Huan Tang

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This work studies the problem of neighbor discovery for device-to-device (D2D) communications of LTE user equipments (UEs) in a modern cellular network. By listening to cellular uplink transmissions, UEs can detect potential D2D partners through a neighbor discovery process compatible with the standard LTE network protocol. We focus on neighbor discovery(More)
—A cognitive network refers to the one where two overlaid structures, called primary and secondary networks coexist. The primary network consists of primary nodes who are licensed spectrum users while the secondary network comprises unauthorized users that have to access the licensed spectrum opportunistically. In this paper, we study the percolation degree(More)
The leaf area index (LAI) is a crucial parameter of vegetation structure. It provides key information for earth surface process simulations and climate change research on the global and regional scales. Focusing on the meadow steppe in Hulunber, Inner Mongolia, China, the present study assessed the accuracy of the Moderate Resolution Imaging(More)
The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were(More)
On 12 May 2008, the 8.0-magnitude Wenchuan earthquake occurred in Sichuan Province, China, triggering thousands of landslides, debris flows, and barrier lakes, leading to a substantial loss of life and damage to the local environment and infrastructure. This study aimed to monitor the status of geologic hazards and vegetation recovery in a post-earthquake(More)
Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes(More)