Aiming at two inherent problems in single frame infrared small target detection (ISTD): The small target lacks local information such as color, texture and shape; The small targets are readily lost during the continuous down-sampling process that yields high-level semantic information and the global receptive field. A double-domain and global context feature extraction network (DDGC-FENet) that is both precise and quick is suggested. The model includes a dual-domain feature extraction (DDFE) module and a global context feature extraction (GCFE) module. The DDFE module simultaneously learns the local contrast information of the small target and the background in the spatial domain and the frequency domain, so as to separate the target from the background. The GCFE module can globally model the feature map after multiple down-sampling to extract the global context and prevent the loss of target features in the deep layer of the network. Furthermore, the model fuses low-level and high-level features from both row and column directions using a two-way attention fusion (TWAF) module. The suggested approach outperforms cutting-edge techniques like AGPCNet, DNANet, and ISNet in terms of mIoU, nIoU, and F1, according to experiments conducted on a number of public datasets.

Underwater visible light communication (UVLC) systems have gained a lot of attention due to the growing need for underwater communication. At present, the concept of communication and sensing integration has emerged, especially the UVLC systems’ development has become a new research hotspot. Orthogonal time-frequency space (OTFS) technology has garnered significant attention due to its exceptional performance in high-Doppler and high-delay channels, thereby providing robust communication support for the system. At the same time, linear frequency modulation (LFM) technology is widely used in the field of wireless communication due to its low sensitivity to Doppler frequency shift. In this paper, an UVLC perception integration system is designed by combining OTFS technology with LFM technology. Through experimental simulation comparison, the system performs well in bit error rate (BER), ambiguity function, target speed and distance information acquisition. The system, which combines OTFS technology and LFM technology, exhibits excellent adaptability in complex underwater environments, bringing new possibilities to the field of underwater communication. At the same time, three common modulation methods are also considered, and the possible effects of different modulations on system performance are further analyzed, offering useful insights for system optimization.

Although the existing compressed sensing-magnetic resonance imaging (CS-MRI) methods based on deep learning have achieved good results, the interpretability of these methods still faces challenges, and the transition from theoretical analysis to network design is not natural enough. In order to solve the above problems, this paper proposed a deep dual-domain geometry distillation feature adaptive network (DDGD-FANet). The deep unfolding network iteratively expanded the MRI reconstruction optimization problem into three sub-modules: data consistency module, dual-domain geometry distillation module, and adaptive network module. It could compensate for the lost context information of the reconstructed image, restore more texture details, remove global artifacts, and further improve the reconstruction effect. Three different sampling modes were used in the public dataset. The results show that DDGD-FANet achieves a higher peak signal-to-noise ratio and structural similarity index in all three sampling modes. At the Cartesian 10% compressed sensing(CS )ratio, the peak signal-to-noise ratio is increased by 5.01 dB, 4.81 dB, and 3.34 dB, respectively, higher than that of iterative shrinkage-thresholding algorithm (ISTA)-Net +, fast ISTA (FISTA)-Net, and DGDN models.