dtmb_LDPC

信噪比MATLAB程序，可以分析信号的干净程度， 适合信号处理的初学者。(SNR MATLAB program that can analyze the cleanness of the signal, signal processing for beginners.)

AFNI在Matlab下应用源码。用于在matlab环境下导入，修改，处理afni数据。(AFNI source code in Matlab applied. Matlab environment for the import, modify, afni treatment data.)

ome numerical calculation using matlab code, suitable for novice matlab and digital computing use, according to the ideological preparation of matlab matrix, for beginners to grasp a better matlab programming ideas useful...

it will shrink the i/p image

利用matlab GUI实现的串口编程例子，实现了图像的灰度化并进一步用于视频监视控，最大信噪比的独立分量分析算法。( Use serial programming examples matlab GUI implementation, Achieve a grayscale image and further control for video surveillance, SNR largest independent component analysis algorithm.)

自己编写的改进ELMAN网络辨识程序.非常实用(I have written procedures to improve the ELMAN network identification. Very useful)

dopasowanie rozkł adu log-normalnego

说明：  grey predictor algorithm

差分混沌键控DCSK的实现，及其延迟相干解调。并计算了其误码率曲线，并与BPSK误码率曲线对比(Differential chaos shift keying DCSK the realization of coherent demodulation and delay. And calculated its error rate curves, and compared with the BPSK bit error rate curve)

一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。 (A new approach to compare models for body area networks (BAN) that accommodates multiple links and multiple subjects is presented. The absolute measure described allows comparison across a spectrum of possible characterizations ranging from single-parameter for an entire ensemble, through to per-activity, per-subject and per-link based parameterized models. The use of an explicit trade-off between error and complexity, combined in a goodness-of-fit measure, is shown to have important consequences when applied to a range of typical BAN channel data. It is shown that there are different implications in choice of model, and it’s associated complexity, for mixed-activity “everyday” data, when compared with set-activity dynamic data (e.g. walking). The deficiency of mean path loss, or even median path loss measures, as a sole characterization of the BAN channel is also highlighted.)