SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class

Learning to Generate SAR Images with Adversarial Autoencoder

Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from sparsely distributed training samples and rapid angular variations due to scattering scintillation. Thus, data-driven SAR target recognition is considered a typical few-shot learning (FSL) task. This paper first reviews the key issues of FSL and provides a definition of the FSL task. A novel adversarial autoencoder (AAE) is then proposed as a SAR representation and generation network. It consists of a generator network that decodes target knowledge to SAR images and an adversarial discriminator network that not only learns to discriminate “fake” generated images from real ones but also encodes the input SAR image back to a target knowledge. The discriminator employs progressively expanding convolution layers and a corresponding layer-by-layer training strategy. It uses two cyclic loss functions to enforce consistency between the inputs and outputs. Moreover, rotated cropping is introduced as a mechanism to address the challenge of representing the target orientation. The MSTAR 7-target dataset is used to evaluate the AAE’s performance, and the results demonstrate its ability to generate SAR images with aspect angular diversity. Using only 90 training samples with at least 25 degrees of orientation interval, the trained AAE is able to generate the remaining 1,748 samples of other orientation angles with an unprecedented level of fidelity. Thus, it can be used for data augmentation in SAR target recognition FSL tasks. Our experimental results show that the AAE could boost the test accuracy by 5.77%

Multi-sensor Image Data Fusion based on Pixel-Level Weights of Wavelet and the PCA Transform

Abstract -The goal of image fusion is to create new images that are more suitable for the purposes of human visual perception, object detection and target recognition. For Automatic Target Recognition (ATR), we can use multi-sensor data including visible and infrared images to increase the recognition rate. In this paper, we propose a new multiresolution data fusion scheme based on the principal component analysis (PCA) transform and the pixel-level weights wavelet transform including thermal weights and visual weights. In order to get a more ideal fusion result, a linear local mapping which based on the PCA is used to create a new "origin" image of the image fusion. We use multiresolution decompositions to represent the input images at different scales, present a multiresolution/ multimodal segmentation to partition the image domain at these scales. The crucial idea is to use this segmentation to guide the fusion process. Physical thermal weights and perceptive visual weights are used as segmentation multimodals. Daubechies Wavelet is choosen as the Wavelet Basis. Experimental results confirm that the proposed algorithm is the best image sharpening method and can best maintain the spectral information of the original infrared image. Also, the proposed technique performs better than the other ones in the literature, more robust and effective, from both subjective visual effects and objective statistical analysis results

Diagnostic Value of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Crohn’s Disease

The aim of this study is to investigate the diagnostic efficacy of neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-monocyte ratio (NMR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in patients with Crohn’s disease (CD) and non-CD controls. These ratios were all derived from complete blood counts. Two hundred and six participants including CD inpatients and non-CD controls were retrospectively enrolled. We found statistically higher NLR and PLR and lower LMR in CD patients than in non-CD controls (all P<0.01). However, NMR was not different between the two groups (P=0.18). In addition, NLR, PLR, and LMR were associated with CRP and ESR. Optimal cutoffs for NLR and PLR were 2.72 (sensitivity: 68.3%, specificity: 75.9%, and overall accuracy: 70.1%) and 132.88 (sensitivity: 76.7%, specificity: 84.8%, and overall accuracy: 80.8%), respectively. In conclusion, the NLR and PLR might be effective, readily available, and low-cost biomarkers for differentiating CD patients from non-CD controls

Prevalence of hyperuricemia and its related risk factors in healthy adults from Northern and Northeastern Chinese provinces

BACKGROUND: Hyperuricemia (HUA) is a potential risk factor for developing insulin resistance, hypertension, dyslipidemia and cardiovascular disease. Therefore, we studied the prevalence of HUA and associated risk factors in the population of two provinces in northern China. METHODS: Based on the research of Chinese Physiological Constant and Health Conditions conducted in 2008–2010, we enrolled 29,639 subjects in a randomized, stratified study in four sampling areas in Heilongjiang Province and the Inner Mongolia Autonomous Region. We collected 13,140 serum samples to determine biochemical indicators including uric acid(UA), glucose, blood lipids, liver function, and renal function, and finally a representative sample of 8439 aged 18 years and older was determined. We also defined and stratified HUA, hypertension, diabetes, obesity and lipid abnormalities according to international guidelines. RESULTS: There were significant differences in the UA levels between different genders and regions. The total prevalence of HUA is 13.7%. Men had a higher prevalence of HUA than women (21% vs. 7.9%; P < 0.0001). As age increased, HUA prevalence decreased in men but rose in women. The suburbs of big cities had the highest HUA prevalence (18.7%), and in high-prevalence areas the proportion of women with HUA also increased. A stepwise logistic regression model was used to filter out twelve HUA risk factors, including age, gender, residence, hypercholesterolemia, hypertriglyceridemia, impaired fasting glucose, hypertension, obesity, abdominal obesity, CKD, drinking and sleeping. After adjusting for these factors, the odds ratio of HUA was 1.92 times higher in men than in women. Compared with agricultural and pastoral areas, the odds ratio of having HUA was 2.14 for participants in the suburbs of big cities and 1.57 in the center of big cities. CONCLUSIONS: The prevalence of HUA is high in northern China. The differences in HUA prevalence by geographic region suggested that unbalanced economic development and health education, therefore HUA prevention measures should be strengthened to improve quality of life and reduce health care costs

A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III

We established a method on measuring the \dzdzb mixing parameter $y$ for BESIII experiment at the BEPCII $e^+e^-$ collider. In this method, the doubly tagged $\psi(3770) \to D^0 \overline{D^0}$ events, with one $D$ decays to CP-eigenstates and the other $D$ decays semileptonically, are used to reconstruct the signals. Since this analysis requires good $e/\pi$ separation, a likelihood approach, which combines the $dE/dx$, time of flight and the electromagnetic shower detectors information, is used for particle identification. We estimate the sensitivity of the measurement of $y$ to be 0.007 based on a $20fb^{-1}$ fully simulated MC sample.Comment: 6 pages, 7 figure