adaboost

在不同的信道条件下计算第一类优先级数据平均频谱效率的MATLAB函数(In different channel conditions, the first class priority data calculated the average spectral efficiency of the MATLAB function)

说明：  详细说明：语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等 绝对保证质量，是保研后导师布置的一些基础程序(Details: short-time speech signal analysis, including: framing, short-term energy, short-term average rate, short-time zero crossing rate, short-time autocorrelation function, short-term magnitude difference, cepstrum, complex cepstrum, lpc coefficient, lpc absolute guarantee of the quality of spectral estimation, is the security arrangement of some of the research base after the mentor program)

说明：  曲线拟合和多项式拟合~~~~~~~~~~~~(Curve fitting and multinomial fitting)

the ELM method is a learning technique, used to train a system

文中的方法是把图像分块，小波分解得到低频分量、高频分量，然后计算每一块的对比度，把图像块划分为清晰块、模糊块，把清晰块和模糊块相邻的区域定义为边界区域，融合时，直接选取清晰块作为融合后的相应块，对于边界区域，在小波分解的基础上采用基于对比度的像素选取的方法进行处理。(Paper, the method is to image segmentation, wavelet decomposition are low frequency, high frequency components, then calculate the contrast of each piece, the image block is divided into clear blocks, fuzzy block, to clear blocks and fuzzy block is defined as the border region adjacent to area, integration time, a clear block directly select the corresponding block as a fusion, for the border region, the wavelet decomposition on the basis of the pixel-based contrast method selected for processing.)

MATLAB HELP FUNDENMENTAL IN YOUR CODE 2 IN FILE

本方法是对二维医学图像实现的Walsh变换，属于预处理范围，为下一步的图像分割，配准等打下基础(This method is the realization of two-dimensional medical images of the Walsh transform, belong to the scope of pre-treatment, for the next phase of image segmentation, registration, etc. to lay the groundwork)

语音PCM编码，从输入，在高斯白噪声下传输到译码(PCM voice coding, from the importation of the white Gaussian noise transmitted to the decoder)

利用WNN对人脸进行模式识别的一些源程序，已经经过验证(WNN using pattern recognition of human faces for some source code, has been proven)

Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */