ex8

BAYESIAN FILTERING: including KF, EKF, Unscented EKF, Particle Filter & etc

Contains the matlab routines to estimate a univariate markov switching multifractal model of calvet and fisher 2004

this code is calculating the transformation matrix for illuminant A and D65 using von kries predicted equation

Basically template match with rotation. Given a template as binary edge image the function find the object that match the template in the image. This work even if the template and object are in different orientations. The matching is is done by rotating the template and matching it to the image in various of angles, the best match is returned. Matching the template to the image can be done by various of template match approaches including crosscorrelation and generalized hough transform. See the readme file supplied in the code zip file for more details

说明：  蓄电池仿真模型，电池为锂离子电池，11.2KWH，模型含开路电压和内阻的数据。(Battery simulation model, the battery is lithium-ion battery, 11.2kwh, the model contains open circuit voltage and internal resistance data.)

说明：  基于已知特性信道的QAM系统的仿真和性能分析(Based on the known characteristics of the QAM channel system simulation and performance analysis)

SRGB 数据转化为XYZ 数据格式，色彩学相关研究人员会有所帮助(SRGB data into XYZ data format, color science will help the researchers)

LSB Watermarking for Image

power_zener.rar,功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_zener.rar,Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

Okumura analyzed path-loss characteristics based on a large amount of experimental data collected around Tokyo, Japan. He selected propagation path conditions and obtained the average path-loss curves under flat urban areas. Then he applied several correction factors for other propagation conditions, such as: Antenna height and carrier frequency Suburban, quasi-open space, open space, or hilly terrain areas Diffraction loss due to mountains Sea or lake areas Road slope Hata derived empirical formulas for the median path loss (L50) to fit Okumura curves.