Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.

Radar signals are emerging constantly for urgent task because of its complex patterns and rich working modes. For some radar waveforms with known modulation methods, they can be identified by correlation between radar prior knowledge and the received signals by the reconnaissance receiver. As for the unknown radar signals, how to identify unknown radar waveforms under the condition of limited samples and low signal-to-noise ratio is a challenging problem. Aiming at the learning ability of the deep features of the image by the convolutional neural network (CNN), the reconstructed features of the time-frequency image (TFI) of the known and unknown radar waveform signals have been excavated. A decision fusion unknown radar signal identification model based on transfer deep learning and linear weight decision fusion is designed in this paper. Firstly, the CNN is trained using the known radar signals; Then, based on the transfer learning, the neurons obtained from the multiple underlying the CNN are used to represent the reconstruction feature; Finally, the performance of the single random forest classifier of the original TFI and short- time autocorrelation features images (SAFI)are fused, the identification decision of unknown signals is realized by setting linear weight to the two databases. The recognition rate of unknown new classes for small samples exceeds 80.31%, and the classification accuracy rate for known radar waveform reach more than 99.15%.

As one of the key technologies of electromagnetic spectrum operations, radar waveform recognition is an important basis for judging the threat degree of enemy’s weapons. At present, it is difficult to recognize the waveform at low signal‐to‐noise ratio (SNR), and the theoretical simulation is used in most research studies, which leads to the problem that the engineering practicality of the algorithm is poor. In this paper, a two‐stream convolutional network radar waveform recognition (TCNRWR) algorithm that fuses the original time–frequency image and the continuous frames autocorrelation time–frequency image is proposed, and an experimental platform is built by using software defined radio technology. Compared with traditional theoretical simulations, this paper focusses on the influence of spatial background interference and signal incompleteness on the recognition of intra‐pulse signals, and proposes an automatic removal method of invalid signal in the time–frequency domain. In order to further improve the recognition rate at low SNR, a deeper feature of short‐time autocorrelation flow is excavated, and a two‐stream time–frequency images fusion convolutional network (TTIFCNet) is constructed. The network effectively fuses the pre‐classification results of the two streams through Softmax layer and different weights. Finally, the hardware platform HackRF is used to realize the communication and processing of signals in real space, which proves the effectiveness of the TCNRWR algorithm.

Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks.

Stimulus-responsive hydrogels have broad applications in the biomedical, sensing, and actuation fields. However, conventional fabrication methods are often limited to 2D structures, hindering the creation of complex, personalized 3D hydrogel architectures. Furthermore, hydrogels responding to only a single stimulus and delays in fabrication techniques restrict their practical utility in biomedicine. In this study, we developed two novel multi-stimuli-responsive hydrogels (based on Gelatin/Sodium Alginate and Tannic Acid/EDTA-FeNa complexes) specifically designed for direct ink writing (DIW) 3D printing. We systematically characterized the printed properties and optimized component ratio, revealing sufficient mechanical strength (e.g., tensile modulus: Gel/SA-TA ≥ 0.22854 ± 0.021 MPa and Gel/SA-TA@Fe³⁺ ≥ 0.35881 ± 0.021 MPa), high water content (e.g., water absorption rate Gel/SA-TA ≥ 70.21% ± 1.5% and Gel/SA-TA@Fe³⁺ ≥ 64.86% ± 1.28%), excellent biocompatibility (e.g., cell viability: Gel/SA-TA and Gel/SA-TA@Fe³⁺ ≥ 90%) and good shape memory performance (e.g., the highest shape recovery rate of Gel/SA-TA reaches 74.85% ± 4.776%). Furthermore, we explored electrical characteristics, showing that the impedance value of Gel/SA-TA@Fe³⁺ hydrogel changes significantly under finger bending and NIR irradiation. This investigation demonstrates the potential of these 3D-printed multi-stimuli hydrogels for applications such as wearable flexible strain sensors.