Specific emitter identification (SEI) is a security authentication technology by utilizing radio frequency fingerprinting (RFF) features. However, current mainstream RFF feature extraction methods based on neural networks (NNs) generally suffer from poor interpretability and difficulties in architecture optimization. To address these issues, we propose a novel convolution-enhanced swin vision transformer (CoSwinVIT) that combines filter characteristics of NN architectures to achieve a uniform spectrum response. Specifically, we treat the NN used in SEI tasks as filters and investigate their filter characteristics by applying Fourier transforms to hidden vectors. Through spectral analysis, we found that different NN architectures exhibit significantly different responses to the signal spectrum. This affects the model’s sensitivity to specific frequency bands of the signal, thereby influencing its accuracy (Acc). Subsequently, by integrating the filtering properties of swin vision transformer (SwinVIT) and convolutional neural network (CNN), we achieve a uniform spectral response design. Finally, to evaluate the performance of the CoSwinVIT architecture, we design both a supervised learning algorithm and a contrastive learning-based self-supervised algorithm. Experimental results on a real-world automatic dependent surveillance-broadcast (ADS-B) and wireless fidelity (WiFi) dataset indicate that CoSwinVIT provides a more uniform spectrum response. Under supervised learning, the proposed CoSwinVIT obtains the accuracies of 98.6% and 99.8% on the ADS-B and WiFi datasets, respectively. Under self-supervised learning with 5% and 10% labeled data, the accuracies of 73.6% and 86.3% are achieved on the ADS-B dataset, and the accuracies of 81.6% and 91.5% are achieved on the WiFi dataset. These results surpass the state-of-the-art (SOTA) methods used in the SEI tasks.

Deep learning has demonstrated promising results over traditional hand-crafted methods for automatic modulation classification (AMC), which plays a critical role as an intermediate step between signal detection and modulation. However, acquiring large-scale labeled data remains challenging, as both data quality and annotation costs are critical factors in achieving accurate and efficient training. In this paper, we propose a novel self-supervised contrastive learning (SSCL) with gradient-adversarial-based data augmentation (GADA) approach for AMC. Additionally, a meticulous encoder based on Transformer-LSTM architectures is employed to pre-train a feature extractor using unlabeled base classes. Subsequently, knowledge transfer is employed to fine-tune the feature extractor, and ensemble learning is introduced to efficiently leverage multiple classifiers for joint decision-making. Experiments demonstrate that we achieve up to 91.87% accuracy on the challenging large-scale RadioML2018.10a dataset, demonstrating performance competitive with state-of-the-art supervised implementations.

The broadcast nature of device authentication systems in the Internet of Things (IoT) and maritime transportation makes real-time signals susceptible to forgery attacks, leading to critical risks, such as information leakage and navigation failures. specific emitter identification (SEI) plays a crucial role in protecting communication systems from spoofing and tampering. However, traditional SEI networks struggle with limited generalizability, high-computational costs, and susceptibility to extraction and inversion attacks. To overcome these challenges, this article introduces sliding window linear attention and the convolution mechanism (SWLC)-Conformer, a novel hybrid SEI architecture that seamlessly integrates SWLC. The proposed architecture dynamically captures both local signal characteristics and global temporal dependencies through a hybrid design incorporating window-based attention blocks and depth-wise convolution layers. Furthermore, to strengthen system security against model inversion threats, we incorporate a perturbation mechanism that employs reverse sigmoid activation in the final feature representation. Experimental results demonstrate that SWLC-Conformer achieves high recognition accuracy, reaching 91.10% on the AIS100 dataset and 97.89% on the WiSig dataset, while maintaining FLOPs at 22.17M and 11.11M, respectively. In terms of security, SWLC-Conformer reduces the accuracy of stolen models by over 40% when the stolen dataset ratio is 0.9. This result highlights the effectiveness of the introduced defense mechanism in degrading the usability of stolen models through controlled feature obfuscation.

