Due to the presence of redundancy and interference, frame sampling is a promising but challenging solution to mitigate the expensive computation of video action recognition. Although the motion prior has shown great potential for frame selection, existing motion-based strategies suffer from limitations in terms of robustness and interpretive evaluation. In this paper, we devise a robust frame sampling strategy called positive motion guided sampler (PMGSampler). It consists of two procedures, local motion capture and global motion statistics. At the local level, we propose two concepts about inter-frame motion amplitude and motion continuity, which helps to perceive the movement of subjects and identify abnormal events that may generate negative pseudo-motion information. Then, through a global analysis of the obtained local motions, the sampler becomes more sensitive to informative frames and robust to outliers. The proposed sampler can be applied to most existing models for improving recognition accuracy. We conduct extensive experiments on four widely-used benchmarks to demonstrate the superiority of our PMGSampler over other methods of the same type. In addition, to analyse how sampled frames influence action recognition, we present a visual interpretation method for video models, termed as spatio-temporal class activation map (STCAM). By introducing spatial and temporal branches, our STCAM is able to visualise the salience of spatio-temporal features. With the help of STCAM, we can further intuitively evaluate the performance of different sampling strategies.

The surge in encrypted network traffic poses a significant challenge to existing cyberspace security measures. Classical traffic identification methods, such as those based on ports or statistical features, are ineffective against encrypted traffic. Deep learning methods offer new avenues for identifying encrypted traffic, but they are highly dependent on labeled data and struggle to adapt to new types of encrypted traffic. Pre-trained models possess a powerful generalization capability due to their training on large-scale unlabeled datasets, and they reduce the dependency on labeled data for specific downstream tasks. In this paper, we present an encrypted traffic classification framework based on pre-trained models and introduce a tokenization method tailored for encrypted traffic called FlowPiece. The experimental results demonstrate that our approach, along with the FlowPiece method, can reduce the parameter count by approximately 93% while maintaining nearly identical performance. This significant reduction in parameters enables the widespread application of encrypted traffic identification methods based on pretrained models.

Silent high-dynamic target recognition using single receiver has garnered significant attention due to its advantages in safety and stealth within military operations. This paper presents an in-depth exploration of a silent high-dynamic target recognition method based on frequency modulation (FM) signals. The proposed method processes the FM signals with a least squares filtering approach and further implements the calculation of Cross-Ambiguity Function (CAF) mapping to achieve target imaging within the CAF spectrum. By capturing FM signals with an antenna array tuned to various orientations, we conducted empirical analysis using actual aircraft in flight as the target for identification. The recognition scheme put forth by our research is capable of precisely determining the velocity of the target and the relative distance parameters between dual base stations, facilitating accurate tracking of targets.

Falls, a common type of accident, especially among the elderly and those with mobility impairments, potentially leading to serious physical injuries and health issues. Fall detection refers to the use of sensors, monitoring equipment, or other technological means to monitor and identify occurrences of falls. Currently, a lot of research on fall detection from various aspects such as vision, wearable devices, and multi-modal. However, current vision-based fall detection datasets are limited to single indoor scenarios and do not consider outdoor scenarios. Therefore, in this paper we propose a multi-perspective indoor and outdoor scenarios fall detection dataset based on campus surveillance, which include both indoor and outdoor campus scenarios. We employed YOLOv5 to carry out experiments on our proposed dataset as a benchmark. Moreover, we undertook comparative experiments against other datasets and assessed the richness of our dataset. The experiment results reveal that our dataset encompasses more diverse scenarios than other vision-based fall detection dataset.

The Specific Emitter Identification (SEI) technology has broad application prospects in the fields of the Internet of Things and cognitive communication and serves as an effective means for device authentication. Most existing SEI methods based on deep learning primarily operate in the real number domain. However, complex numbers naturally represent radio frequency signals, making complex-valued neural network(CVNN) a superior choice for signal representation. To address the limitations of traditional real-valued neural network methods, such as low recognition accuracy and the requirement for a large number of training samples, we propose an SEI method based on CVNN to learn the true RF fingerprint features. In the data preprocessing stage, we employ a signal slicing strategy with a random window step size to enhance data randomness and improve the model’s generalization performance. Given the complex-valued nature of signals, we employ a complex-valued convolutional neural network for feature extraction. Subsequently, we design a feature fusion module based on the complex-valued attention mechanism to eliminate redundant features while preserving radio frequency fingerprint features. Considering the high similarity between emitter signals, we employed a joint loss function based on metric learning that promotes intra-class aggregation and interclass separation. The experimental results demonstrate that the proposed method uncovers the authentic RF fingerprint with high accuracy and robustness under limited training sample conditions.

