When multiple feature modalities are fused， there is a superposition of noise， and the cascaded structure used to reduce the differences between modalities does not fully utilize the feature information between modalities. To address these issues， a cross-modal Dual-stream Alternating Interactive Network （DAINet） method was proposed. Firstly， a Dual-stream Alternating Enhancement （DAE） module was constructed to fuse modal features in interactive dual-branch way. And by learning mapping relationships between modalities and employing bidirectional feedback adjustments of InFrared-VISible-InFrared （IR-VIS-IR） and VISible-InfRared-VISible （VIS-IR-VIS）， the cross suppression of inter-modal noise was realized. Secondly， a Cross-Modal Feature Interaction （CMFI） module was constructed， and the residual structure was introduced to integrate low-level and high-level features within and between infrared-visible modalities， thereby minimizing differences and maximizing inter-modal feature utilization. Finally， on a self-constructed infrared-visible multi-modal typhoon dataset and a publicly available RGB-NIR multi-modal dataset， the effectiveness of DAE module and CMFI module was verified. Experimental results demonstrate that compared to the simple cascading fusion method on the self-constructed typhoon dataset， the proposed DAINet-based feature fusion method improves the overall classification accuracy by 6.61 and 3.93 percentage points for the infrared and visible modalities， respectively， with G-mean values increased by 6.24 and 2.48 percentage points， respectively. These results highlight the generalizability of the proposed method for class-imbalanced classification tasks. On the RGB-NIR dataset， the proposed method achieves the overall classification accuracy improvements of 13.47 and 13.90 percentage points， respectively， for the two test modalities. At the same time， experimental results of comparing with IFCNN （general Image Fusion framework based on Convolutional Neural Network） and DenseFuse methods demonstrate that the proposed method improves the overall classification accuracy by 9.82， 6.02， and 17.38， 1.68 percentage points for the two test modalities on the self-constructed typhoon dataset.

Traditional K-means algorithm is sensitive to the initial start center. To solve this problem, a method was proposed to optimize the initial center points through adopting the theory of good point set and Leader method. According to the different combination ways, the new algorithms were called KLG and KGL respectively. Better points could be obtained by the theory of good point set rather than random selection. The Leader method could reflect the distribution characteristics of the data object. The experimental results conducted on the UCI database show that the KLG and KGL algorithms significantly outperform the traditional and other initialization K-means algorithms.

Considering that contact duration has significant influence on whether packet can be transmitted successfully or not, the authors proposed a Probabilistic Routing Protocol using History of Encounters and Transitivity based on Contact Duration (PRoPHET-CD), which combined contact duration with encounter frequency to estimate delivery probability. This protocol could improve the delivery probability significantly and reduce the interruption of packet transmission. The simulation results show that the protocol of PRoPHET-CD can significantly enhance the message delivery probability and reduce the overhead ratio.