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

A Fume Concentration Model of Underground Mine Fire and Its Calculation

AbstractIn order to obtain fume concentration distribution of underground mine fire spread process, a fume concentration calculation model, which takes into accounts into the effect of the fume's diffusion effect and the transport effect of the airflow, is established with the gradient transfer theory. The corresponding calculation steps are put forward. Then through calculating the fire fume concentration of a typical underground mine, the risk range of certain time after the fire accident can be given according to related standards. The results verify the validity and accuracy of the model and can provide references for the evacuation and rescue of underground mine fire accident