SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner

Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in practice is frequently challenging. Therefore, current SAR automatic target recognition (ATR) algorithms perform poorly with limited training data availability, resulting in a critical need to increase SAR ATR performance. In this study, a new method to improve SAR ATR when training data are limited is proposed. First, an embedded feature augmenter is designed to enhance the extracted virtual features located far away from the class center. Based on the relative distribution of the features, the algorithm pulls the corresponding virtual features with different strengths toward the corresponding class center. The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features. Second, a dynamic hierarchical-feature refiner is proposed to capture the discriminative local features of the samples. Through dynamically generated kernels, the proposed refiner integrates the discriminative local features of different dimensions into the global features, further enhancing the inner-class compactness and inter-class separability of the extracted features. The proposed method not only increases the amount of information available for supervised training but also extracts the discriminative features from the samples, resulting in superior ATR performance in problems with limited SAR training data. Experimental results on the moving and stationary target acquisition and recognition (MSTAR), OpenSARShip, and FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR performance of the proposed method in response to limited SAR training data

Integrated photonics modular arithmetic processor

Integrated photonics computing has emerged as a promising approach to overcome the limitations of electronic processors in the post-Moore era, capitalizing on the superiority of photonic systems. However, present integrated photonics computing systems face challenges in achieving high-precision calculations, consequently limiting their potential applications, and their heavy reliance on analog-to-digital (AD) and digital-to-analog (DA) conversion interfaces undermines their performance. Here we propose an innovative photonic computing architecture featuring scalable calculation precision and a novel photonic conversion interface. By leveraging Residue Number System (RNS) theory, the high-precision calculation is decomposed into multiple low-precision modular arithmetic operations executed through optical phase manipulation. Those operations directly interact with the digital system via our proposed optical digital-to-phase converter (ODPC) and phase-to-digital converter (OPDC). Through experimental demonstrations, we showcase a calculation precision of 9 bits and verify the feasibility of the ODPC/OPDC photonic interface. This approach paves the path towards liberating photonic computing from the constraints imposed by limited precision and AD/DA converters.Comment: 23 pages, 9 figure

SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier

Maritime surveillance is not only necessary for every country, such as in maritime safeguarding and fishing controls, but also plays an essential role in international fields, such as in rescue support and illegal immigration control. Most of the existing automatic target recognition (ATR) methods directly send the extracted whole features of SAR ships into one classifier. The classifiers of most methods only assign one feature center to each class. However, the characteristics of SAR ship images, large inner-class variance, and small interclass difference lead to the whole features containing useless partial features and a single feature center for each class in the classifier failing with large inner-class variance. We proposes a SAR ship target recognition method via selective feature discrimination and multifeature center classifier. The selective feature discrimination automatically finds the similar partial features from the most similar interclass image pairs and the dissimilar partial features from the most dissimilar inner-class image pairs. It then provides a loss to enhance these partial features with more interclass separability. Motivated by divide and conquer, the multifeature center classifier assigns multiple learnable feature centers for each ship class. In this way, the multifeature centers divide the large inner-class variance into several smaller variances and conquered by combining all feature centers of one ship class. Finally, the probability distribution over all feature centers is considered comprehensively to achieve an accurate recognition of SAR ship images. The ablation experiments and experimental results on OpenSARShip and FUSAR-Ship datasets show that our method has achieved superior recognition performance under decreasing training SAR ship samples

Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR

Although deep learning-based methods have achieved excellent performance on SAR ATR, the fact that it is difficult to acquire and label a lot of SAR images makes these methods, which originally performed well, perform weakly. This may be because most of them consider the whole target images as input, but the researches find that, under limited training data, the deep learning model can't capture discriminative image regions in the whole images, rather focus on more useless even harmful image regions for recognition. Therefore, the results are not satisfactory. In this paper, we design a SAR ATR framework under limited training samples, which mainly consists of two branches and two modules, global assisted branch and local enhanced branch, feature capture module and feature discrimination module. In every training process, the global assisted branch first finishes the initial recognition based on the whole image. Based on the initial recognition results, the feature capture module automatically searches and locks the crucial image regions for correct recognition, which we named as the golden key of image. Then the local extract the local features from the captured crucial image regions. Finally, the overall features and local features are input into the classifier and dynamically weighted using the learnable voting parameters to collaboratively complete the final recognition under limited training samples. The model soundness experiments demonstrate the effectiveness of our method through the improvement of feature distribution and recognition probability. The experimental results and comparisons on MSTAR and OPENSAR show that our method has achieved superior recognition performance

