Energy-Efficient and Secure Resource Allocation for Multiple-Antenna NOMA with Wireless Power Transfer
Non-orthogonal multiple access (NOMA) is considered as one of the promising techniques for providing high data rates in the fifth generation mobile communication. By applying successive interference cancellation schemes and superposition coding at the NOMA receiver, multiple users can be multiplexed on the same subchannel. In this paper, we investigate resource allocation algorithm design for an OFDM-based NOMA system empowered by wireless power transfer. In the considered system, users who need to transmit data can only be powered by the wireless power transfer. With the consideration of an existing eavesdropper, the objective is to obtain secure and energy efficient transmission among mul…
Depth Enhancement by Fusion for Passive and Active Sensing
This paper presents a general refinement procedure that enhances any given depth map obtained by passive or active sensing. Given a depth map, either estimated by triangulation methods or directly provided by the sensing system, and its corresponding 2-D image, we correct the depth values by separately treating regions with undesired effects such as empty holes, texture copying or edge blurring due to homogeneous regions, occlusions, and shadowing. In this work, we use recent depth enhancement filters intended for Time-of-Flight cameras, and adapt them to alternative depth sensing modalities, both active using an RGB-D camera and passive using a dense stereo camera. To that end, we propose …
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organised as a workshop in conjunction with ECCV 2020. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. Challenge 1 is further split into two tracks, focusing, first, on large body and clothing regions, and, second, on fine body details. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction and the amount of completed data. Additionally, two unique da…