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This paper presents the development of a new compact six-axis compliant stage employing piezoelectric actuators to achieve six-axis actuation with nanometer resolution. The integration of direct metrology in the object space, based on real-time visual feedback, enables high-precision motion control. In order to achieve greater motion range, the simple and compact decoupled mechanical structure utilizes two-tap displacement amplifiers for in-plane motion and semibridge amplifiers for out-of-plane motion. The kinematic analysis of the stage is presented. Laterally sampled white light interferometry was implemented to measure the out-of-plane motion of the stage, and a measurement model associated with the designed target patterns is developed to estimate the in-plane motion in real time. Together, they form a visual tracking system and are integrated with the six-axis compliant stage to realize precision six-axis real-time visual servo-control. Experimental results demonstrate that the six-axis compliant stage has the motion range of 77.42 microm, 67.45 microm, 24.56 microm, 0.93 mrad, 0.95 mrad, and 3.10 mrad, and the resolution of +/-5 nm, +/-8 nm, +/-10 nm, +/-10 murad, +/-10 murad, and +/-20 murad for x-axis, y-axis, and z-axis translation and rotation, respectively.

In contrast to the oriented bounding boxes, point set representation has great potential to capture the detailed structure of instances with the arbitrary orientations, large aspect ratios and dense distribution in aerial images. However, the conventional point set-based approaches are handcrafted with the fixed locations using points-to-points supervision, which hurts their flexibility on the fine-grained feature extraction. To address these limitations, in this paper, we propose a novel approach to aerial object detection, named Oriented RepPoints. Specifically, we suggest to employ a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects, which is able to automatically arrange themselves over the object in a spatial and semantic scenario. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box. Moreover, we introduce an effective quality assessment measure to select the point set samples for training, which can choose the representative items with respect to their potentials on orientated object detection. Furthermore, we suggest a spatial constraint to penalize the outlier points outside the ground-truth bounding box. In addition to the traditional evaluation metric mAP focusing on overlap ratio, we propose a new metric mAOE to measure the orientation accuracy that is usually neglected in the previous studies on oriented object detection. Experiments on three widely used datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that our proposed approach is effective.

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The purposes of this article are to explore the challenges the Chinese health care system will be facing in the next decade. The recent outbreak of coronavirus disease (COVID-19) having infected more than 90 000 persons in China (Source: World Health Organization, WHO Coronavirus Disease Dashboard) again reveals the weaknesses of the fragmental health care system. Over the past 3 decades, increasing out-of-pocket spending on health care, increasing mortality rate of chronic disease, growing disparities between rural and urban populations, the defectiveness of disease surveillance system, and disease outbreak response system have been pressing Chinese authorities for action. As this country has experienced an unprecedented economic growth along with an unparalleled development of health care system in the past 3 decades, the challenges ahead are unavoidably numerous and complex.