sliderPanel

数组图像处理阈值分割 ＭＡＴＬＡＢ源程序

说明：  显示具体日期、多种整体风格还有多种日期格式(Show specific date, a variety of overall style, there are many date formats)

空时格码编码器(STTC encoder)

This source code gives an idea about how to plot PAPR vs CCDF which is an important paramenter for evalution of the schemes of PAPR reduction

一种基于3D_ESPRIT的散射中心参数估计算法_温晓杨(A 3D-ESPRIT Method for Radar Target Scattering Centers Parameters Estimation)

一类具有Holliing 功能反应的捕食者-食饵系统的反应扩散方程(a predator-prey system with reaction-diffusion and Holling functional response)

多核变换，稀疏变换，核变换自适应学习，图形图像变换(Description ----------- Multi-Kernel-Path package is a Matlab program that computes the entire regularization path for multiple-kernel learning problems. More precisely, it solves any learning problem with a smooth loss function and where the kernel itself is optimized over combinations of several basis kernels. It returns the solutions for ALL values of the regularization parameter.)

RLS的自适应滤波 有非常详细的标注 非常适合学习和修改(A very strong and degtial result)

本实验语音库为免费的柏林情感语音库，其采样频率为16KHZ，16bit量化。该语音库共有500 句情感语音信号，分别由十名专业演员(5 男，5 女)在不同情感状态下（高兴、愤怒、平静、悲伤、害怕、厌烦、憎恨）朗读十句不同文本的德语组成。本实验选取其中的部分情感（高兴、愤怒、悲伤）加以识别。仿真实验环境为MATLAB7.0。 实验选取的情感特征为短时平均能量、短时平均幅度、基频和短时过零率。为了降低不同人在表达不同情感时的个人差异造成的影响，本文实验过程中将提取的情感特征进行归一化处理。归一化采取将同一个人的三种情感语音信号的情感特征放在一起归一化处理，并将归一化后的情感特征作为KNN分类器的训练样本和测试样本。 该实验使用了传统的KNN算法和改进算法两种识别方式，通过实验结果对比了各自的特点。 (This experiment YuYinKu for free Berlin YuYinKu emotion, the sampling frequency for 16 KHZ, 16 bit quantification. The YuYinKu 500 words of emotional speech signal, respectively by ten professional actors (5 male, five female) in different emotional state (happy, anger, sadness, fear, calm, bored, hate) read ten sentences in different text of the German. This experiment selection of part of the emotions (joy, anger, sadness) is recognized. The simulation experiment environment for MATLAB7.0. The selection of emotional characteristics for short-term average energy, short-term average amplitude, frequency, and the short zero rate. In order to reduce the different people in different emotional expression of the impact of individual differences, the experiment process will extract emotional characteristics is normalized. The normalized will take the same three kind of emotional speech signal emotional features normalized put together, and after the normalized the emotional characteristics)

可以实现ofdm的LS信道估计，并与未采用估计时进行比较(The LS can ofdm channel estimation, and with no comparison with estimates)