Squeezer

自动控制理论中利用matlab编写二阶系统分别在欠阻尼，临界阻尼，过阻尼时的情况(Automatic control theory using matlab write second-order system in underdamped, critically damped, overdamped)

关于RFID的LANDMARC算法与加权计算平均功率的计算(About RFID' s LANDMARC algorithm and the calculation of weighted average power)

Digital Transmission

digital signal processing code for fir filter

说明：  kalman 跟踪滤波，基于matlab语言编写，可运行(kalman tracking filter, based on the matlab language, can be run)

说明：  图像阈值分割技术在图像分割中具有重要的意义。遗传算法既可以对全局信 息高效利用，也减少了存储空间和计算量。利用遗传算法的优点，提出了一 种用改进遗传算法的图像分割的方法，可以将父代中适应度大的个体代替当前代 中的个体，保证了可以选出最优的个体。遗传算法是具有鲁棒性和自适应性的搜 索方法.通过实验对比，表明该方法具有快速、稳定分割的特点。(Image threshold segmentation technology is of great significance in image segmentation. Genetic Algorithms can be used for global information Efficient utilization of information also reduces storage space and computation. Based on the advantages of genetic algorithm, a new genetic algorithm is proposed. A method of image segmentation based on improved genetic algorithm can replace the current generation with individuals with large adaptability in the parent generation. The individual in the system ensures that the best individual can be selected. Genetic Algorithms are Robust and Adaptive Search The experimental comparison shows that the method has the characteristics of fast and stable segmentation.)

基于局域均值的盲源分离技术在故障诊断中的应用文章，希望对大家有用！(Based on Local Mean Blind Source Separation Techniques in Fault Diagnosis article, I hope useful for everyone!)

its a code used to explain the complete dscription of the matlab code in matlab

Mann-Kendall 方法是一种非参数统计检验方法。非参数检验方法也称无分布检验,其 优点是不需要样本遵从一定的分布，改进了其中缺点，使用效果更好(Mann-Kendall method)

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)