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tiep tuc vay thoi chu con gi hon dau

It is related to image processing

CRC生成程序，可以自定义CRC位数等参数。十分具有参考价值(CRC generator, you can customize the CRC bits and other parameters. Is a reference value)

说明：  频域宽带波束形成，使用了LCMV算法，非常好用(Broadband beamforming in frequency domain)

this is the source of sobel filter

Shepp-Logan CT 旋转中心偏移伪像仿真，矫正 (Shepp-Logan CT artifact rebuilt)

Simplified Bulirsch-Stoer method for intergration of y = F(x,y).

matlab数字图像处理常用语句，方便查询和学习。分享给大家。(matlab digital image processing commonly used sentences, easy access and learning. For everyone to share.)

Visual C++_MATLAB图像处理与识别实用案例精选+源代码(Visual C++ _MATLAB image processing and recognition of practical case selection+ Source Code)

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)