NoUltrasonicPathway

说明：  《MATLAB 7.0编程基础基础》源程序，适合初学者使用，第8章(&quot MATLAB 7.0 programming foundation base&quot source, suitable for beginners, Chapter 8)

卡尔曼滤波在仿真中的应用，低于跟踪目标有很大的作用(NNAD applied to multi-target tracking, a great help for filtering, and fusion technology)

conitnue for last m file

小波变换系数在matlab7.0中的显示代码，便于我们分析其不同方向的性质(wavelet display with matlab)

1、系统仅处于训练状态。训练序列为2cos(2π×f1×n/fs)， 干扰为sin(2π×f2×n/fs)；序列长度为100个样点。 其中n=0…99，f1=1kHz，f2=2kHz，fs=8kHz。 2、采用LMS算法，均衡器输入为训练序列加上干扰，均衡器阶数为21阶，步长因子β=0.01。 3、在Matlab中编写浮点LMS算法，绘制均衡器的输出波形，并与训练序列进行对比。 (1, the system only in the training status. The training sequence is 2cos (2πf1n/fs), interference sin (2πf2n/fs) sequence length of 100 samples. Where n = 0 ... 99, f1 = 1kHz, f2 = 2kHz, fs = 8kHz. 2, using LMS algorithm, the input to the equalizer training sequence plus interference equalizer order is 21 bands, step factor β = 0.01. 3, the preparation of the floating-point LMS algorithm in Matlab, draw the output waveform equalizer, and with the training sequence comparison.)

】通过建立倒立摆系统的数学模型,应用状态反馈控制配置系统极点设计倒立摆系统的 控制器,实现其状态反馈,从而使倒立摆系统稳定工作。之后通过MA TLAB 软件中Simulink 工具对倒立摆的运动进行计算机仿真,仿真结果表明,所设计方法可使系统稳定工作并具有良 好的动静态性能(] Through the establishment of an inverted pendulum system mathematical model, the application of state feedback control design configuration of the system pole inverted pendulum system controller, to achieve its state-feedback, so that the work of the inverted pendulum system is stable. After the adoption of MA TLAB software Simulink tool for inverted pendulum movement of the computer simulation, simulation results show that the design work will enable the system is stable and has good static and dynamic performance)

忆阻器《忆阻器非线性漂移》翻译及其忆阻器宏模型建立及其电路仿真。(memristor emulator )

PDE introdution.let u konw hot to use the PDE to reliace in programe.I t is usful to anyone who wannto use PDE in project.

Gnuradio 中MIMO系统的实现与Gnuradio安装向导(In the MIMO system to achieve Gnuradio)

the network lifetime based on a new Energy-Aware Objective Function used to design a Routing Protocol for Low-Power and Lossy Networks. The proposed Objective Function uses the Expected Transmission Count Metric and the Remaining Energy on each sensor node to compute the best paths to route data packets across the network.So EAOF prolongs the network lifetime and reduces the need for human intervention on battery changing, thereby enhancing the system wearability and reducing operational costs.