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Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations.

Intracardiac foreign bodies (IFB) are rare clinical conditions. There are now several reports on the percutaneous retrieval of IFB under fluoroscopy. However, some IFB are not radiopaque, and retrieval requires combined fluoroscopic and ultrasound guidance. We report the case of a bedridden 23-year-old male patient with T-lymphoblastic lymphoma treated with long-term chemotherapy. Ultrasound examination diagnosed a huge thrombus in the right atrium near the opening of the inferior vena cava which affected the patency of his PICC line. Ten days of anticoagulant therapy did not modify the thrombus size. Open heart surgery was not feasible because of the patient clinical condition. Snare-capture of the non-opaque thrombus was done from the femoral vein under fluoroscopic and ultrasound guidance with excellent outcomes. We also present a systematic review of IFB. We found out that percutaneous removal of IFBs is a safe and effective procedure. The youngest patient who received percutaneous IFB retrieval was 10 days old and weighed only 800 g, while the oldest patient was 70 years old. Port catheters (43.5%) and PICC lines (42.3%) were the most commonly found IFBs. Snare catheters and forceps were the most commonly used instruments.

Deep learning is an important support for the development of cognitive communication in the cognitive Internet of Things (IoT). Deep convolution neural networks have powerful functional expression and feature extraction capabilities. Mallat et al. proposed a convolutional network model with strict mathematical theory support and excellent feature extraction ability in 2012, i.e., wavelet scattering convolution networks, which is widely used in audio and image classification. In this paper, we improve the wavelet scattering convolution networks and propose construct weight-variable scattering convolution networks by combining the scattering network and the deep convolution neural network organically. We select a variety of wavelet filters for filtering operations and add 1*1 convolution layers to enable the features extracted by different types of wavelet filters to be combined optimally. In order to verify the effectiveness of the proposed network structure, we conduct experiments on classification of typical electromagnetic signals. The experimental results show that the signal classification accuracy obtained by the weight-variable scattering convolution networks is significantly better than that of the traditional wavelet scattering convolution network, and the computational complexity is greatly reduced while the classification accuracy is close to the deep convolution neural network.

In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods.

This paper proposes a novel open set recognition method, the Spatial Distribution Feature Extraction Network (SDFEN), to address the problem of electromagnetic signal recognition in an open environment. The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors. The designed hybrid loss function considers both intra-class distance and inter-class distance, thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training. Consequently, this method allows unknown classes to occupy a larger space in the feature space. This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct. Additionally, the feature comparator threshold can be used to reject unknown samples. For signal open set recognition, seven methods, including the proposed method, are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter. The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment. Specifically, compared to the state-of-the-art Openmax method, the novel method achieves up to 8.87% and 5.25% higher micro-F-measures, respectively. [ABSTRACT FROM AUTHOR]