AWGN_Channel_BCH_8DPSK

这是一个统计模块的C++小程序源代码，供大家学习参考用。(err)

matlab语音信号处理工具箱 本工具箱可配合语音数据库使用，用于计算线性预测语音模型的参数，语音声调转换，语音自动解析分解，语音语速变换及更改发音的重音，音量，清晰度等。配套图书《Speech Processing and Synthesis Toolboxes》已经出版，翻译进行中 (matlab signal processing toolbox with the voice of the Toolbox database can be used for calculating linear predictive speech model parameters, voice tone conversion, automatic speech analysis decomposition, transformation and change of voice speech rate accented pronunciation, volume, clarity, etc. . Matching book " Speech Processing and Synthesis Toolboxes" has been published in translations)

一种新的快速傅立叶变换的程序-Matlab实现(A program for a new fast Fourier transform)

MATLAB GUI操作相关指南，有详细的例程，方便初学者学习及探索。对于新手而言，有较高的使用价值。提供了较为简洁的开发平台手册。通俗易懂，方便使用。(MATLAB GUI related to the operation guide, with detailed routines, easy for beginners to learn and explore. For starters, there is a higher use value.)

Draws Phase Portraits

这个文件是，opendss与matlab交互编程的标准示例，可以帮助学习者更快的掌握opendss与matlab的交互(This file is a standard example opendss interact with matlab programming, can help learners to quickly grasp the interaction with the matlab opendss)

parzen窗函数估计，matlab代码(parzen window function estimation, matlab codes)

说明：  最速下降法matlab源码 有一定参考价值(Steepest descent method matlab source has certain reference value)

通过设计一个二阶加权系数自适应横向FIR滤波器，对一个加随机噪声的正弦信号实现滤波。 具体设计方案为： 1，生成标准正弦信号S 2，生成等长的随机信号N 3，生成加随机噪声的正弦信号X 4，X通过参数可调数字滤波器，输出Y 5，Y与参考信号作差得到误差E 6，E通过自适应算法调整权值W 7，用LMS算法处理噪声干扰的信号，最终实现滤波器功能(A two order weighted coefficient adaptive transverse FIR filter is designed to filter a sinusoidal signal with random noise. The specific design scheme is as follows: 1, generating a standard sinusoidal signal S 2, generating an equal length random signal N 3, generating a sinusoidal signal X with random noise 4, X can adjust the digital filter by parameter and output Y 5, the difference between the Y and the reference signal is E 6, E adjusts the weight W through the adaptive algorithm 7, the LMS algorithm is used to deal with the noise interference signal, and the filter function is finally realized.)

In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications. For example, in some disease diagnosis, the cost of erroneously diagnosing a patient as healthy is much higher than that of diagnosing a healthy person as a patient. Also, the acquisition of labeled data requires medical diagnosis which is expensive, while the collection of unlabeled data such as basic health information is much cheaper