caikuang

边缘检测 基于matlab的图像边缘检测(Edge detection based on image edge detection matlab)

说明：  《数字通信（第四版）》答案 《数字通信》(第4版)是数字通信领域的一本经典教材,通过对概率论及随机过程的复习,详细介绍了数字和模拟信源编码、数字调制信号和窄带信号与系统的特征、加性高斯白噪声中数字通信的调制和最佳调制与检测方法、基于最大似然准则的载波相位估计和定时同步的方法、不同信道模型的信道容量及随机编码、带限信道的信号设计、受到符号间干扰恶化信号的解调与检测问题、自适应信道均衡、多信道与多载波调制、扩展频谱信号和系统、衰落信道上的数字通信。(The solution of "Digital communication" " Digital communication" (4th edition) is a digital communications of a classic textbook in the field, through the review process, the probability of randomly addressed, details of the digital and analog source coding, digital modulation signal and the narrowband signal and system characteristics, additive white Gaussian noise in digital communication modulation and the best modulation and detection methods, based on maximum likelihood criterion of carrier phase estimation and time synchronization method, different channel model of channel capacity and random coding, band-limited channel signal design by ISI worse signal demodulation and detection, adaptive channel equalization, multi-channel and multi-carrier modulation, spread spectrum signals and systems, fading channel digital communications.)

通过MATLAB仿真实现梯度校正最小二乘法的验证。(Gradient through the MATLAB simulation verification correction least square method.)

基于阈值的脑磁共振图像分割，其中的所有代码。(Brain magnetic resonance image segmentation based on the threshold, all code.)

通过提取不变量特征来实现两幅图像之间的匹配(matching by two pointwhich are sift)

Spectrum sensing SCS method

在matlab或者PSCAD中对桥差不平衡电流进行仿真计算，得到仿真结果与常规计算结果一致，此方法修改简单快捷。(In matlab or PSCAD to bridge the difference between the current imbalance in the simulation calculation, the simulation results are consistent with conventional calculations, this method is simple and quick modification.)

该程序包实现了VBLAST结构的传输下，接收端采用ZF、ZF_SIC、MMSE、MMSE-SIC等检测算法时误码率和信噪比的性能比较(The package implements the transmission of VBLAST structure, the receiver with ZF, ZF_SIC, MMSE, MMSE-SIC detection algorithm for error rate and SNR performance comparison)

雷达方程，可以输入参数来求解雷达方程，亲测可以运行(radar equation)

This code use Principle Component Analysis(PCA)for feature extraction