The vascular cambium activity of trees directly affects the yield and quality of forest products. Therefore, studying the laws governing vascular cambium activity in Cryptomeria fortunei, a species mainly used for woo...
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The vascular cambium activity of trees directly affects the yield and quality of forest products. Therefore, studying the laws governing vascular cambium activity in Cryptomeria fortunei, a species mainly used for wood production, is important. miRNAs regulate target gene expression and play an important role in plant growth and development, but the role of miRNAs in C. fortunei vascular cambium growth remains poorly understood. Here, small RNA (sRNA) and degradome libraries were constructed for C. fortunei vascular cambium at five different growth stages. A total of 1064 miRNAs (859 known and 205 novel miRNAs), including 266 differentially expressed miRNAs (DEMs), were identified by sRNA sequencing, and 4215 targets of 843 miRNAs were identified by degradome analysis. These targets were enriched in metabolism (e.g., phenylpropanoid biosynthesis), genetic information processing and signal transduction (e.g., plant hormone signal transduction) pathways during vascular cambium development. We obtained 365 differentially expressed target pairs and constructed a regulatory network of 111 annotated negatively regulated target pairs. The blue module was identified as a key module through weighted DEM coexpression network analysis, and seven negatively regulated target pairs (pabmiR159g/aly-miR397a-5p/pab-miR397a/novel23_mature-MYB, novel12_star-UGT72B1 and smo-miR1083/pabmiR1083-GARP-ARR-B) were considered as key regulators of C. fortunei vascular cambium growth. This study provides important miRNA and target data related to C. fortunei vascular cambium growth. These results increase the understanding of the posttranscriptional regulatory mechanisms of vascular cambium development in conifers and provide important references for the genetic improvement of wood production in plants.
Due to the dynamic motion of the sea, radar detection of small maritime targets can be difficult. Airborne maritime surveillance platforms are also increasingly required to operate at higher altitudes where backscatte...
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ISBN:
(纸本)9781728168135
Due to the dynamic motion of the sea, radar detection of small maritime targets can be difficult. Airborne maritime surveillance platforms are also increasingly required to operate at higher altitudes where backscatter from the sea becomes stronger. The focus of this paper is to investigate three sparse signal separation formulations using the short time Fourier transform as a dictionary. This approach has been demonstrated as effective in separating both stationary and moving targets from sea clutter, but relies on the tuning of different parameters. The first part of this work looks at the selection of the penalty parameter, which is essential to achieve good separation. Then a number of practical detection schemes are presented that allow control of the false alarm rate. The algorithm performance is demonstrated using Monte-Carlo simulation with synthetic targets injected into the Ingara medium grazing angle sea-clutter data set.
Ground penetrating radar (GPR) detection is a popular technology in civil engineering. Because of its advantages of non-destructive testing (NDT) and high work efficiency, GPR is widely used to detect hard foreign obj...
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Ground penetrating radar (GPR) detection is a popular technology in civil engineering. Because of its advantages of non-destructive testing (NDT) and high work efficiency, GPR is widely used to detect hard foreign objects in soil. However, the interpretation of GPR images relies heavily on the work experience of researchers, which may lead to problems of low detection efficiency and a high false recognition rate. Therefore, this paper proposes a real-time detection technology of GPR based on deep learning for the application of soil foreign object detection. In this study, the GPR image signal is obtained in real time by the GPR instrument and software, and the image signals are preprocessed to improve the signal-to-noise ratio of the GPR image signals and improve the image quality. Then, in view of the problem that YOLOv5 poorly detects smalltargets, this study improves the problems of false detection and missed detection in real-time GPR detection by improving the network structure of YOLOv5, adding an attention mechanism, data enhancement, and other means. Finally, by establishing a regression equation for the position information of the ground penetrating radar, the precise localization of the foreign matter in the underground soil is realized.
Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. I...
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With the increasingly extensive use of diver and unmanned underwater vehicle in military, it has posed a serious threat to the security of the national coastal area. In order to prevent the underwater diver's impa...
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ISBN:
(纸本)9781728154466
With the increasingly extensive use of diver and unmanned underwater vehicle in military, it has posed a serious threat to the security of the national coastal area. In order to prevent the underwater diver's impact on the safety of water area, it is of great significance to identify underwater smalltargets in time to make early warning for it In this paper, convolutional neural network is applied to underwater small target recognition. The recognition targets are diver, whale and dolphin. Due to the time-frequency spectrum can reflect the essential features of underwater target, convolutional neural network can learn a variety of features of the acoustic signal through the image processed by the time-frequency spectrum, time-frequency image is input to convolutional neural network to recognize the underwater smalltargets. According to the study of learning rate and pooling mode, the network parameters and structure suitable for underwater small target recognition in this paper are selected The results of dataprocessing show that the method can identify underwater smalltargets accurately.
Target detection in the background of sea clutter is an important part of sea surface radar signalprocessing. The traditional detection of weak targets in sea clutter is based on the statistical characteristics of se...
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ISBN:
(纸本)9789811365041;9789811365034
Target detection in the background of sea clutter is an important part of sea surface radar signalprocessing. The traditional detection of weak targets in sea clutter is based on the statistical characteristics of sea clutter, which does not reflect the intrinsic dynamics of sea clutter. Therefore, the detection results are not ideal. Based on the chaotic characteristics of sea clutter, this dissertation reconstructs the space structure of the sea clutter and proposes an improved particle swarm optimization (PSO) algorithm based on adaptive time-varying weights and local search operators. This method was applied to the optimization learning of the parameters of the radial basis function (RBF) neural network kernel function. The method was validated by using McIX University in Canada to measure the sea clutter data with the target in the Dartmouth area using IPIX radar. The results showed that the PSO-RBF algorithm in the background of chaotic sea clutter has good predictability. Compared with the general radial basis neural network, the improved algorithm not only has fast convergence speed but also has high error accuracy.
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