In recent years, low Earth orbit (LEO) satellite communication has emerged as a focal area of extensive research due to its potential for global broadband connectivity. Multi-carrier direct sequence spread spectrum (MC-DSSS) technology, leveraging the inherent anti-jamming advantages of spread spectrum signals, has shown great promise in enhancing communication reliability. However, the acquisition of MC-DSSS signals in LEO transmission link presents significant challenges, primarily due to the coexistence of extremely low signal-to-noise ratio (SNR) and substantial Doppler effect.To address these issues, this paper proposes an optimal two-dimensional (2D) acquisition framework for MC-DSSS signals. Based on the maximum likelihood (ML) criterion, the proposed framework enables coherent combination of subcarriers with high time resolution, thereby improving the acquisition performance in harsh LEO environments. Furthermore, a two-step low-complexity coherent acquisition algorithm is developed. This algorithm significantly reduces the computational burden while maintaining the performance of fully coherent subcarrier combination, making it more suitable for real-time implementation in resource-constrained LEO communication terminals. Moreover, this paper derives closed-form solutions for the false alarm probability, detection probability, and mean squared error (MSE) in additive white Gaussian noise (AWGN) channel, which are validated through simulations. The results demonstrate that the proposed algorithm achieves 2 dB performance improvement in SNR compared to noncoherent combining method, with a 1024-fold reduction in MSE when the number of subcarriers is 16.

Cross-scene hyperspectral image (HSI) classification presents significant challenges due to domain shifts, which amplify epistemic uncertainty and lead to substantial performance drops in unseen scenes. While evidence deep learning (EDL) has shown promise in modeling uncertainty, existing methods fall short, as they do not explicitly account for the epistemic uncertainty arising from spatial-spectral feature interactions. To address these challenges, we propose the spectral-spatial evidence deep learning for cross-scene hyperspectral image classification (HyperEDL) framework, which introduces the spatial-spectral multiorder aggregation module (SS-Moga). This module effectively captures and adaptively encodes multiorder contextual interactions from both spatial and spectral perspectives. By combining multiorder contextual encoding with spatial-spectral confidence, our approach fully aggregates multiorder evidence to mitigate epistemic uncertainty arising from knowledge gaps between seen and unseen scenes. Specifically, it uses Dirichlet distribution to capture correlation between spatial-spectral knowledge about different scenes, which can be generalized to unseen scenes. Extensive experiments on three benchmark datasets demonstrate that HyperEDL outperforms state-of-the-art methods, showcasing its effectiveness and strong generalization ability.

In this paper, we propose a new task named incremental few-shot action recognition (IFSAR), which aims to learn new action classes incrementally with limited samples. Existing few-shot class incremental learning methods are mainly designed for image datasets and cannot be directly applied to action recognition due to the complicated temporal evolution and spatial structure in videos. Besides, because of the incremental and fewshot setting, the catastrophic forgetting and overfitting problems are further intensified in the video domain. To address the above issues, we propose a spatiotemporal orthogonal projection capsule network (STOP), which employs a spatiotemporal attention routing mechanism and an orthogonal projection capsule layer for effective IFSAR. The former can effectively encode spatial and temporal transformation information and explore the action partwhole relationships to prevent catastrophic forgetting, while the latter is further designed to maintain a sufficient distance between the prototypes of old and novel classes to avoid overfitting by considering spatial-temporal features. Extensive experimental results demonstrate that the proposed method outperforms a series of state-of-the-art approaches on UCF-101, Kinetics-100, and HMDB-51 datasets.

Open-set action recognition(OSAR) aims to recognize known classes and reject unknown classes. Most OSAR methods focus on learning a favorable threshold to distinguish known and unknown samples in a pure data-driven manner. However, these methods do not utilize the prior knowledge of action classes. In this paper, we propose to Leverage Attribute Knowledge (LAK) for OSAR. Specifically, the class-attribute knowledge learning is designed to integrate attribute knowledge into the model based on spatial-temporal features. Here, attributes are used as a bridge, linking known and unknown classes implicitly to make up the knowledge gap. Furthermore, a learnable relation matrix is adaptively adjusted during training to obtain the class-attribute relations that are expected to be generalized in open-set settings. Extensive experiments on three popular datasets show that the proposed method achieves state-of-the-art performance.

Few-shot video classification (video FSL), which learns classifiers for novel concepts, has gained increasing attention in the last few years from only a few samples. The existing methods rarely consider the local-global relation for video feature learning, which would ultimately result in low discriminative ability. Recently, the capsule network (CapsNet) has shown considerable potential in local-global relation learning in the image analysis field. However, CapsNet cannot be directly applied in video FSL since it ignores the interaction between videos and has high computational complexity. In this paper, a dual-routing capsule graph neural network (DR-CapsGNN) is proposed to solve the above issues. The DR-CapsGNN leverages CapsNet and a graph neural network (GNN) to explore local-global relations and to preserve the detailed properties. Specifically, the CapsGNN is used to learn video relations and structural information to generate high-quality hierarchical capsules. Furthermore, a novel dual-routing mechanism is designed to filter low-discriminative capsules from a holistic perspective and achieves high efficiency, which consists of inter-video and intra-video routing. Extensive experimental results demonstrate that our proposed approach performs favorably compared to state-of-the-art methods on two popular benchmarks.

Open set action recognition (OSAR) is a rising research domain that simultaneously identifies all videos from known classes and rejects videos from unknown classes. Existing methods rarely consider the open set data distribution and the spatial-temporal relations of video subsequence. Recently proposed Capsule Network (CapsNet) has shown robust performance in many fields, especially image recognition. However, the current CapsNet has not been directly applied to the OSAR task since it cannot explicitly consider the data distribution of known and unknown classes along with the spatial-temporal relations for videos. This paper proposes the Spatial-Temporal Exclusive Capsule Network (STE-CapsNet) to solve the problems in the OSAR task. The STE-CapsNet designs the temporal-spatial routing mechanism to jointly capture the spatial-temporal information of the videos. Furthermore, the exclusive capsules are learned with dot product routing mechanism to limit the data distribution of closed set and open set and reduce the open set risk for OSAR. Extensive experimental results demonstrate that our proposed approach performs favorably compared with state-of-the-art methods on three standard datasets, which verifies its effectiveness and generalization ability.

Kernel‐based extreme learning machine (KELM) solves the problem of random initialisation of extreme learning machine (ELM), and it has a faster learning speed and higher learning accuracy. However, when it comes to a scenario in which the dimensionality of kernel function mapping space is less than the number of samples, the kernel function theoretically cannot be introduced into ELM. To solve this problem, ELM with feature mapping (FM) of kernel function (FM‐KELM) is proposed in this study, in which the random FM between the input layer and hidden layer of ELM is replaced with the FM of the kernel function. Moreover, the authors prove that when the regularised parameter C is close to zero, the solution of introduced kernel function is approximately equal to the correct solution. The proposed algorithm is more robust than KELM for the parameter C. Several experimental results show that the proposed algorithm in this study achieves higher classification accuracy without excessive parameter tuning, and the duration of the training and testing process is significantly reduced.