new-matlab-project

The above file show the Independent componets analysis used in face recognisation

实现图像模糊C_均值聚类分割，包含算法思想和matlab源程序(Fuzzy C_ c-mean partition)

改进的总体经验模态分解,效果不错,欢迎讨论(Improved overall empirical mode decomposition, the effect is good, welcome to discuss)

关于穿墙雷达探测与监视的一篇较新国外论文，并附有程序(The title of this dissertation:Through-wall human monitoring using data-driven models with Doppler information)

说明：  用matlab实现SVM，并与BP神经网络进行对比(Implement SVM, with matlab and compare it with BP neural network)

zernike 矩 不变矩特征 zernike 矩 不变矩特征(zernike Moment Moment Invariant Feature zernike Moment Moment Invariant Feature)

人脸识别方面PCA算法的matlab实现(face recognition aspects of the PCA algorithm to achieve Matlab)

这是基于MATLAB的人脸识别，将人脸基本能够清晰的识别出来(This is a MATLAB-based face recognition, basic human face can be clearly identified)

The signal received by a user over the forward link in such a high data rate system contains interference from the neighboring base stations. we note that the strength of this interference is a function of the network load in the neighboring cells. we obtain an expression for the received SINR (Signal to Interference and Noise Ratio) as a function of the traffic load in the interfering cells. Using this result, we propose an improvement to the SINR estimation scheme that is implemented in the current cellular data network. The proposed scheme provides a more accurate estimate of the user SINR by taking better account of the contribution of inter-cell interference. It builds on top of the current SINR measurement scheme by using a combination of pilot measurement and traffic load measurement

文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型(This paper presents methods for speech spectrum prediction based on Gaussian mixture models. Spectrum prediction may be useful in a packet transmission system where the sensitivity to packet losses is a major problem. Models of speech are trained by the Expectation Maximization algorithm using pairs, triples etc. of consecutive cepstral vectors. The models are used to design first, second etc. order predictors. The prediction schemes are evaluated using the spectral distortion criterion and compared to a simple reference method. The best prediction scheme obtains an average spectral distortion that is 0.46 dB less than for the reference method. )