gmsk_final_s_ruido

车辆类型识别，简单的车辆模型识别技术，通过交通灯来检测一条道路上车辆的多少(trafficpatterns- a simple model of vehicular traffic on four roads all leading to an intersection controlled by a traffic light. It turns green for each of the four paths with specified durations. Tweak the traffic light durations to study how much traffic builds up on the different paths.)

说明：  一个关于插值的例程，步骤详细，有详细的注释，对于通信专业的学习有用(An interpolation routine on the steps in detail, with detailed notes, learn useful for the communication professional)

通过matlab实现数据的时域图D代码呵呵呵呵(When implementing data via matlab domain map)

《自适应滤波器》第五章代码，很详细，值得推荐(Chapter adaptive filter code, very detailed)

利用matlab计算lyapunov最大指数，对混沌时间序列进行短期预测。(Using matlab to calculate lyapunov largest index of short-term prediction of chaotic time series.)

基于MATLAB中的M文件实现QPSK及QDPSK的调制，希望能帮助你学习(M-file MATLAB-based implementation of QPSK modulation and QDPSK)

模糊聚类的经典算法，可以使用UCI数据集进行聚类，该程序附有详细的说明(Classical fuzzy clustering algorithm, you can use the UCI data sets clustering, the program is accompanied by a detailed description of)

A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.(A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.)

It is about matlab which depicts brain image segmentation

本程序仿真了AWGN信道下，采用DS扩频对bpsk信号的影响，扩频码采用walsh码，扩频因子16； 接收端进行rake接收，然后分集合并，比较了三种分集方式对误码率的影响 从图像可以看出在扩频因子较高的情况下，最大比值合并与等增益合并性能相近(This program simulates the AWGN channel, using DS spread spectrum signal for bpsk influence spreading code using walsh code spreading factor 16 Rake receiver receiving end, and diversity combining, diversity compares three methods on the bit error rate of As can be seen from the image in the case of higher spreading factor, the maximum ratio combining and equal gain combining similar performance)