A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm.

Feature pyramid mechanism has improved the performance of object detectors by a large margin, especially for the objects with small scale. As one of the first attempts to use pyramidal feature hierarchy, Single Shot MultiBox Detector (SSD) has largely accelerated the pipeline of detection with the competitive performance. Even if the feature pyramid mechanism is used for better detecting objects with small scale, the complex background information still misleads the network to focus on invalid areas. Image areas where small objects exist are more easily to be misjudged which influences the performance of lowerlevel layer in feature pyramid. In addition, a great deal of false positives may also be introduced into each pyramid layer because of the complex background. In this paper, we propose a novel method named scale pyramid attention hierarchy to better assist the SSD. The general feature pyramid detector can be guided to focus on the valid image areas as well as neglect the influence of complex background with the assist of this proposed module. Furthermore, the end-to-end training can highlight the foreground information which reduces the pressure of detectors, especially for detecting small objects. Flexible implementation makes it easily embedded in the feature pyramid mechanism. Experimental results on PASCAL VOC 2007, VOC 2012 and MS COCO confirm the effectiveness of the proposed module.

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Deep neural networks have witnessed great success in Single Image Super-Resolution (SISR). However, current improvements are mainly contributed by much deeper networks, which leads to huge computation cost and limited application for mobile devices. Moreover, most existing methods propagate the basic content of low-resolution images forward to deeper layers iteratively. Such duplicate computations inevitably result in inefficient reconstruction. To address this issue, we propose an efficient channel segregation block containing multiple branches with different depths, enabling the model to preserve basic content, and focusing on optimizing the detail content with fewer parameters. By merging the output of segregated branches, the block covers a large range of receptive fields. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods even with fewer parameters and lower computational complexity, which is more applicable to lightweight scenarios.

Existing part-based models for person Re-IDentification(Re-ID) usually suffer from part-misalignment problem caused by uniform partition of feature maps. The performances of part-based model are highly dependent on the semanticallyaligned parts of the query and gallery images. However, misalignments occur very commonly in person Re-ID tasks due to the variations of viewpoints and object distances. To address the part-misalignment problem and learn a more discriminative embedding for person Re-ID, we propose a novel Adaptive Part-based Model (APM), which adaptively partition the extracted feature maps by a Partition-Aware module to learn an embedding. The proposed adaptive partition method is very robust to the variations of the pedestrian scale and effective in resolving the part-misalignment problem. Experimental results on three commonly used datasets, including Market-1501, DukeMTMC-reID and CUHK03, clearly demonstrate that the proposed method achieves the state-ofthe-art performance.