Anchor-free SAR Ship Instance Segmentation with Centroid-distance Based Loss

Instance segmentation methods for synthetic aperture radar (SAR) ship imaging have certain unsolved problems. 1) Most of the anchor-based detection algorithms encounter difficulties in tuning the anchor-related parameters and high computational costs. 2) Different tasks share the same features without considering the differences between tasks, leading to mismatching of the shared features and inconsistent training targets. 3) Common loss functions for instance segmentation cannot effectively distinguish the positional relationships between ships with the same degree of overlap. In order to alleviate these problems, we first adopt a lightweight feature extractor and an anchor-free convolutional network, which effectively help to reduce computational consumption and model complexity. Second, to fully disseminate feature information, a dynamic encoder–decoder is proposed to dynamically transform the shared features to task-specific features in channel and spatial dimensions. Third, a novel loss function based on centroid distance is designed to make full use of the geometrical shape and positional relationship between SAR ship targets. In order to better extract features from SAR images in complex scenes, we further propose the dilated convolution enhancement module, which utilizes multiple receptive fields to take full advantage of the shallow feature information. Experiments conducted on the SAR ship detection dataset prove that the method proposed in this article is superior to the other state-of-the-art algorithms in terms of instance segmentation accuracy and model complexity

A Novel Semi-supervised learning Method Based on Fast Search and Density Peaks

Radar image recognition is a hotspot in the field of remote sensing. Under the condition of sufficiently labeled samples, recognition algorithms can achieve good classification results. However, labeled samples are scarce and costly to obtain. Our major interest in this paper is how to use these unlabeled samples to improve the performance of a recognition algorithm in the case of limited labeled samples. This is a semi-supervised learning problem. However, unlike the existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples before using them. In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S2DP) and an iterative S2DP method (IS2DP). When the labeled samples satisfy a certain requirement, S2DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. When the labeled samples are insufficient, IS2DP is used to iteratively search for reliable unlabeled samples for semi-supervision. Then, these samples are added to the labeled samples to improve the recognition performance of S2DP. In the experiments, real radar images are used to verify the performance of our proposed algorithm in dealing with the scarcity of the labeled samples. In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods

Target recognition from multi-domain Radar Range Profile using Multi-input Bidirectional LSTM with HMM

Radars, as active detection sensors, are known to play an important role in various intelligent devices. Target recognition based on high-resolution range profile (HRRP) is an important approach for radars to monitor interesting targets. Traditional recognition algorithms usually rely on a single feature, which makes it difficult to maintain the recognition performance. In this paper, 2-D sequence features from HRRP are extracted in various data domains such as time-frequency domain, time domain, and frequency domain. A novel target identification method is then proposed, by combining bidirectional Long Short-Term Memory (BLSTM) and a Hidden Markov Model (HMM), to learn these multi-domain sequence features. Specifically, we first extract multi-domain HRRP sequences. Next, a new multi-input BLSTM is proposed to learn these multi-domain HRRP sequences, which are then fed to a standard HMM classifier to learn multi-aspect features. Finally, the trained HMM is used to implement the recognition task. Extensive experiments are carried out on the publicly accessible, benchmark MSTAR database. Our proposed algorithm is shown to achieve an identification accuracy of over 91% with a lower false alarm rate and higher identification confidence, compared to several state-of-the-art techniques

SAR Target Incremental Recognition based on Features with Strong Separability

With the rapid development of deep learning technology, many SAR target recognition algorithms based on convolutional neural networks have achieved exceptional performance on various datasets. However, conventional neural networks are repeatedly iterated on a fixed dataset until convergence, and once they learn new tasks, a large amount of previously learned knowledge is forgotten, leading to a significant decline in performance on old tasks. This paper presents an incremental learning method based on strong separability features (SSF-IL) to address the model’s forgetting of previously learned knowledge. The SSF-IL employs both intra-class and inter-class scatter to compute the feature separability loss, in order to enhance the linear separability of features during incremental learning. In the process of learning new classes, an intra-class clustering loss is proposed to replace the conventional knowledge distillation. This loss function constrains the old class features to cluster around the saved class centers, maintaining the separability among the old class features. Finally, a classifier bias correction method based on boundary features is designed to reinforce the classifier’s decision boundary and reduce classification errors. SAR target incremental recognition experiments are conducted on the MSTAR dataset, and the results are compared with several existing incremental learning algorithms to demonstrate the effectiveness of the proposed algorithm.</p

Time-in-range and frequency of continuous glucose monitoring: Recommendations for South Asia

Background and aim: The prevalence of diabetes is on its rise and South Asia bears a huge burden. Several factors such as heterogeneity in genetics, socio-economic factors, diet, and sedentary behavior contribute to the heightened risk of developing diabetes, its rapid progression, and the development of complications in this region. Even though there have been considerable advances in glucose monitoring technologies, diabetes treatments and therapeutics, glycemic control in South Asia remains suboptimal. The successful implementation of treatment interventions and metrics for the attainment of glycemic goals depends on appropriate guidelines that accord with the characteristics of the diabetes population. Method: The data were collected from studies published for more than the last ten years in the electronic databases PubMed and Google Scholar on the various challenges in the assessment and achievement of recommended TIR targets in the SA population using the keywords: Blood glucose, TIR, TAR, TBR, HbA1c, hypoglycemia, CGM, Gestational diabetes mellitus (GDM), and diabetes. Results: The objective of this recommendation is to discuss the limitations in considering the IC-TIR Expert panel recommendations targets and to propose some modifications in the lower limit of TIR in older/high-risk population, upper limit of TAR, and flexibility in the percentage of time spent in TAR for pregnant women (GDM, T2DM) for the South Asian population. Conclusion: The review sheds insights into some of the major concerns in implementing the IC-TIR recommendations in South Asian population where the prevalence of diabetes and its complications are significantly higher and modifications to the existing guidelines for use in routine clinical practice

National survey of variations in practice in the prevention of surgical site infections in adult cardiac surgery, United Kingdom and Republic of Ireland

BackgroundCurrently no national standards exist for the prevention of surgical site infection (SSI) in cardiac surgery. SSI rates range from 1% to 8% between centres.AimThe aim of this study was to explore and characterize variation in approaches to SSI prevention in the UK and the Republic of Ireland (ROI).MethodsCardiac surgery centres were surveyed using electronic web-based questionnaires to identify variation in SSI prevention at the level of both institution and consultant teams. Surveys were developed and undertaken through collaboration between the Cardiothoracic Interdisciplinary Research Network (CIRN), Public Health England (PHE) and the National Cardiac Benchmarking Collaborative (NCBC) to encompass routine pre-, intra- and postoperative practice.FindingsNineteen of 38 centres who were approached provided data and included responses from 139 consultant teams. There was no missing data from those centres that responded. The results demonstrated substantial variation in over 40 aspects of SSI prevention. These included variation in SSI surveillance, reporting of SSI infection rates to external bodies, utilization of SSI risk prediction tools, and the use of interventions such as sternal support devices and gentamicin impregnated sponges.ConclusionMeasured variation in SSI prevention in cardiac centres across the UK and ROI is evidence of clinical uncertainty as to best practice, and has identified areas for quality improvement as well as knowledge gaps to be addressed by future research.</div