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Flexible and wearable healthcare sensors for visual reality health-monitoring

DOI:10.3724/SP.J.2096-5796.2019.0022

Accepted Date:2019-07-15

Abstract (7) | PDF (1)

Visual reality (VR) health-monitoring by flexible electronics provides a new avenue to remote and wearable medicine. The combination of flexible electronics and VR could makes smart remote disease diagnosis by real-timely monitoring the physiological signals and remote interaction between patient and physician. Flexible healthcare sensor is the most crucial unit in flexible and wearable health-monitoring system, which has attracted much attention in recent years. The paper briefly reviewed the progress in flexible healthcare sensors and VR healthcare devices. The flexible healthcare sensor was introduced with the basic flexible materials, manufacturing techniques, and their applications in health-monitoring (such as blood/sweat detections and heart rate tracking). The VR healthcare devices for telemedicine diagnosis have been discussed, and finally the smart remote diagnosis system by flexible and wearable healthcare sensors and VR device was discussed.

Edge vector based large graph visualization and interactive exploration

DOI:10.3724/SP.J.2096-5796.2019.0010

Accepted Date:2019-03-22

Abstract (92) | PDF (9)

The demand for graph analysis is increasing. High quality and high readability graph layout is important for graph analysis. In the past years, we investigate this topic and propose a unified framework for graph layout and exploration. This framework maintains the readability during layout and interaction process. It controls the edge lengths and directions instead of only lengths. We can model most existing layout constraints, as well as develop new ones. For interactive exploration on the detail of a graph, we extend our framework to a new focus + context fisheye view. Traditional fisheye views for exploring large graphs introduce substantial distortions that often lead to a decreased readability of paths and other interesting structures. We use edge directions as constraints for graph layout optimization allows us not only to reduce spatial and temporal distortions during fisheye zooms, but also to improve the readability of the graph structure. Furthermore, the framework enables us to optimize fisheye lenses towards specific tasks and design a family of new lenses. We implement our framework with GPU parallel computing, which allows us process large graphs with up to 10,000 nodes at interactive rates.