radarsystemsimulate2

给音乐添加噪声再通过低通滤波器滤掉噪声，性能改善过后能明显看到效果。(Add noise to the music and through a low pass filter filters noise, performance improvement can be evident after the effects.)

fast motion estimation

MATLAB下的音频AGC实现 (MATLAB audio AGC )

Design of a Nonlinear PID Neural Trajectory Tracking Controller for Mobile Robot based on Optimization Algorithm

dijkstra algorithm as a shortest path finder in terms of cost very simple for the beginners

the use of coherent space-frequency coding in orthogonal frequency division multiplexing (OFDM)-based frequency-selective multiple-input multiple-output (MIMO) antenna fading channels has been proposed. Acquiring knowledge of the fading coefficients in a MIMO channel is already very challenging in the frequency-flat (fast) fading case. In the frequency-selective case, this task becomes significantly more difficult due to the presence of multiple paths, which results in an increased number of parameters to be estimated. In this paper, we address code design for noncoherent frequency-selective MIMO-OFDM fading links, where neither the transmitter nor the receiver knows the channel. We derive the code design criteria

a matlab file to develop models for underwater acoustic communication networks

The EnumWindows function enumerates all top-level windows on the screen by passing the handle to each window, in turn, to an application-defined callback function. EnumWindows continues until the last top-level window is enumerated or the callback function returns FALSE.

本文二次更新型粒子群算法（ TUPSO）的基本思想是：一方面，在保留粒子群算法中快速收敛， 易于实现， 调整参数少等优势的前提下，通过融合基本粒子群算法中的信息共享原理与 DPSO-TSP 中的 2-Opt 运算法则，改进了DPSO-TSP中粒子进化的规则(In this paper, the basic idea of the new particle swarm optimization (TUPSO) algorithm is: on the one hand, by integrating the particle swarm optimization algorithm in the fast convergence, easy to achieve, adjust the parameters of less advantage of the premise, through the integration of basic particle swarm algorithm information sharing Principle and the 2-Opt algorithm in DPSO-TSP improves the rule of particle evolution in DPSO-TSP)

说明：  工程实际中的许多优化问题是多目标的优化设计问题。通常情况下多个目标是处于冲突状态的,不存在一个最优设计使所有的目标同时达到最优,一个目标性能的改善,往往以其它一个或多个目标性能的降低为代价。无论采用自适应遗传算法还是其它在标准遗传算法基础上经过改进的遗传算法,如果采用加权组合法等将多目标转化为单目标的处理方法,就无法充分发挥遗传算法的优势。如果把遗传 算法与适用于多目标处理的几此协方法相结合,就能完全发挥遗传算法在多目标优化设计中的作用。(Many optimization problems in engineering practice are multi-objective optimization design problems. Generally, multiple goals are in conflict. There is no one optimal design to achieve all goals simultaneously. The improvement of one goal's performance is often at the expense of the performance of one or more other goals. Whether adaptive genetic algorithm or other improved genetic algorithm based on standard genetic algorithm is adopted, if weighted combination method is adopted to transform multi-objective into single-objective processing method, the advantages of genetic algorithm cannot be fully utilized. If the genetic algorithm is combined with several cooperative methods suitable for multi-objective processing, the role of genetic algorithm in multi-objective optimal design can be fully exerted.)