back_seg_code

关于K聚类的源程序，实现信号的分选，自己论文编写的，希望对大家有帮助(Clustering on the K source, the realization of signal separation, the preparation of their papers, and I hope all of you will help)

利用matlab实现图像中字母获数字的识别。(use matlab to recognized the words or figures)

基于matlab的jpeg图像的压缩算法。(Based on matlab in jpeg image compression algorithm.)

对信号进行频谱分析及滤波，完整的基于HMM的语音识别系统，雅克比迭代求解线性方程组课设。( The signal spectral analysis and filtering, Complete HMM-based speech recognition system, Jacobi iteration for solving linear equations class-based.)

KARNAUGH_INPUTS Boolean inputs for a Karnaugh map. K = KARNAUGH_INPUTS(N) produces a table (array) of boolean inputs for a Karnaugh map.Each boolean input has N bits, and there are 2^N entries in the table.

PAM误码率的程序,可以直接运行,有图片(PAM error rate of the procedure, can be directly run, there are picture)

DESIGN and IMPLEMENTATION of a SHUNT ACTIVE POWER FILTER with REDUCED DC LINK VOLTAGE

研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度 函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了 基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不 仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知 质量。 (Recent research indicates that the speech spectral amplitude distributions could be fairly described with super-Gaussian probability density function. However, the complexity of speech signal determines that the distribution statistics ofspeech signal could not be well described by single simple function. Thus a super-Gaussian mixture model for speech spectral amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech signals spectral amplitude is derived. The simulation results show that this algorithm based on Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech distortion as compared with the conventional short-time spectral amplitude estimation algorithm. )

自己编写的HYMOD水文模型模块，能够直接运行得到HYMOD的结果。(I have written HYMOD hydrological model module, capable of running HYMOD results obtained directly.)

Bradley local image thresholding The brief idea of the algorithm is that every image s pixel is set to black if its brightness is T percent lower than the average brightness of the surrounding pixels in the window of the specified size, otherwise it is set to white.