With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

This paper presents an efficient inverse kinematics (IK) solution for robotic manipulators, addressing the challenges of high computational complexity, low efficiency, and sensitivity to singularities associated with traditional methods. A data augmentation strategy is introduced, utilizing an enhanced Diffusion-TS model to generate diverse joint-angle samples and corresponding end-effector poses through forward kinematics, thereby creating a high-quality dataset. To improve real-time performance, a Temporal Convolutional Network (TCN) model is developed, optimized using the Grey Wolf Optimizer (GWO), and augmented with a probabilistic sparse attention mechanism to effectively capture key pose features. Experimental evaluations on the Jaka MiniCobo robotic arm demonstrate that the proposed method significantly reduces inference time while maintaining high accuracy, making it suitable for real-world applications that demand both speed and precision.