Integrated detection and communication (IDC) is vital for development of underwater integration technology. Existing approaches heavily rely on either communication or detection signals, requiring complex integration structures and demodulation techniques. In IDC systems, the superimposed waveforms offer enhanced flexibility, simplified waveform design and improved spectral efficiency. However, a significant challenge of superimposed waveforms is the interference between the communication and detection signal. To address this problem, a novel superimposed waveform adopting detection interference cancellation scheme based on compressed sensing is investigated in this paper. This scheme effectively eliminates detection signal and obtains residual communication signal. By combining the sparse estimation of channel impulse response with prior knowledge of detection signals, it reconstructs the detection interference at the receiver. Furthermore, we demonstrate the superiority of proposed scheme based on compressing sensing by comparing it with correlation matching estimation. Considering the influence of multi-path effects and ambient noise on residual communication signal, this paper introduces a virtual time reversal mirror (VTRM) for channel equalization and Gaussian kernel soft decision algorithm. The multi-path effects and ambient noise are modeled by the Bellhop and Middleton Class A model, respectively. Simulation results and sea trial show that the effectiveness of the proposed scheme.

Specific Emitter Identification (SEI) is a non-encrypted authentication technology that adds a layer of security to wireless communications in intelligent transportation systems. However, most existing SEI methods are constrained to identify fixed classes and cannot learn incrementally. In practical applications, the constant emergence of new classes or tasks necessitates the capacity to continuously learn new classes from streaming data. In this paper, combining momentum-based prototype correction and hierarchical regularization, a simple and effective non-example class-incremental method for SEI is proposed, named MoPC-HR. The momentum-based prototype correction dynamically adjusts old class prototypes (i.e., old class centers), strengthening the separation between old and new classes and enabling smoother integration of new classes. Hierarchical regularization is applied at multiple levels to control feature differences between old and new classes, preventing the model from favoring new classes and reducing catastrophic forgetting. Experiments on real-world AIS-100 and ADS-B datasets show that MoPC-HR outperforms state-of-the-art methods. Specifically, in the incremental phase 20, MoPC-HR achieves an average accuracy of 97.04%, average forgetting of 2.95%, and average intransigence of 2.85% on the AIS-100 dataset; and an average accuracy of 95.99%, average forgetting of 3.66%, and average intransigence of 3.60% on the ADS-B dataset. The code is available at https://github.com/xmuLdz/MoPC-HR.git

Specific emitter identification (SEI) is a non-cryptographic authentication technique to provide an extra security layer for wireless devices, which has promising applications. However, the traditional methods of SEI are only available in limited equipments. In actual application scenarios, new devices (as new classes) are constantly appearing. In this paper, an effective class incremental learning (CIL) method is proposed for SEI, named class-incremental with self-training and prototype augmentation (CISP). It is a teacher-student network. Firstly, the teacher network trained by the old-class data is utilized to instruct the student network to adapt the new classes while retaining the old-class knowledge through the knowledge distillation (KD) techniques. Secondly, in order to mitigate the problem of favoring the new classes, weight aligning (WA) method is introduced to balance the weights of the new-class and old-class classification layers in the student network. Lastly, the old-class samples are recalled from the unlabeled dataset by the student network and input into the teacher network. Then the feature prototypes of the old classes are constructed and augmented. This would further ease the imbalance between the old and new classes and alleviate the problem of noisy pseudo-labels. Experiment results on the real AIS-100 dataset and ADS-B-100 dataset with the number of the initial classes being 20 and 20 classes per incremental step demonstrate that the proposed method can achieve an average accuracy of 95.29% and 95.84%, respectively. It effectively mitigates the catastrophic forgetting of the model and is superior to the state-of-the-art incremental learning approaches of not saving the old-class samples.

Depression is a common mental disease, and it is committed to promote the research of depression assessment based on physiological signals. By collecting the resting state EEG data of depressive disorder, we collected the resting state EEG data of 14 patients with depression and 17 normal people. Through the analysis, the number of troughs of each person's data was statistically analyzed, combined with the convolution neural network model. The accuracy rate of some data is 94.88% by the statistical trough number method, and the accuracy rate of the remaining part of the test data set is 85.0% through the convolution neural network model, and the final fusion accuracy rate is 86.88%. The experimental results show that the combination of statistical trough number and convolution neural network can distinguish depression patients better in resting state EEG data.