Low Rank Properties for Estimating Microphones Start Time and Sources Emission Time

The absence of unknown timing information about the microphones recording start time and the sources emission time presents a challenge in several applications, including joint microphones and sources localization. Compared with traditional optimization methods that try to estimate unknown timing information directly, low rank property (LRP) contains an additional low rank structure that facilitates a linear constraint of unknown timing information for formulating corresponding low rank structure information, enabling the achievement of global optimal solutions of unknown timing information with suitable initialization. However, the initialization of unknown timing information is random, resulting in local minimal values for estimation of the unknown timing information. In this paper, we propose a combined low rank approximation method to alleviate the effect of random initialization on the estimation of unknown timing information. We define three new variants of LRP supported by proof that allows unknown timing information to benefit from more low rank structure information. Then, by utilizing the low rank structure information from both LRP and proposed variants of LRP, four linear constraints of unknown timing information are presented. Finally, we use the proposed combined low rank approximation algorithm to obtain global optimal solutions of unknown timing information through the four available linear constraints. Experimental results demonstrate superior performance of our method compared to state-of-the-art approaches in terms of recovery rate (the number of successful initialization for any configuration), convergency rate (the number of successfully recovered configurations), and estimation errors of unknown timing information.Comment: 13 pages for main content; 9 pages for proof of proposed low rank properties; 13 figure

A review of UAV autonomous navigation in GPS-denied environments

Unmanned aerial vehicles (UAVs) have drawn increased research interest in recent years, leading to a vast number of applications, such as, terrain exploration, disaster assistance and industrial inspection. Unlike UAV navigation in outdoor environments that rely on GPS (Global Positioning System) for localization, indoor navigation cannot rely on GPS due to the poor quality or lack of signal. Although some reviewing papers particularly summarized indoor navigation strategies (e.g., Visual-based Navigation) or their specific sub-components (e.g., localization and path planning) in detail, there still lacks a comprehensive survey for the complete navigation strategies that cover different technologies. This paper proposes a taxonomy which firstly classifies the navigation strategies into Mapless and Map-based ones based on map usage and then, respectively categorizes the Mapless navigation into Integrated, Direct and Indirect approaches via common characteristics. The Map-based navigation is then split into Known Map/Spaces and Map-building via prior knowledge. In order to analyze these navigation strategies, this paper uses three evaluation metrics (Path Length, Deviation Rate and Exploration Efficiency) according to the common purposes of navigation to show how well they can perform. Furthermore, three representative strategies were selected and 120 flying experiments conducted in two reality-like simulated indoor environments to show their performances against the evaluation metrics proposed in this paper, i.e., the ratio of Successful Flight, the Mean time of Successful Flight, the Mean Length of Successful Flight, the Mean time of Flight, and the Mean Length of Flight. In comparison to the CNN-based Supervised Learning (directly maps visual observations to UAV controls) and the Frontier-based navigation (necessitates continuous global map generation), the experiments show that the CNN-based Distance Estimation for navigation trades off the ratio of Successful Flight and the required time and path length. Moreover, this paper identifies the current challenges and opportunities which will drive UAV navigation research in GPS-denied environments

The HDIN Dataset: A Real-World Indoor UAV Dataset with Multi-Task Labels for Visual-Based Navigation

Supervised learning for Unmanned Aerial Vehicle (UAVs) visual-based navigation raises the need for reliable datasets with multi-task labels (e.g., classification and regression labels). However, current public datasets have limitations: (a) Outdoor datasets have limited generalization capability when being used to train indoor navigation models; (b) The range of multi-task labels, especially for regression tasks, are in different units which require additional transformation. In this paper, we present a Hull Drone Indoor Navigation (HDIN) dataset to improve the generalization capability for indoor visual-based navigation. Data were collected from the onboard sensors of a UAV. The scaling factor labeling method with three label types has been proposed to overcome the data jitters during collection and unidentical units of regression labels simultaneously. An open-source Convolutional Neural Network (i.e., DroNet) was employed as a baseline algorithm to retrain the proposed HDIN dataset, and compared with DroNet’s pretrained results on its original dataset since we have a similar data format and structure to the DroNet dataset. The results show that the labels in our dataset are reliable and consistent with the image samples

Bestatin, an Inhibitor of Aminopeptidases, Provides a Chemical Genetics Approach to Dissect Jasmonate Signaling in Arabidopsis

Bestatin, a potent inhibitor of some aminopeptidases, was shown previously to be a powerful inducer of wound-response genes in tomato (Lycopersicon esculentum). Here, we present several lines of evidence showing that bestatin specifically activates jasmonic acid (JA) signaling in plants. First, bestatin specifically activates the expression of JA-inducible genes in tomato and Arabidopsis (Arabidopsis thaliana). Second, the induction of JA-responsive genes by bestatin requires the COI1-dependent JA-signaling pathway, but does not depend strictly on JA biosynthesis. Third, microarray analysis using Arabidopsis whole-genome chip demonstrates that the gene expression profile of bestatin-treated plants is similar to that of JA-treated plants. Fourth, bestatin promotes a series of JA-related developmental phenotypes. Taken together, the unique action mode of bestatin in regulating JA-signaled processes leads us to the hypothesis that bestatin exerts its effects through the modulation of some key regulators in JA signaling. We have employed bestatin as an experimental tool to dissect JA signaling through a chemical genetic screening, which yielded a collection of Arabidopsis bestatin-resistant (ber) mutants that are insensitive to the inhibitory effects of bestatin on root elongation. Further characterization efforts demonstrate that some ber mutants are defective in various JA-induced responses, which allowed us to classify the ber mutants into three phenotypic groups: JA-insensitive ber mutants, JA-hypersensitive ber mutants, and mutants insensitive to bestatin but showing normal response to JA. Genetic and phenotypic analyses of the ber mutants with altered JA responses indicate that we have identified several novel loci involved in JA signaling