jiyuMatlab

本程序设计的是咬尾编码的turbo码，用matlab编写。咬尾turbo码也即不需要拖尾比特，使得前后两个状态相同，译码采用修正的MAP译码算法。此文件为turbo码译码一般的MAP算法，程序中用作与咬尾turbo译码的对照(This program is designed to encode the turbo tail biting code, written with matlab. Tail biting turbo code that is not trailing bits to make the same before and after the two states, the MAP decoding using modified decoding algorithm. This document is a general MAP turbo decoding algorithm, and biting the end of the program for the control turbo decoding)

DCT embedding and extraction

使用1749年至1924年太阳黑子的经典序列来确定和估计具有观测误差的模型(specification and estimation of models with observation errors using W?lfer’s classic time series of total number of sunspots from 1749 to 1924)

OFDMA仿真代码,可以直接运行，包含很多M文件，可以分块使用，非常有用。(OFDMA simulation code can run, including a lot of M file, you can block use, very useful.)

QAM,PSK,DPSK,FSK,MSK调制仿真，包括眼图输出，误码率输出，星座图，频谱，及实际wav文件的调制解调(QAM, PSK, DPSK, FSK, MSK modulation simulation, including eye diagram output, the output BER, constellation, spectrum, and actual modem wav files)

利用近远场外推做圆柱体雷达散射截面，并用fdtd方法。(Make use of the near and far field extrapolation cylinder RCS and use fdtd method.)

用于求解线性规划、整数规划、非线性规划、混合规划等标准优化问题以及LMI(For solving standard optimization problems such as linear programming, integer programming, non-linear programming, mixed programming and LMI)

SMALLbox是一种新型的基础框架采用自适应处理信号稀疏结构的表示。其主要目的是成为一个SMALLbox考验地探索新大概很好的方法来获得内在稀疏数据驱动模型,它是可以做到的应付大规模、复杂的数据资料。( SMALLbox is a new foundational framework for processing signals using adaptive sparse structured representations. The main aim of SMALLbox is to become a test ground for exploration of new provably good methods to obtain inherently data-driven sparse models, which are able to cope with large-scale and complicated data.)

huffman tree in matlab

本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。(The purpose of this paper is threefold: firstly, the local Successive Mean Quantization Transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up Sparse Network of Winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the Receiver Operation Characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors.)