375 research outputs found
LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
The local reference frame (LRF) acts as a critical role in 3D local shape
description and matching. However, most of existing LRFs are hand-crafted and
suffer from limited repeatability and robustness. This paper presents the first
attempt to learn an LRF via a Siamese network that needs weak supervision only.
In particular, we argue that each neighboring point in the local surface gives
a unique contribution to LRF construction and measure such contributions via
learned weights. Extensive analysis and comparative experiments on three public
datasets addressing different application scenarios have demonstrated that
LRF-Net is more repeatable and robust than several state-of-the-art LRF methods
(LRF-Net is only trained on one dataset). In addition, LRF-Net can
significantly boost the local shape description and 6-DoF pose estimation
performance when matching 3D point clouds.Comment: 28 pages, 14 figure
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent years, computer vision technologies have experienced a
significant breakthrough due to the emergence of large-scale datasets and
increased computational resources. Naturally image-based approaches have also
received much attention in plant-related studies. Yet a fact is that most
image-based systems for plant phenotyping are deployed under controlled
laboratory environment. When transferring the application scenario to
unconstrained in-field conditions, intrinsic and extrinsic variations in the
wild pose great challenges for accurate counting of maize tassels, which goes
beyond the ability of conventional image processing techniques. This calls for
further robust computer vision approaches to address in-field variations. This
paper studies the in-field counting problem of maize tassels. To our knowledge,
this is the first time that a plant-related counting problem is considered
using computer vision technologies under unconstrained field-based environment.Comment: 14 page
Security enhancement using a novel two-slot cooperative NOMA scheme
In this letter, we propose a novel cooperative non-orthogonal multiple access (NOMA) scheme to guarantee the secure transmission of a specific user via two time slots. During the first time slot, the base station (BS) transmits the superimposed signal to the first user and the relay via NOMA. Meanwhile, the signal for the first user is also decoded at the second user from the superimposed signal due to its high transmit power. In the second time slot, the relay forwards the signal to the second user while the BS retransmits the signal for the first user as interference to disrupt the eavesdropping. Due to the fact that the second user has obtained the signal for the first user in the first slot, the interference can be eliminated at the second user. To measure the performance of the proposed cooperative NOMA scheme, the outage probability for the first user and the secrecy outage probability for the second user are analyzed. Simulation results are presented to show the effectiveness of the proposed scheme
Hybrid Successive Interference Cancellation and Power Adaptation: a Win-Win Strategy for Robust Uplink NOMA Transmission
The aim of this paper is to reveal the importance of hybrid successive
interference cancellation (SIC) and power adaptation (PA) for improving
transmission robustness of uplink non-orthogonal multiple access (NOMA).
Particularly, a cognitive radio inspired uplink NOMA communication scenario is
considered, where one primary user is allocated one dedicated resource block,
while M secondary users compete with each other to be opportunistically served
by using the same resource block of the primary user. Two novel schemes are
proposed for the considered scenario, namely hybrid SIC with PA (HSIC-PA)
scheme and fixed SIC with PA (FSIC-PA) scheme. Both schemes can ensure that the
secondary users are served without degrading the transmission reliability of
the primary user compared to conventional orthogonal multiple access (OMA)
based schemes. Rigorous analytical results are presented to evaluate the
performance of the proposed two schemes. It is shown that both schemes can
avoid outage probability error floors without any constraints on users' target
rates in the high SNR regime. Furthermore, it is shown that the diversity gain
achieved by the HSIC-PA scheme is M, while that of the FISC-PA scheme is only
1. Numerical results are provided to verify the developed analytical results
and also demonstrate the superior performance achieved by the proposed schemes
by comparing with the existing HSIC without PA (HSIC-NPA) scheme. The presented
simulation results also show that HSIC-PA scheme performs the best among the
three schemes, which indicates the importance of the combination of HSIC and PA
for improving transmission robustness.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0151
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