allfns

矢量算法，计算声源位置。速度比较快，精度很高。(Vector algorithm to calculate the location of a sound source. Relatively fast speed, high precision.)

采用matlab或其它软件工具编程实现。 ">

Texture Analysis Using DWT for Grape Plant Species Classification:- By observing leaf, we can easily identify plant but it is difficult to identify its species. In this project we developed an algorithm that gives user the ability to identify plant species based on photographs of the leaf. The core theme of this application is an algorithm that acquires statistical features of the leaves, and then classifies the species based on a novel combination of the computed texture feature analysis and wavelet analysis. While implementing this algorithm we have considered the Grape plant and its four species viz. Clone, Sonaka, Thomson and Manik. The algorithm is first trained against several samples of known plant species and then used to classify unknown query species. By using this algorithm we have achieved 93.33 efficiency

面阵阵列天线二维方向图仿真，代码简单易懂(The two-dimensional surface bursts columns antenna pattern simulation, the code is easy to understand)

实现LSB数字图像水印算法，限于BMP图像(LSB digital image watermarking algorithm to achieve, limited to the BMP image)

对图像进行预处理，然后进行经典的阈值分割，形态学分割以及种子区域生长。(Preprocess the image, and then the classic threshold segmentation, morphological segmentation as well as seed region growing.)

信道仿真程序，通过simulink实现BPSK调制在二进制对称信道、AWGN、RAyleigh、Rician信道下的误比特率仿真，并提供了Okumura-Hata模型函数、COST231-WI视距模型函数、COST231-WI非视距模型函数、COST-231 Hata模型仿真(Channel simulation program, through the simulink to realize BPSK modulation in binary symmetric channel, AWGN and RAyleigh and Rician channel bit error rate of the simulation, and provides Okumura Hata model function, COST231 WI stadia model function, COST231- WI non line-of-sight Hata model simulation model function, COST- 231)

review of super-resolution

随机信号功率谱估计，在频域对信号进行分析，用Matlab编程(Random signal power spectrum estimation using Matlab programming)

SVM向量机用于目标分类的代码，代码很简单，可以运行，适合新手(The code for the target vector machine SVM classification code is very simple, it can run for novice)