Highly fluorescent nanotubes assembled from designed asymmetric perylene diimide molecules (PDIs) exhibit high sensitivity (lowering the existing detection limit to ppb levels) and selectivity to amines in the vapor phase, which renders them capable of monitoring and assessing the deterioration of meat.

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Light detection and ranging (LiDAR) sensors are receiving an increasing amount of attention in traffic detection because of their powerful capacity for providing accurate trajectory data of vehicles and non-motorized road users. When installed at the roadside, LiDAR faces the same occlusion problem as other over-roadway sensors (such as video cameras)—the integrity and reliability of object detection can be reduced when occlusion occurs. Existing occlusion reasoning methods are either developed for video sensors or require sensor fusion. Fast and accurate occlusion determination is an indispensable step in the process of vehicle detection, classification, and tracking. This paper presents a novel algorithm to automatically identify the occurrence of vehicle partial occlusion and the corresponding occluding/occluded relationship from roadside LiDAR data. According to the inherent characteristics of LiDAR sensors, two specifically designed heatmaps (ClusterID heatmap and Distance heatmap) were generated and used as the basis for fast occlusion search. Using the field data collected from two testbeds, it was shown that the proposed method could achieve about 95.60% accuracy for partial occlusion identification and a processing speed of 2 ms per frame (32-laser LiDAR sensor with 0.1 s per frame).

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