1. Solar energy is central to the global power infrastructure, with photovoltaic (PV) cells serving as the primary technology for converting sunlight into electricity [1–3]. Although modern solar c...

Accurate thickness recognition plays a vital role in safeguarding the structural reliability of critical assets. Pulse eddy current testing (PECT), as a non-destructive method that is both non-contact and insensitive to surface coatings, provides an efficient pathway for this purpose. Nevertheless, the complex, nonstationary, and nonlinear characteristics of PECT signals make it difficult for conventional models to jointly capture localized high-frequency patterns and long-range temporal dependencies, thereby constraining their prediction performance. To overcome these issues, we introduce a novel deep learning framework, multi-scale residual dilated convolution, and bidirectional long short-term memory with a squeeze-and-excitation mechanism (MRDC-BiLSE) for PECT time series analysis. The architecture integrates a multi-scale residual dilated convolution block. By combining dilated convolutions with residual connections at different scales, this block captures structural patterns across multiple temporal resolutions, leading to more comprehensive and discriminative feature extraction. Furthermore, to better exploit temporal dependencies, the BiLSTM-SE module combines bidirectional modeling with a squeeze-and-excitation mechanism, resulting in more discriminative feature representations. Experiments on experimental PECT datasets confirm that MRDC-BiLSE surpasses existing methods, showing applicability for real-world thickness recognition.

Fine-grained cotton detection is challenging because targets at different developmental stages exhibit substantial variations in scale and morphology, while visually similar categories, partial occlusion, and complex field backgrounds further increase recognition difficulty. To address these issues, this study proposes WINGS-Det, a lightweight object detector that coordinates spatial, scale, and frequency-aware feature modeling within a unified architecture. Built on YOLO26n, WINGS-Det incorporates three complementary components. C3k2LGL combines large-kernel local aggregation with sparse global self-attention to capture local structures and long-range spatial dependencies. SCMIFF performs selective cross-scale interaction between hierarchical features to strengthen the fusion of spatial details and semantic information. C2WSA introduces wave-aware frequency modeling to preserve global structural information while enhancing high-frequency boundary and texture cues. A four-category cotton dataset containing 13,771 images and 27,352 annotated instances was constructed for evaluation. WINGS-Det achieves 98.6% Precision, 98.2% Recall, 98.9% mAP₅₀, and 75.7% mAP_50:95 with only 2.38 M parameters and 5.3 GFLOPs, while achieving 163 FPS on an RTX 4090 with native PyTorch FP32 inference. On the newly constructed four-category cotton dataset, WINGS-Det improves mAP₅₀ and mAP_50:95 over the YOLO26n baseline by 4.7 and 3.8 percentage points, respectively. Extensive evaluations across target scales, object densities, image perturbations, network architectures, hardware platforms, and external datasets further demonstrate the robustness, transferability, and generalization capability of WINGS-Det. The results indicate that the proposed framework achieves an effective balance between fine-grained detection accuracy, computational efficiency, and practical deployment.

Electroencephalogram (EEG)-based motor imagery (MI) decoding is vital in brain–computer interface (BCI) systems. Although convolutional neural networks (CNNs) have achieved promising results, existing methods still face limitations in multi-scale feature extraction. Specifically, some CNN architectures focus primarily on local and global temporal features, while medium temporal information remains underexplored. Moreover, conventional feature integration strategies often overlook the distinct characteristics of features at different scales, limiting their effective utilization. To address these limitations, we propose a multi-perspective attention-based multi-scale CNN, called MPAMCNN. The model extracts local, medium, and global EEG features simultaneously by applying convolution kernels with different receptive fields. In addition, we incorporate different attention mechanisms into the corresponding branches to emphasize discriminative information and reduce feature redundancy before fusion. Extensive experiments on two benchmark datasets, BCI Competition IV-2a and IV-2b, demonstrate that MPAMCNN achieves classification accuracies of 79.71% and 88.02%, respectively, validating its efficacy for EEG motor imagery classification. Visualization analysis also reveals improved feature separability and clearer decision boundaries.