Recently,with the development of new-generation mobile communication systems,the demand for high data rates and large bandwidth is increasing.In this study,we proposed a second-order Raman fiber amplifier designed with two second-order pumps and four first-order pumps to amplify the C+L full-band signal light using a tellurium-based optical fiber as the transmission medium,which can effectively alleviate optical communication network challenges due to bandwidth growth.First,a simplified second-order Raman coupled wave equation is solved numerically,and then the pumping parameters of the second-order Raman fiber amplifier are optimized using a cooperative search algorithm to improve output performance.Meanwhile,the performance of first-and second-order Raman fiber amplifiers under the same pump parameter configuration is analyzed.Additionally,the influence of two key factors,that is,second-order pump optical power and fiber length on the average output gain and gain flatness of a designed second-order tellurium-based Raman fiber amplifier are investigated.Experimental results show that the average output gain of the designed second-order tellurium-based fiber Raman amplifier is 27.3601 dB and the gain flatness is 0.6601 dB in the ultrawide bandwidth range of 1530-1630 nm.

Aiming at the problems of insufficient target extraction and loss of details in infrared and visible image fusion algorithm, an infrared and visible image fusion method based on improved region growing method (IRG) and guided filtering is proposed. First, use IRG to extract targets from infrared images, then use NSST for infrared and visible images, and conduct guided filtering for the obtained low-frequency and high- frequency components. The filtered infrared and visible low-frequency components get low-frequency fusion coefficients through IRG based fusion rules, and the enhanced high-frequency components get high-frequency fusion coefficients through dual- channel spiking cortical model (DCSCM). Finally, the fused image is obtained by NSST inverse transform. The fused image is evaluated with subjective evaluation and 6 common objective evaluation indexes. The experimental results show that the proposed algorithm has obvious advantages in subjective and objective evaluation, such as prominent target, clear background information, strong detail retention ability.

To solve the problems of vague targets, detail loss, and algorithm instability in traditional infrared and visible-light image fusion algorithms, a fusion method based on fuzzy c-means（FCM） clustering and guided filtering is proposed. The low-frequency sub-band was enhanced by guided filtering after applying a non-subsampled shearlet transform（NSST） to the original image. The low-and high-frequency sub-bands were then fused using FCM clustering and a dual-channel spiking cortical model. Finally, the fused image was obtained using an inverse NSST transform. The experimental results showed that the proposed algorithm was stable, the fusion image had clear targets and relatively complete details in the subjective evaluation, and the algorithm had an excellent standard deviation, mutual information, average gradient, information entropy, and edge retention factor in the objective evaluation.

提出了一种将樽海鞘群算法优化极限学习机与自适应差分进化算法相结合的方法,并利用该方法优化多泵浦拉曼光纤放大器的参数配置.采用极限学习机构建泵浦参数和拉曼增益之间的非线性映射,并利用樽海鞘群优化算法对极限学习机参数进行优化获得最佳模型.对比分析了上述模型与BP神经网络和传统的极限学习机模型在评价指标方面的差异,结果表明本文所提出的模型预测性能较好.为了提高增益平坦性,利用自适应差分进化算法优化泵浦参数,得到最佳的参数配置.仿真结果表明,利用该方法设计出的拉曼放大器达到了预期效果,其目标增益与预测增益的最大误差不超过0.5 dB.该方法为今后拉曼光纤放大器的设计提供了一种新的思路方法.

Traditional fusion algorithms of infrared and visible images often have defects such as insufficient target extraction and loss of details, which lead to unsatisfactory fusion effects, and the fused image can not be applied to target detection, tracking or recognition. Therefore, a fusion method of infrared and visible images based on guided filtering and improved maximum Shannon entropy segmentation method using Ant Lion Optimization algorithm（ALO） is proposed. First, Ant Lion Optimized Maximum Entropy Segmentation（ALOMES） algorithm is used to extract the target from infrared image. Then, the Non-Subsampled Shearlet Transform（NSST） is performed on the infrared and visible images to obtained the low frequency and high frequency sub-bands, and conduct guided filtering for obtained sub-bands. The low-frequency fusion coefficient is obtained from the extracted target image and the enhanced infrared and visible low-frequency components through the fusion rule based on ALO-MES. And the high-frequency fusion coefficient is obtained by the enhanced high-frequency sub-bands components through Dual-Channel Spiking Cortical Model（DCSCM）.Finally, the fusion image is obtained by inverse NSST transform. The experimental results show that the proposed algorithm can get fusion image with clear target and background information.

The fusion of infrared and visible images is a crucial subject in the field of infrared technology. To obtain clear target and rich detail fusion images, this paper proposes a fusion method for infrared and visible images based on an improved two-dimensional Kanidakis entropy segmentation method optimized by the Grey Wolf Optimizer (GWO) and fast-guided filtering. First, the Simplified Two-dimensional Kaniadakis entropy segmentation algorithm using Grey Wolf Optimizer (GWO-S2DKan) is used to fully extract the target from the infrared image. Next, the Non-Subsampled Shearlet Transform (NSST) is performed on both visible and infrared images to obtain the low-frequency and high-frequency sub-bands, respectively. Fast-guided filtering is then conducted on the high-frequency components to retain rich details in the visible image. The low-frequency fusion coefficient is obtained from the extracted target image and the infrared and visible low-frequency components using a fusion rule for low-frequency. The high-frequency fusion coefficient is obtained from the enhanced high-frequency sub-band components using the Dual-Channel Spiking Cortical Model (DCSCM). Finally, the fusion image is obtained by inverse NSST transform. Experimental results demonstrate that the fusion image obtained by the proposed algorithm has clear targets, background information, and stable performance.

We propose a second-order Raman fiber amplifier gain and noise co-prediction model combining convolutional neural network and long short-term memory network to study the influence of different LSTM layers on the performance of the prediction model, and the optimal parameter configuration model was obtained by optimizing with the Seahorse Algorithm. The model can accurately reflect the mapping relationship between pumping parameters, fiber length, target gain, and noise distribution, and effectively improves the design efficiency and performance of Raman fiber amplifiers. The experimental results show that the root mean square error of the finally established SHO-CNN-LSTM model in terms of gain and noise prediction is only 0.0431 and 0.0224dB, the error between the predicted value and the target value does not exceed 0.26dB, and the average design time does not exceed 0.0002s. This design scheme provides the best design methods and ideas for the flexible and fast design of future Raman fiber amplifiers.

A fusion algorithm based on Improved Multi-seed Region Growing combined with Dual Channel Spiking Cortical Model (DCSCM) is proposed to improve the problem of incomplete and unstable target extraction in traditional infrared and visible image fusion algorithm. Firstly, the source infrared image and the source visible light image are decomposed into their own high and low frequency subband coefficients by using the Nonsubsampled Shear-let Transform (NSST). Then, the target information of the source infrared image is extracted by using the improved multi-seed region growing, and the low-frequency fusion coefficient is obtained by comparing the information entropy. The high-frequency fusion coefficient is obtained by using DCSCM. Finally, the fused image is obtained by inverse NSST transform. Simulation results show that the fused image obtained by the algorithm proposed in this paper is rich in infrared target information and detailed texture information, and has good visual effect. In objective evaluation, the fused image has higher standard deviation, information entropy, mutual information and edge preservation coefficient, it has obvious advantages over other methods in subjective and objective aspects.