To address the issue that the single red-green-blue (RGB) modality contains limited semantic information, is susceptible to noise interference, and exhibits suboptimal segmentation performance, this paper proposes an RGB-depth (RGB-D) semantic segmentation algorithm based on an asymmetric feature interaction method. First, a two-stream network is employed to extract features from the RGB and depth modalities separately. By incorporating an asymmetric feature correction module, features from one modality are used to correct those of the other, thereby suppressing intra-modal noise. Then, an asymmetric fusion module is applied to further enhance information interaction between the modalities. Additionally, multi-scale feature fusion is introduced in the decoder, and adversarial training is adopted as an auxiliary strategy during the training process to effectively leverage contextual information and improve overall accuracy. Experimental results demonstrate that the proposed algorithm effectively suppresses intra-modal noise and enhances the interaction of valid semantic information across modalities，achieving mean intersection over union（mIoU）scores of 57.4% and 52.1% on the New York University depth dataset v2（NYUDepthv2）and the Stanford University RGB-D dataset（SUN-RGBD），respectively.

Aimingat solving the problems of large amounts of computation and simplifying high complexity of M-ary low density parity check code (LDPC) decoding algorithm, a M-ary LDPC improved decoding algorithm is proposed based on the characteristics of BeiDou-3 (BD-3) B1C satellite signal. On the one hand, the proposed algorithm adopts the dimension reduction and edge complement mode to reduce the calculated amount of decoding algorithm by reducing the calculation dimension between nodes in the updating process of verification equation；On the other hand, to reduce the decoding complexity, the algorithm uses the way in which the nodes update simultaneously, which means the check node updates as the variable node updates. The simulation results show that the improved algorithm reduces the decoding computation and simplifies the decoding algorithm complexity without affecting the decoding performance in the dynamic range of receiving level of BD-3 B1C satellite signals. Compared with before optimization, the calculation amount is reduced to 25% of the original algorithm in one iteration, and 20000 times of data storaging and reading operations are saved when the calculation dimension is 32 by updating the node algorithm. Moreover, with the improvement of signal to noise ratio (SNR), the decoding computation amount and complexity can be more simplified by using smaller dimensions.