This paper presents a metric that achieves good results in optimizing the placement, power, and orientation of CDMA2000 1xEV-DO base stations that provide wireless airto-ground (ATG) communications to commercial and general aviation aircraft. A network optimized using simulated annealing (SA) and the aforementioned metric is compared against one done by hand and traditional network planning methods. Results on a realistic network show that gains of 65% in the median user experience are possible through optimization around this metric.Index Terms-Aircraft communication, air-to-ground, code division multiple access (CDMA), and simulated annealing.
In recent years, compact and efficient scene understanding representations have gained popularity in increasing situational awareness and autonomy of robotic systems. In this work, we illustrate the concept of a panoptic edge segmentation and propose PENet, a novel detection network called that combines semantic edge detection and instancelevel perception into a compact panoptic edge representation. This is obtained through a joint network by multi-task learning that concurrently predicts semantic edges, instance centers and offset flow map without bounding box predictions exploiting the cross-task correlations among the tasks. The proposed approach allows extending semantic edge detection to panoptic edge detection which encapsulates both category-aware and instanceaware segmentation. We validate the proposed panoptic edge segmentation method and demonstrate its effectiveness on the real-world Cityscapes dataset.
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