ModelingAccelSigProcess

一个详细的MIMO编程，包含各种衰落模型，运用空时网格码实现。(MIMO a detailed program includes a variety of fading model, the use of space-time trellis code to achieve.)

纯弯构件受力分析，曲率-弯矩关系，荷载-挠度关系。(Pure bending component stress analysis, curvature- the moment the relationship between the load- deflection relationship.)

search for u,m of moment invariant (search for u,m of moment invariant)

matlab 初级学习。。。。。。。。。。。。。。。。。。。。。。。。。。(matlab primary learning. . . . . . . . . . . . . . . . . . . . . . . . . .)

载波恢复算法的MATLAB实现，很具有参考价值，精度满足要求(practice algorithm for carrier frequency estimate)

对ofdm系统进行信道估计，特别是对时变信道，受多径和多普勒影响严重的系统，算法收敛性很好(Ofdm system for channel estimation, especially for time-varying channel, affected by severe multipath and Doppler system is very good convergence)

：探索一种能有效抑制和消除颅脑MRI运动伪影产生的方法，以提高图像质量和工作效率。方法：利用包装CT管球的海绵，设计并制作了2 cm x 7．5 cm x 22 cm，3 cm x 7．5 cm x 22 cm，4 cm×7．5 cm x 22 em等3种规格的海绵垫， 配合使用颅脑正交线圈进行颅脑MRI检查。结果：采用“加垫法”行颅脑MRI检查2 638例。其中，一次性扫描成功2 633 例(占99．81％)，失败5例(占0．19％)； 在扫描失败的5例患者中，第2次扫描时配合使用医用绷带成功4例。失败l例。结论：采用“加垫法”行常规颅脑MRI扫描，方法简单、可靠。可有效抑制和消除运动伪影的产生，提高图像质量和工作效率。(To explore a brain can effectively inhibit and eliminate motion artifacts of MRI approach to improve the image quality and efficiency. Methods: The CT tube packing sponge ball, designed and made 2 cm x 7.5 cm x 22 cm, 3 cm x 7.5 cm x 22 cm, 4 cm × 7.5 cm x 22 em 3 sizes, etc. sponge pad, with the quadrature head coil using brain MRI. Results: The " plus cushion Law" the Brain MRI examination 2 638 cases. Among them, the one-time scan is successful 2 633 cases (99.81 ), failed in 5 cases (0.19 ) Conclusion: " plus cushion Law" routine brain MRI scan, the method is simple and reliable. Can effectively suppress and eliminate motion artifacts, and to improve image quality and efficiency.)

PCA的原代码，绝对好使，大家用了就知道~~~希望对大家有帮助(the original code, absolutely so, we have spent know ~ ~ ~ hope to be helpful)

模式识别中的经典算法，感知器算法在matlab中的编程实现(The classic pattern recognition algorithms, perception algorithm in matlab programming)

image processing and pattern identification