In this paper, we propose the energy-tier template insertion shaping (ETTIS) probabilistic shaping algorithm, which performs shaping and deshaping through preset templates and simple bit insertion/deletion operations, thereby significantly reducing the algorithmic complexity. The ETTIS algorithm effectively mitigates the Hamming distance shrinkage problem commonly observed in traditional probabilistic shaping algorithms and exhibits excellent compatibility, allowing seamless integration with standard forward error correction coding and interleaving algorithms. Moreover, the ETTIS framework utilizes predefined symbol components in the shaping templates to implicitly introduce pilot symbols, enabling real-time estimation of channel gain and noise variance without additional bandwidth overhead.. Experimental and simulation results show that, at the same bit rate, the proposed scheme achieves 0.4–0.6 dB performance gains under 16-quadrature amplitude modulation (QAM) and 64-QAM modulation formats, respectively. Compared with other state-ofthe- art algorithms, ETTIS attains comparable performance while maintaining significantly lower complexity. During decoding, the absolute deviation of the estimated noise bit error rate remains below 0.75%. These features make ETTIS a promising solution for high-throughput and cost-sensitive optical interconnect systems.

Due to different imaging mechanisms, the registration of optical and Synthetic Aperture Radar (SAR) image is a very challenging task. Many optical and SAR registration methods have been proposed. But most of them are for low-to-medium resolution images, and less for high-resolution images. Therefore, this paper proposes a high-resolution optical and SAR image registration method using local self-similar descriptor based on edge feature. Firstly, a Gauss-Gamma bi-windows algorithm is used to extract the edge intensity maps of the images respectively. Its function is to eliminate the non-linear gray-scale difference between SAR and optical images, and also to avoid the interference of isolated speckle noise on feature point extraction. Then, local self-similar descriptor is extracted on the edge intensity map, and descriptor matching is performed using Euclidean distance. Finally, the fast sample consensus algorithm is used to eliminate mismatched point pairs. The experimental results can effectively resist speckle noise and radiation differences, and obtain pixel-level registration accuracy.