Remote sensing change detection (RSCD) aims to utilize paired temporal remote sensing images to detect surface changes in the same area. Traditional CNN-based methods are limited by the size of the receptive field, making it difficult to capture the global features of remote sensing images. In contrast, Transformer-based methods address this issue with their powerful modeling capabilities. However, applying the Transformer architecture to image processing introduces a quadratic complexity problem, significantly increasing computational costs. Recently, the Mamba architecture based on state-space models has gained widespread application in the field of RSCD due to its excellent global feature extraction capabilities and linear complexity characteristics. Nevertheless, existing Mamba-based methods lack optimization for complex change areas, making it easy to lose shallow features or local features, which leads to poor performance on challenging detection cases and high-difficulty datasets. In this paper, we propose a Mamba-based RSCD network for difficult cases (DC-Mamba), which effectively improves the model’s detection capability in complex change areas. Specifically, we introduce the edge-feature enhancement (EFE) block and the dual-flow state-space (DFSS) block, which enhance the details of change edges and local features while maintaining the model’s global feature extraction capability. We propose a dynamic loss function to address the issue of sample imbalance, giving more attention to difficult samples during training. Extensive experiments on three change detection datasets demonstrate that our proposed DC-Mamba outperforms existing state-of-the-art methods overall and exhibits significant performance improvements in detecting difficult cases.

The degradation of image resolution reduces the detection performance in aerial imagery because it generates a large number of small objects, and accurately detecting these small objects remains a challenge. Existing methods mostly use a Super-Resolution (SR) model to first obtain the SR image of the Low-Resolution degraded image ( $I^{LR}$ ) and then use this image as the input of the object detection (OD) network to solve this problem. However, this architecture that involves executing a complex SR network before the detector is time-consuming and makes it hard to achieve real-time model inference. To address this challenge, we propose a simple and effective rotated small object detection method, named End-to-end Super-Resolution enhanced real-Time rotated object Detector (ESRTMDet). Firstly, we design a lightweight Embedded feature map Super-Resolution Module (ESRM) embedded in the detection model to enhance and amplify the backbone output features, making the detection heads detect small objects more easily. Furthermore, we train a parallel SR network branch (PSRB) simultaneously that uses the backbone feature to restore a High-Resolution image ( $I^{HR}$ ). Through our proposed feature alignment loss ( $\mathcal {L}_{AL}$ ) and feature affinity layer (FAL), our PSRB effectively guides the feature map enhancement of ESRM. Finally, through end-to-end joint optimization of the detector and PSRB, the detection performance on $I^{LR}$ is significantly improved. Extensive experiments over DOTA and UCAS-AOD demonstrate that our method can achieve state-of-the-art (SOTA) results. Additionally, we discard our PSRB and use $I^{LR}$ as the input during inference, reducing the inference time-consuming of our model. Therefore, our ESRTMDet-X not only achieves 77.11% mean of Average Precision (mAP) on the degraded DOTA dataset, but also achieves an amazing inference speed of 337 FPS, thus obtaining the best speed-accuracy trade-off. The code and models will be released at https://github.com/liufeinuaa/aisodet.git .

Traditional Euclidean spatial data processing is difficult to capture the inherent relationships of unstructured data such as bearing vibration signals. Representing vibration signals in graphical form helps to preserve their topological structure and temporal information. Secondly, most existing graph convolutional network methods are based on large graph structured data, which incurs certain memory overhead when aggregating high-order neighborhood node information and ignores important information between samples in the global graph structure. To address these issues, this paper proposes a high-order multi-head graph attention network based on an adaptive small graph structure (ASG-HOMGAT) for fault diagnosis of rolling bearings. Firstly, the adaptive preprocessing layer is used to adaptively denoise and compress the one-dimensional time-domain vibration signal, generating small rule graph data with topological structure. Then, these small graph structured data samples are input into a higher-order graph neural network, which aggregates features from multiple higher-order neighborhoods to achieve richer feature representations and fully explore the intrinsic correlation between samples. Finally, these features are aggregated into a reinforced representation of graph nodes through a multi head attention mechanism, and a SoftMax classifier is used for fault classification. The experimental results show that the ASG-HOMGAT method has better performance compared to mainstream graph neural network diagnostic models. The code and model will be released at: https://github.com/ding-ss/ASG-HOMGAT.

The formation operation of unmanned aerial vehicles (UAVs) is a current research hotspot, particularly in specific mission scenarios where UAV formations are required to cooperatively arrive at designated task areas to meet the needs of coordinated operations. This paper investigates the issues of cooperative arrival and energy consumption optimization for UAV formations in such scenarios. First, focusing on rotorcraft UAVs, the flight energy consumption optimization model and cooperative arrival model are derived and constructed. Next, to address the challenges in solving these models, the multi-objective non-convex functions are transformed into single-objective continuous functions, thereby reducing computational complexity. Furthermore, an interior-point-method-based solving strategy is designed by estimating the initial values of the solving parameters. Finally, simulation experiments validate the feasibility and effectiveness of the proposed method. The experimental results show that when optimizing the energy consumption of a formation of five UAVs, the algorithm converges in just 16 iterations, demonstrating its suitability for practical applications.

Although convolutional neural networks (CNNs) and Transformer-based models have achieved considerable success in the remote sensing change detection (RSCD) field, they often struggle to balance local detail preservation and global dependency modeling. Recently, state-space models such as Mamba have shown promising potential for efficient long-range representation learning. However, most existing Mamba-based RSCD methods are constrained by fixed residual connection mechanisms and selective scanning strategies, resulting in limited feature adaptability in spatial dependency modeling. To address these challenges, we propose Adaptive Residual Mamba (AR-Mamba), a novel Mamba-based framework that introduces adaptive residual and cross-gated scanning mechanisms. Specifically, the adaptive residual state (ARS) block employs adaptive kernel convolver and learnable scaling to dynamically calibrate residual signals. The cross-gated bi-scanning (CGBS) strategy performs bidirectional spatial scanning and cross-directional gating. Experimental results on the LEVIR-CD, SYSU-CD and WHU-CD datasets demonstrate the effectiveness of the proposed AR-Mamba, which achieves F1-score improvements of 0.82%, 1.95% and 1.62% over ChangeMamba, respectively.

The data-driven fault diagnosis method has achieved many good results. However, classical convolutional and recurrent neural networks have problems with large parameters and poor anti-noise performance. To solve these problems, we propose a lightweight shifted windows transformer based on inverted residual structure and residual multi-layer perceptron (IRMSwin-T) for fault diagnosis of rolling bearings. First, the original data are expanded by using overlapping sampling technology. Then, the collected one-dimensional vibration signals are vector serialized by using the patch embedding strategy. Finally, the IRMSwin-T network is developed to extract features of vector sequences and classify faults. The experimental results showed that compared with mainstream lightweight models, the IRMSwin-T model in this paper has fewer parameters and higher diagnostic accuracy.