Cuckoo Search (CS) is a Meta-heuristic method, which exhibits several advantages such as easier to application and fewer tuning parameters. However, it has proven to very easily fall into local optimal solutions and has a slow rate of convergence. Therefore, we propose Modified cuckoo search algorithm with variational parameter and logistic map (VLCS) to ameliorate these defects. To balance the exploitation and exploration of the VLCS algorithm, we not only use the coefficient function to change step size $\alpha$ and probability of detection $p_a$ at next generation, but also use logistic map of each dimension to initialize host nest location and update the location of host nest beyond the boundary. With fifteen benchmark functions, the simulations demonstrate that the VLCS algorithm can over come the disadvantages of the CS algorithm.In addition, the VLCS algorithm is good at dealing with high dimension problems and low dimension problems.

波导不变量有两种定义方式，按照干涉条纹斜率的定义适合工程应用，按照简正波频散的定义适合理论分析.在有跃层的波导中，这两种定义方式并不完全一致.由于简正波频散特性差异，按照频散特性定义的波导不变量β会有许多不同的取值，这些β被称为波导不变量的β谱.此时声场干涉结构应该用多个β来描述，而以往的β提取算法只能给出一个最佳估计值，导致一些信息的丢失.借鉴图像处理中的积分投影概念，将声强图像按照角度进行积分投影，以分离不同斜率的条纹成分；然后对投影曲线进行傅里叶变换，以分离不同间距的条纹成分，最终实现各个β谱值的分离.仿真和实验结果表明，β谱分离算法可以从声场干涉结构中有效地提取不同条纹成分的β，并将其映射到二维平面内，具有更强的噪声抑制能力，所以能在更低的信噪比条件下使用.

Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO) from two aspects: first, we introduce differential evolution (DE) process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS) process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

Marine surveillance radar plays an important role in national security defense, but the islands or land will introduce discrete or flaky strong clutter, which may result in high false alarm probability. In order to solve the problem of serious degradation of clutter suppression performance in target detection, a land-sea separation algorithm based on phase correlation is proposed in this paper. In the proposed algorithm, the land clutter and sea clutter are distinguished according to the characteristics that the phases of land echo sequences in adjacent circles are with strong correlation while the phases of ocean echo sequences are random. Finally, the proposed algorithm is validated based on the measured data of radar placed at the top of a mountain near coastline.

In cognitive radio networks (CRNs), improving system utility and ensuring system fairness are two important issues. In this paper, we propose a spectrum allocation model to construct CRNs based on graph coloring theory, which contains three classes of matrices: available matrix, utility matrix, and interference matrix. Based on the model, we formulate a system objective function by jointly considering two features: system utility and system fairness. Based on the proposed model and the objective problem, we develop an improved gravitational search algorithm (IGSA) from two aspects: first, we introduce the pattern search algorithm (PSA) to improve the global optimization ability of the original gravitational search algorithm (GSA); second, we design the Chebyshev chaotic sequences to enhance the convergence speed and precision of the algorithm. Simulation results demonstrate that the proposed algorithm achieves better performance than traditional methods in spectrum allocation.