matlabexample

MATLAB implementation of SPIHT (without Arithmatic coding stage). ％ Performance evaluation : this toolbox VS. SPIHT ％ Test image: lena512.raw ％ bpp SPIHT(dB) This code(dB) ％ 0.1000 29.8107 29.3202 ％ 0.2000 32.7202 32.2514 ％ 0.3000 34.5479 34.0331 ％ 0.4000 35.8422 35.4857 ％ 0.5000 36.8623 36.5939 ％ 0.6000 37.6650 37.3759 ％ 0.7000 38.2581 38.0491 ％ 0.8000 38.9390 38.7058 ％ 0.9000 39.5218 39.3437 (MATLAB implementation of SPIHT (without A rithmatic coding stage). Performance evalua% tion : this toolbox VS. SPIHT%% Test image : lena512.raw%% bpp SPIHT (dB) This code (dB)% 0. 1000 29.8107 32.7202 0.2000 29.3202% 32.2514% 0.3000 0.4000 34.5479 34.0331% 35.8422 35.485 7% 0.5000 0.6000 36.8623 36.5939% 37.6650 37.3 759% 38.0491% 0.7000 0.8000 38.2581 38.9390 38 .7058 0.9000 39.5218 39.3437%)

卡农音乐播放代码，能正常运行，用matlab编写(Canon music player code, it works, using matlab)

关于matlab编写的语音端点检测的程序(Matlab prepared speech endpoint detection procedures)

脑电中的递归式的处理方法，相比参数估计的方法更加智能，ＬＭＳ是最小均方误差法，是十分经典的一种方法，里面是包括一些最新的改进。(EEG recursive style approach, parameter estimation method compared to more intelligent, LMS is the minimum mean square error method is a very classical approach, which is to include some of the latest improvements.)

MATLAB实现基于互相关的亚像素级图像匹配配准源代码(MATLAB-based implementation of inter-related sub-pixel image matching with prospective source code)

FundamentalsofWirelessCommunication中文版，MIMO经典资料(FundamentalsofWirelessCommunication Chinese, MIMO classical information)

matlab的中文说明书，很好的，值得学习，，，(matlab the Chinese manual)

说明：  提出一种受灰狼（Canis lupus）启发的新的元启发式方法，称为灰狼优化器（GWO）。 GWO算法模仿自然界中灰狼的领导等级和狩猎机制。(This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature.)

频谱分析中的倒频谱分析,在故障诊断和声音信号处理方面很常用(The inverted spectrum analysis spectrum analysis in fault diagnosis and sound signal processing is commonly used)

利用神经网络对输入的Ｍ序列进行线性离散系统辨识，及其改进算法(The use of neural network input M-sequence of linear discrete-time system identification, and its improved algorithm)