SingleFreqCancel

最大似然估计法的matlab实现，及Crammer-lor下界的确定(Maximum likelihood estimation of the matlab implementation, and Crammer-lor to determine the lower bound)

求音频的掩蔽曲线,可以用于音频提取水印,还可以用于音频分析(For audio masking curve, can be used for audio watermark extraction can also be used for audio analysis)

Image Demosaicing by bilinear interpolation method

相位解缠是INSAR数据处理的重要步骤，本文用基于FFT的最小二乘法实现实现相位解缠(Phase unwrapping)

说明：  利用AMDF法求语音信号的基音周期（结果为时频图）(Pitch detection by AMDF algorithm)

说明：  用于方程求根的二分法。方程求根。二分法。(Dichotomy method to solve the functions.)

这里主要对LMS算法及一些改进的LMS算法（NLMS算法、变步长LMS算法、变换域LMS算法）之间的不同点进行了比较，在传统的LMS算法的基础上发展了LMS算法的应用。(here on the main LMS algorithm and some improvements to the LMS algorithm (NLMS. modified LMS algorithm, transform domain LMS algorithm) between the different points of comparison, in the traditional LMS algorithm developed on the basis of the LMS algorithm application.)

本系统是LTE系统同频邻区自动邻区关系_ANR_的研究与实现(This system is the study and implementation of LTE systems with frequency automatic neighbor relations of neighborhood _ANR_)

这是一本研究目标定位跟踪的学习手册（在雷达，声纳，无线传感器网络领域等），采用的方法有最小二乘/极大似然，卡尔曼滤波，蒙特卡洛，粒子滤波等方法，更是很多读者需要的是里面有大量的仿真实例和程序代码，并配以中文注释，读者可以结合书中的原理和公式，对程序有进一步的理解。也适合很多读者在已有的代码的基础上对算法进行改进。(This is a study targeting the learning manual tracking (radar, sonar, wireless sensor networks, etc.), using the least squares method/maximum likelihood, Kalman filtering, Monte Carlo, particle filtering methods , but many readers need is there are a large number of simulation examples and program code, and with Chinese notes, the book the reader can combine the principles and formulas, a better understanding of the program. Also suitable for many readers on the basis of the existing code to improve the algorithm.)

维纳滤波其设计，假设一个点目标在x，y平面上绕单位圆做圆周运动，由于外界干扰，其运动轨迹发生了偏移。其中，x方向的干扰为均值为0，方差为0.05的高斯噪声；y方向干扰为均值为0，方差为0.06的高斯噪声。(Wiener filter is designed, assuming a point target in the x, y plane around the unit circle in a circular motion due to external interference, its trajectory shift occurred. Which, x the direction of the interference of mean 0 and variance of 0.05 Gaussian noise y direction of the disturbance is zero mean Gaussian noise variance 0.06.)