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Creators/Authors contains: "Guo, Junpeng"

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  1. In this work, we present our attempt to tackle the last-hundred-feet problem for autonomous drone delivery.We take a computer-vision based approach to progressively landing towards a convenient and safe drop-off point at all times (here, at the front/garage door). Specifically, we develop structural semantic segmentation (SSS), a new technique that leverages a single-family house structure to streamline and enhance semantic segmentation in the drop-to-door problem context.We implement SSS into an Android app; Our preliminary evaluation in a residential zone shows SSS is promising to make autonomous drop-to-door in real-time, with no need to wait for slow visual processing. Video demo is available at Youtube [5]. App is released at Github [6]. 
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  2. In this work, we have conducted a measurement study with three US operators to reveal three types of problematic failure handling on secondary radio access which have not been reported before. Compared to primary radio access failures, secondary radio access failures do not hurt radio access availability but significantly impact data performance, particularly when 5G is used as secondary radio access to boost throughput. Improper failure handling results in significant throughput loss, which is unnecessary in most instances. Datasets are available at https:// github.com/mssn/ scgfailure. 
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  3. Edge-assisted video analytics is gaining momentum. In this work, we tackle an important problem to compress video content live streamed from the device to the edge without scarifying accuracy and timeliness of its video analytics. We find that on-device processing can be tuned over a larger configuration space for more video compression, which was largely overlooked. Inspired by our pilot study, we design VPPlus to fulfill the potentials to compress the video as much as we can, while preserving analytical accuracy. VPPlus incorporates two core modules – offline profiling and online adaptation – to generate proper feedback automatically and quickly to tune on-device processing. We validate the effectiveness and efficiency of VPPlususing five object detection tasks over two popular datasets; VPPlus outperforms the state-of-art approaches in almost all the cases. 
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