Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing

Multipath time-delay estimation is commonly encountered in radar and sonar signal processing. In some real-life environments, impulse noise is ubiquitous and significantly degrades estimation performance. Here, we propose a Bayesian approach to tailor the Bayesian Compressive Sensing (BCS) to mitigate impulsive noises. In particular, a heavy-tail Laplacian distribution is used as a statistical model for impulse noise, while Laplacian prior is used for sparse multipath modeling. The Bayesian learning problem contains hyperparameters learning and parameter estimation, solved under the BCS inference framework. The performance of our proposed method is compared with benchmark methods, including compressive sensing (CS), BCS, and Laplacian-prior BCS (L-BCS). The simulation results show that our proposed method can estimate the multipath parameters more accurately and have a lower root mean squared estimation error (RMSE) in intensely impulsive noise

LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map

This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for robust and fast localization. To achieve robust correspondence matching, we represent the points as a set of Gaussian distributions and evaluate the divergence in variance for outlier rejection. Based on the fitted distributions, a new residual metric is proposed for the filter-based Lidar inertial odometry, which demonstrates an improvement from merely quantifying distance to incorporating variance disparity, further enriching the comprehensiveness and accuracy of the residual metric. Due to the strategic design of the residual metric, we propose a simple yet effective voxel-solely mapping scheme, which only necessities the maintenance of one centroid and one covariance matrix for each voxel. Experiments on different datasets demonstrate the robustness and accuracy of our framework for various data inputs and environments. To the benefit of the robotics society, we open source the code at https://github.com/Ji1Xingyu/lio_gvm

Effects of fully open-air [CO2] elevation on leaf photosynthesis and ultrastructure of Isatis indigotica Fort

Traditional Chinese medicine relies heavily on herbs, yet there is no information on how these herb plants would respond to climate change. In order to gain insight into such response, we studied the effect of elevated [CO2] on Isatis indigotica Fort, one of the most popular Chinese herb plants. The changes in leaf photosynthesis,chlorophyll fluorescence, leaf ultrastructure and biomass yield in response to elevated [CO2] (550619 mmol mol–1) were determined at the Free-Air Carbon dioxide Enrichment (FACE) experimental facility in North China. Photosynthetic ability of I. indigotica was improved under elevated [CO2]. Elevated [CO2] increased net photosynthetic rate (PN), water use efficiency (WUE) and maximum rate of electron transport (Jmax) of upper most fully-expended leaves, but not stomatal conductance (gs), transpiration ratio (Tr) and maximum velocity of carboxylation (Vc,max). Elevated [CO2] significantly increased leaf intrinsic efficiency of PSII (Fv’/Fm’) and quantum yield of PSII(WPSII), but decreased leaf non-photochemical quenching (NPQ), and did not affect leaf proportion of open PSII reaction centers (qP) and maximum quantum efficiency of PSII (Fv/Fm). The structural chloroplast membrane, grana layer and stroma thylakoid membranes were intact under elevated [CO2], though more starch grains were accumulated within the chloroplasts than that of under ambient [CO2]. While the yield of I. indigotica was higher due to the improved photosynthesis under elevated [CO2], the content of adenosine, one of the functional ingredients in indigowoad root was not affected

An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator

With the wide application of intelligent manufacturing and the development of diversified functions of industrial manipulator, the requirements for the control accuracy and stability of the manipulator servo system are also increasing. The control of industrial manipulator is a time-varying system with nonlinear and strong coupling, which is often affected by uncertain factors, including parameter changing, environmental interference, joint friction and so on. Aiming at the problem of the poor control accuracy of the manipulator. Under the complex disturbance environment, control accuracy of the manipulator will be greatly affected, so this paper proposes an adaptive type-2 fuzzy sliding mode control (AT2FSMC) method applied to the servo control of the industrial manipulator, which realizes the adaptive adjustment of the boundary layer thickness to suppress the trajectory error caused by the external disturbance and weakens the chattering problem of the sliding mode control. The simulation results on a two-axis manipulator indicate that, with the presence of external disturbances, the proposed control method can help the manipulator maintain control signal stability and improve tracking accuracy. It also suppressed chattering produced by sliding mode control (SMC) and strengthening the robustness of the system. Compared with other conventional trajectory tracking control methods, the effectiveness of the proposed method can be reflected. Finally, the proposed method is tested in an actual manipulator to complete a practical trajectory to prove its feasibility

SOML: Sparse Online Metric Learning with Application to Image Retrieval

Ministry of Education, Singapore under its Academic Research Funding Tier

Recent Advances in RecBole: Extensions with more Practical Considerations

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update requests. This motivates us to make some significant improvements on our library, so as to meet the user requirements and contribute to the research community. In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole. In general, we focus on the flexibility and efficiency of RecBole in the past few months. More specifically, we have four development targets: (1) more flexible data processing, (2) more efficient model training, (3) more reproducible configurations, and (4) more comprehensive user documentation. Readers can download the above updates at: https://github.com/RUCAIBox/RecBole.Comment: 5 pages, 3 figures, 3 table

Porcine RIG-I and MDA5 Signaling CARD Domains Exert Similar Antiviral Function Against Different Viruses

The RIG-I-like receptors (RLRs) RIG-I and MDA5 play critical roles in sensing and fighting viral infections. Although RIG-I and MDA5 have similar molecular structures, these two receptors have distinct features during activation. Further, the signaling domains of the N terminal CARD domains (CARDs) in RIG-I and MDA5 share poor similarity. Therefore, we wonder whether the CARDs of RIG-I and MDA5 play similar roles in signaling and antiviral function. Here we expressed porcine RIG-I and MDA5 CARDs in 293T cells and porcine alveolar macrophages and found that MDA5 CARDs exhibit higher expression and stronger signaling activity than RIG-I CARDs. Nevertheless, both RIG-I and MDA5 CARDs exert comparable antiviral function against several viruses. Transcriptome analysis showed that MDA5 CARDs are more effective in regulating downstream genes. However, in the presence of virus, both RIG-I and MDA5 CARDs exhibit similar effects on downstream gene transcriptions, reflecting their antiviral function