suoxianghuan

SQP算法的matlab实现，包括子程序的实现和整个的大问题，配合使用(The sub-problem of SQP programing)

微动经典文献，可以帮助大家学习微动方面的知识。里面包含三篇文献，都是雷达目标回波的。(Fretting classic literature can help us learn fretting area. Which contains three documents are radar target echo.)

这厮压缩感知恢复算法中L1同伦优化的算法(Zhesi restore compressed sensing algorithm Homotopy algorithm optimized L1)

antenna characteristics using matlab

索书号文字图像分割 索书号文字图像分割(Call text image segmentation Call text image segmentation)

基于MATLAB/Simulink建立2RC等效电路模型，用于电池模型参数辨识(2RC equivalent circuit model for battery model parameter identification)

The combination of EEM fluorescence and parallel factor analysis has proved to be a promising tool for studying DOM. These relatively inexpensive fluorescence measurements can be used in combination with other measurements to rapidly quantify and characterize DOM across a range of environments. This approach also has great potential in a wide range of other applications such as drinking water monitoring, waste water treatment control and the evaluation of ballast water exchange in ships. However, as with many new techniques, it is not as straightforward as could be desired and there are a range of potential pitfalls. For the approach to persist and become a trustworthy method, a basis for a standardized approach was required.

说明：  对文档图像进行分割，有效去除文档的边界黑色信息，(right Document Image segmentation, effective removal of a black border documentation information,)

在基于Mean-Shift的目标跟踪算法中，尺度自适应机制是算法研究的一个重要方向。一种典型的方法采 用Lindeberg的尺度空间理论以获取目标尺度信息。但现有算法中将尺度由2．D矢量压缩为1一D量，未能精细地 刻画目标仿射变换时的尺度变化，从而限制了算法的适用范围。为此，该文将尺度维1．D滤波推广至2一D，构造得 到了相应的垂D尺度空间，并利用窄间维和尺度维的Mean-Shift交替迭代，实现了同时在空间位置和尺度方向对 目标的有效跟踪，提高了算法在目标尺度变化时的自适应性，并扩大了算法的适用范围。 关键(Scale adaptive mechanism in the target tracking algorithm based on Mean-Shift algorithm is an important direction of research. A typical method using Lindeberg' s scale space theory to obtain the target scale. However, the existing algorithms will scale from 2. D vector compression for 1-D volume, not fine to portray the target affine transformation of scale changes, thus limiting the scope of application of the algorithm. To this end, the paper will scale dimension. D filter to the 2-D structure has been the vertical D scale space and the use of narrow inter-dimension and scale dimension of the Mean-Shift alternating iteration, effective tracking of the target position in space and scale direction to improve the self-adaptive algorithm when the target scale changes, and to expand the scope of application of the algorithm. Key)

demo Learning-Vector-Quantization