸变或涂抹,依靠单一方式难以实现稳健的动目标检测。该文基于目标能量和阴影的双域联合检测思想,分别通过快速区域卷积神经网络和航迹关联两种技术途径实现了视频SAR动目标联合检测,给出了机载实测数据处理结果,并进行了详细分析。该文方法充分利用目标阴影与能量的特征及空时信息,提升了机动目标检测的稳健性。      

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.     

全固态医用窄线宽皮秒激光器研究

输出窄线宽、超短脉冲的全固态激光器在医疗美容、加工业领域的发展潜力巨大。本文开展了被动调Q微片激光器的研究。采用LD泵浦Nd:Ce:YAG、Cr:YAG微片键合晶体的方案。激光器输出脉冲的重复频率为10 Hz,脉冲宽度被压缩至503 ps,最大脉冲能量为197μJ,能量的均方值稳定性为3%,输出激光峰值波长为1 064.53 nm,光谱线宽为0.06 nm。光束质量M2因子在x和y方向上分别为1.32和1.29。     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.