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A flexible wireless dielectric sensor is presented here for noninvasively monitoring the permittivity and conductivity of fluids, based on resistor–inductor–capacitor (RLC) resonant circuit and capacitively coupled contactless conductivity detection (C4D) technique. The RLC sensor consists of one single-turn inductor and one interdigital capacitor. The resonant frequency of the device is sensitive to the surrounding environment, thanks to the electric field leaked out between the interdigital capacitor electrodes. Through the high-frequency structure simulator (HFSS) simulation, and experiments on ethanol/water solutions and NaCl solutions, it was confirmed that a fluid’s permittivity and conductivity could be detected by the return loss curve (S11). With great repeatability and stability, the proposed sensor has potential for broad applications, especially in wearable low-cost smart devices.

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.