Image Sensing and Processing with Convolutional Neural Networks

Convolutional neural networks are a class of deep neural networks that leverage spatial information, and they are therefore well suited to classifying images for a range of applications [...

Salicylic acid collaborates with gene silencing to tomato defense against tomato yellow leaf curl virus (TYLCV)

Antiviral research in plants has been focused on RNA silencing (i.e. RNA interference), and several studies suggest that salicylic acid (SA)-mediated resistance is a key part of plant antiviral defense. However, the antiviral defense mechanism of SA-mediation is still unclear, and several recent studies have suggested a connection between SA-mediated defense and RNA silencing, which needs further characterization in TYLCV infection. In this study, both SA-mediated defense and the RNA silencing mechanism were observed to play an important role in the antiviral response against TYLCV. First, we found that SA application enhanced the resistance to TYLCV in tomato plants. The expression of RNA-silencing-related genes, such as SlDCL1, SlDCL2, SlDCL4, SlRDR2, SlRDR3a, SlRDR6a, SlAGO1, and SlAGO4, were significantly triggered by exogenous SA application and inoculation with TYLCV, respectively. Furthermore, silencing of SlDCL2, SlDCL4 in tomato resulted in attenuated resistance to TYLCV, and reduced the expression of defense-related genes (SlPR1 and SlPR1b) in SA-mediated defense after infection with TYLCV, particularly in SlDCL2/SlDCL4-silenced plants. Taken together, we conclude that SA collaborates with gene silencing in tomato defense against TYLCV

FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels

This paper presents a fast lidar-inertial odometry (LIO) that is robust to aggressive motion. To achieve robust tracking in aggressive motion scenes, we exploit the continuous scanning property of lidar to adaptively divide the full scan into multiple partial scans (named sub-frames) according to the motion intensity. And to avoid the degradation of sub-frames resulting from insufficient constraints, we propose a robust state estimation method based on a tightly-coupled iterated error state Kalman smoother (ESKS) framework. Furthermore, we propose a robocentric voxel map (RC-Vox) to improve the system's efficiency. The RC-Vox allows efficient maintenance of map points and k nearest neighbor (k-NN) queries by mapping local map points into a fixed-size, two-layer 3D array structure. Extensive experiments are conducted on 27 sequences from 4 public datasets and our own dataset. The results show that our system can achieve stable tracking in aggressive motion scenes (angular velocity up to 21.8 rad/s) that cannot be handled by other state-of-the-art methods, while our system can achieve competitive performance with these methods in general scenes. Furthermore, thanks to the RC-Vox, our system is much faster than the most efficient LIO system currently published