With the advancement of Internet of Things technology, the need for sophisticated signal modulation classification has intensified, ensuring seamless communication and bolstering security among interconnected devices. In the contemporary complex channel environment, the difficult lies in dealing with a multitude of modulation schemes that exhibit subtle distinctions. Prior knowledge-guided and deep learning methods have complementary strengths in the current context of signal modulation classification. To synthesize the advantages of these two methods, we propose an integrated method of prior knowledge and contrast feature for signal modulation classification, called APFS. APFS integrates prior knowledge from the modulation task with feature information acquired through contrastive learning. Feature extraction guided by prior knowledge accurately captures the key patterns in modulated signals. Contrastive learning reveals the inherent distinctions among various modulation modes by comparing different samples. In the joint feature extraction approach for prior knowledge, each form of prior knowledge is first analyzed independently, and then jointed to extract information from its temporal sequence. The contrast features surpass the constraints of labeling and unearth deeper implicit information. In experiments, we systematically compared the performance of our method with various baselines, as well as combinations of prior knowledge and contrast feature. The results demonstrate the superior performance of our method.

Advances in communication technology have enabled signal modulation classification (SMC) to be widely used in noncooperative identification situations, such as spectrum detection, electronic countermeasures, and target identification. In the face of complex electromagnetic environments and various classification tasks, the class imbalance phenomenon in modulated signal data sets has become a problem that cannot be ignored. For the SMC based on machine learning, the unbalanced training data set will cause the actual decision boundary to shift, thereby reducing the prediction accuracy of minority signals. And for SMC based on deep learning, unbalanced data will lead to distortion of the feature space and affect the extraction of discriminative features. However, the existing modulation classification methods cannot effectively deal with the imbalance problem. This study introduces an oversampling method tailored for modulation signals. Our method balances the data set by synthesizing new samples according to the distribution of signal samples and the distance between samples, which will effectively reduce the impact of the imbalance problem on the classifier. For modulated signals, experimental results show that our method performs better than other oversampling methods. In addition to the SMC task, we test the performance of the proposed method for individual identification of radiation sources on the aircraft communications addressing and reporting system data set. Compared with other comparison methods, our method improves the classification performance the most.

The electromagnetic environments have become more complex with the development of wireless communication technology. Signal modulation classification has attracted extensive attention due to its application in electronic countermeasures and physical layer security threat prevention under complex electromagnetic environments. Excellent classification performance requirements challenge the adaptability of the method and the ability to extract modulation characteristics. This paper proposes an automated machine learning framework, AutoSMC, for signal modulation classification. An adaptive signal augmentation method is proposed to adapt to the network changes during the search process. In order to extract the modulation features effectively, an scalable convolutional random fourier feature block is proposed. Moreover, the initial search space of the framework is given. The Bayesian Optimization is used to drive hyperparameter optimization to achieve AutoSMC and obtain the optimal method state. Great experiments were carried out on RADIOML 2016.10A and RADIOML 2016.10B. Experimental evaluations on these datasets show that our approach AutoSMC achieves state-of-the-art results compared to the most relevant signal modulation classification methods.

Imbalanced signal modulation classification is a challenging problem in wireless communication. In this paper, we propose an overall framework from data generation to generated data qualitative analysis. It generates data based on Synthetic Minority Over-Sampling Technique (SMOTE) and evaluates the generated data in terms of amplitude, distribution, and classification validity, namely SMOTE-ADC. The framework aims to generate synthetic data for the minority class to rebalance the dataset and improve the classification performance. SMOTE-ADC creates and defines a matrix of distances between eigenvalues by considering the eigenvalues and their relationships. By comparing the variability of neighboring feature vectors and defining the weights of the distance matrix to make the generated data more close to the real data. For the evaluation of generated data in SMOTE-ADC, we propose three methods to analyze the effect of generated data, comparing the amplitude, distribution, and classification effectiveness of generated data and original data, respectively. Experimental results demonstrate that our approach effectively mitigates the impact of data imbalance and enhances the accuracy of signal modulation classification.

Unsupervised signal modulation clustering is becoming increasingly important due to its application in the dynamic spectrum access process of 5G wireless communication and threat detection at the physical layer of Internet of Things. The need for better clustering results makes it a challenge to avoid feature drift and improve feature separability. This article proposes a novel separable loss function to address the issue. Besides, the high-level semantic properties of modulation types make it difficult for networks to extract their features. An autoencoder structure based on the random Fourier feature (RffAe) is proposed to simulate the demodulation process of unknown signals. Combined with the separable loss of RffAe (RffAe-S), it has excellent feature extraction ability. Great experiments were carried out on RADIOML 2016.10 A and RADIOML 2016.10 B. Experimental evaluations on these datasets show that our approach RffAe-S achieves state-of-the-art results compared to classical and the most relevant deep clustering methods.