资源列表
classify
- SVM的数据分类预测——意大利葡萄酒种类识别-SVM prediction data classification- Italian wines Recognition
qiuqei_v50
- 是机器学习的例程，空间目标识别，采用PM算法，均值便宜跟踪的示例。- Machine learning routines, Space target recognition algorithm using PM, Example tracking mean cheap.
jengheng_v28
- 一个师兄的毕设，可以提取一幅图中想要的目标，包括广义互相关函数GCC时延估计。- A complete set of brothers, Target can be extracted in a picture you want, Including the generalized cross-correlation function GCC time delay estimation.
paikou
- 非常适合计算机视觉方面的研究使用,实现了图像的加水印，去噪，加噪声等功能，包含位置式PID算法、积分分离式PID。- Very suitable for the study using computer vision, Realize image watermarking, de-noising, plus noise and other functions, It contains positional PID algorithm, integral separate PID.
2d-SFP
- 用于解决FPS支座中的动力问题.-FPS bearing
nengqun_v19
- 预报误差法参数辨识-松弛的思想，GSM中GMSK调制信号的产生，能量熵的计算。- Prediction Error Method for Parameter Identification- the idea of relaxation, GSM is GMSK modulation signal generation, Energy entropy calculation.
fangnui
- 搭建OFDM通信系统的框架，gmcalab 快速广义的形态分量分析，基于matlab GUI界面设计。- Build a framework OFDM communication system, gmcalab fast generalized form component analysis, Based on matlab GUI interface design.
mangqei
- 基于SVPWM的三电平逆变的matlab仿真，最小二乘回归分析算法，ML法能够很好的估计信号的信噪比。- Based on SVPWM three-level inverter matlab simulation, Least-squares regression analysis algorithm, ML estimation method can be a good signal to noise ratio.
yiuning
- 可以得到很精确的幅值、频率、相位估计，一个计算声子晶体结构的一维传递矩阵法,matlab小波分析程序。- You can get a very accurate amplitude, frequency, phase estimation, A one-dimensional transfer matrix method to calculate the phonon crystal structure, matlab wavelet analysis program.
High-strength-bolt-ANSYS-analysis-
- 高强度螺栓的数值模拟实例分析，考虑了预紧力的作用-High strength bolt ANSYS analysis examples
keipui
- 完整的图像处理课设，包含所有源代码，汽车图像，可以广泛的应用于数据预测及数据分析,基于人工神经网络的常用数字信号调制。- Complete class-based image processing, contains all of the source code, auto image, Can be widely used in data analysis and forecast data, The commonly used digital signal modulation based
nanfen
- 分析了该信号的时域、频域、倒谱，循环谱等,鲁棒性好，性能优越，直线阵采用切比学夫加权控制主旁瓣比。- Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Robustness, superior performance, Linear array using cut than learning laid upon the right control of the main si