Search

Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority. [ABSTRACT FROM AUTHOR]

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

5-Phospho-d-ribosyl-1-diphosphate (PRPP) synthase (PRS) catalyzes the biosynthesis of PRPP, which is an important compound of metabolism in most organisms. However, no PRS genes have been cloned, let alone studied for their biological function in rubber tree. In this study, we identify a novel protein (PRS4) that interacts in vivo with rubber tree anaphase promoting complex/cyclosome (APC/C) subunit 10 (HbAPC10) by yeast two-hybrid assays. PRS4 has been cloned from rubber tree and named as HbPRS4 . Blastp search in the genome of Arabidopsis thaliana showed that HbPRS4 shared the highest similarity with AtPRS4, with 80.71% identity. qRT-PCR was used to determine the expression of HbPRS4 in different tissues and under various treatments. HbPRS4 was preferentially expressed in the bark. Moreover, the expression level of HbPRS4 was significantly induced by the proteasome inhibitor MG132 treatment, suggesting it might be regulated by the ubiquitin/26S proteasome pathway. The amount of HbPRS4 transcript was obviously decreased after mechanical wounding and abscisic acid (ABA) treatments, while a slight increase was observed at 24 h after ABA treatment. HbPRS4 transcript in the latex was significantly upregulated by ethephon (ET) and methyl jasmonate (MeJA) treatments. These results suggested that HbPRS4 may be a specific substrate of HbAPC10 indirectly regulating natural rubber biosynthesis in rubber tree.