chazhi

OFDM系统中基于注水算法的功率分配,有助于深入了解注水算法的原理(power allocation based on waterfilling algorithm for OFDM systems，It helps to understand the principles of water-filling algorithm)

通过总结当前对处理器架构的安全性能的处理方法，提出一种增强DSP处理器安全性能的方法。主要从并行性方面进行了改进。最后对改进的方法进行了仿真和结果分析。(By summing up the current security architecture of the processor performance approach, a DSP processor to enhance the safety performance of the method. Mainly from the aspects of parallelism to improve. Finally, improved methods and results of simulation analysis.)

RBF神经网络一步预测 - Prediction_RBFMain_RBF.m(RBF神经网络一步预测- Prediction_RBFMain_RBF.m)

根据功率谱的原始定义编制的功率谱分析程序，相比于周期图、welch法等更简洁，但分辨率不高。(spectrum analysis based on self-correlation)

基于PEG算法的准循环LDPC码构造方法研究(PEG algorithm based on quasi-cyclic LDPC code construction methods)

很多mtalab关于数据分析的源代码，欢迎各位童鞋下载(Many mtalab data analysis on the source code, to welcome you to download children s shoes)

线性时不变离散时间系统的频域分析传输函数和频率响应，(Linear time-invariant discrete-time system transfer function and frequency domain analysis of frequency response,)

用matlab实现对语音信号的短时分析，包括清音和浊音的判决，基音周期的估计，实验报告。(use matlab language analysis the speech signal,contains the analysis for unvoiced sound and voiecd sound.)

小波包能量程序，支持向量机组分类时，特征的提取，小波包能量(Wavelet packet energy program, support vector machine classification, feature extraction, wavelet packet energy)

“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。(In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.)