Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Machine learning-based image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and Severe weather conditions affects image quality, which affects the ac-curacy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multi-scale inflation convolution and a multi-layer Convolutional Block Attention Module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insuffi-cient number of training samples to a certain extent and improved the accuracy of image seg-mentation. (2) This study designed a multilevel Gaussian noise data augmentation scheme to improve the network's ability to resist noise interference and achieve a more accurate segmenta-tion of images with different degrees of noise pollution. (3) The inclusion of a multi-scale inflation perceptron and multi-layer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

Background: LncRNA and pyroptosis play important roles in cancer development and tumor immune microenviroment. However, pyroptosis-related lncRNAs (PRLs) in ovarian cancer have not been identified and its impact on prognosis and immune response are not fully understood. Methods: Using pearson correlation analysis, PRLs were screened. Subsequently, we constructed a prognosis signature by using LASSO cox regression. In addition, the association between risk score and cancer immune environment was analyzed. Results: In TCGA-RNA-seq cohort (n=377), 32 prognostic PRLs were selected and a 7-gene signature were developed and had high accuracy in predicting the OS of ovarian cancer patients. Stratification analysis suggested that it might serve as an independent prognostic indicator. Except to clinical outcome, the signature was significantly associated with tumor immune microenvironment. Patients with high risk score exhibited lower infiltration abundance of MHC class Ⅰ cells, Type Ⅰ IFN response and immunotherapy response. In ovarian cancer, TYMSOS was highly expressed and its high expression was associated with worse OS. TYMSOS deletion in ovarian cancer cell lines inhibited the cell proliferation, invasion and migration, indicating that it might serve as a novel biomarker in ovarian cancer. Conclusions: The prognostic PRLs signature constructed in this work is available for prognostic prediction and immune microenvironment infiltration in ovarian cancer.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.

A novel scheme that can simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed. At the signal receiving unit (SRU), two echo signals and the reference signal are modulated by a Sagnac loop structure and sent to the central station (CS) for processing. At the CS, two low-frequency electrical signals are generated after polarization control and photoelectric conversion. The DFS without direction ambiguity and wide AOA measurement can be real-time acquired by monitoring the frequency and power of the two low-frequency electrical signals. In the simulation, an unambiguous DFS measurement with errors of ±3×10-3 Hz and a -90° to 90° AOA measurement range with errors of less than ±0.5° are realized. The safety and robustness of the system to environmental disturbance are improved, and it is more suitable for the modern electronic warfare system.