Effects of hyperbaric oxygen on vascular endothelial function in patients with slow coronary flow

   Background: To improve therapy for slow coronary flow (SCF), the effects of hyperbaric oxygen (HBO) therapy on vascular endothelial function in SCF patients is the focus of this investigation. Methods: Ninety-eight patients who exhibited chest discomfort were retrospectively analyzed, and di­agnosed with SCF by coronary artery angiography at the Third Hospital of Hebei Medical University, Shijiazhuang, China from 2014 to 2016. The patients were divided into two groups according to the following treatment: HBO group (n = 48) and the control group (n = 50). Patients in the control group were administrated with conventional treatment, while those in the HBO group were administrated HBO therapy for 4 weeks in addition to conventional treatment. To evaluate the effects of HBO on vas­cular endothelial functions, plasma levels of nitric oxide (NO), calcitonin gene-related peptide (CGRP), endothelin-1 (ET-1), high sensitivity C-reactive protein (hsCRP) as well as endothelial-dependent flow-mediated vasodilation (FMD) of the brachial artery were measured in both groups before and after their respective treatments. Results: There were no significant differences in plasma levels of NO, ET-1, CGRP, hsCRP nor in FMD measurements between the two groups before treatment (p > 0.05). Moreover, the levels of all the parameters measured showed no significant changes before and after treatment in the control group. However, when comparing the control group, FMD and plasma NO and CGRP levels were significantly increased in the HBO group after treatment (p < 0.01), whereas hsCRP and ET-1 levels decreased dramatically (p < 0.001). Conclusions: The HBO treatment in addition to conventional therapy may significantly improve the vascular endothelial function in SCF patients. (Cardiol J 2018; 25, 1: 106–112

An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR

Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen target classes, i.e., open set recognition (OSR) problem, which is an urgent problem for the practical SAR ATR. The key solution of OSR is to effectively establish the exclusiveness of feature distribution of known classes. In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes. Through meta-learning tasks, the proposed method learns to construct a feature space of the dynamic-assigned known classes. This feature space is required by the tasks to reject all other classes not belonging to the known classes. At the same time, the proposed entropy-awareness loss helps the model to enhance the feature space with effective and robust discrimination between the known and unknown classes. Therefore, our method can construct a dynamic feature space with discrimination between the known and unknown classes to simultaneously classify the dynamic-assigned known classes and reject the unknown classes. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of our method for SAR OSR

N-acetylcysteine Protects against Apoptosis through Modulation of Group I Metabotropic Glutamate Receptor Activity

The activation of group I metabotropic glutamate receptor (group I mGlus) has been shown to produce neuroprotective or neurotoxic effects. In this study, we investigated the effects of N-acetylcysteine (NAC), a precursor of the antioxidant glutathione, on group I mGlus activation in apoptosis of glial C6 and MN9D cell lines, and a rat model of Parkinson's disease (PD). We demonstrated that NAC protected against apoptosis through modulation of group I mGlus activity. In glial C6 cells, NAC promoted phosphorylation of ERK induced by (s)-3,5- dihydroxy-phenylglycine (DHPG), an agonist of group I mGlus. NAC enhanced the group I mGlus-mediated protection from staurosporine (STS)-induced apoptosis following DHPG treatment. Moreover, in rotenone-treated MN9D cells and PD rat model, NAC protected against group I mGlus-induced toxicity by compromising the decrease in phosphorylation of ERK, phosphorylation or expression level of TH. Furthermore, the results showed that NAC prohibited the level of ROS and oxidation of cellular GSH/GSSG (Eh) accompanied by activated group I mGlus in the experimental models. Our results suggest that NAC might act as a regulator of group I mGlus-mediated activities in both neuroprotection and neurotoxicity via reducing the oxidative stress, eventually to protect cell survival. The study also suggests that NAC might be a potential therapeutics targeting for group I mGlus activation in the treatment of PD