DOA

一种基于SPR光谱分析的液体折射率的计算，结果正确，可以使用。(SPR spectroscopy based on the calculation of the refractive index of liquid, the result is correct, can be used.)

现代信号谱分析 RELAX MATLAB 算法例程(spectral analysis of signals)

introduccion entrenamiento en matlab

模态处理相关程序，包括时域，频率，自普，互普，相干，频响，时频分析，HHt，随机减量法等等，很不错，可以借鉴(Modal treatment-related procedures, including time domain, frequency, since the S & P, each P, coherence, frequency response, time-frequency analysis, HHt, random decrement method, etc., very good, can learn)

Discriminant Analysis Toolbox

jake算法(jake )

smith预估器添加到PID的控制器当中，解决了控制对象有延迟的影响(Smith predictor is added to the PID controller, solves the influence of time delay control object)

harq in lte for channel coding in matlab to correct error in 4g

文章讲述了现在韩国的频谱政策和动态频谱接入技术。过去韩国的频谱管理政策需要特权，并且只有一小部分频谱可以公用。随着无线技术的发展，现在，韩国政府，使用了机遇动态频谱接入的新型频谱策略，例如LBT技术用于REID，UWB和FACS等。MIC还考虑如何在DTV过渡完成后，重新分配频谱。新型的无线电技术如认知无线电和SDR将在UHF边带中应用新的服务。MIC试图用一用有效地方式开放频谱并接受新技术用于频谱策略中。(In this paper, we introduce spectrum policies and dynamic spectrum access technologies in Korea. In the past, Korea spectrum management was the command and control with exclusive usage rights and small portions of spectrum commons bands. With the development of the radio technologies, Recently, Korea government, MIC(ministry of information and com- munication), has been adopting new spectrum policies based on dynamic spectrum access technology such as LBT(Listen Before Talk) for RFID, UWB, and FACS(Flexible Access Common Spectrum) etc. MIC is also considering spectrum reallocation of digital dividend bands after DTV transition. New radio techniques such as cognitive radio and SDR will be applied to new services in this UHF band. MIC is trying to open the radio spectrum in an efficient way and adopt new technology into spectrum policy. Thus MIC is extending the open spectrum such as spectrum commons and spectrum liberalization bands. )

正交匹配追踪(OMP)在稀疏分解与压缩感知重构，稀疏分解先于压缩感知提出，信号稀疏表示的目的就是在给定的超完备字典中用尽可能少的原子来表示信号，可以获得信号更为简洁的表示方式，从而使我们更容易地获取信号中所蕴含的信息，更方便进一步对信号进行加工处理，如压缩、编码等。(Orthogonal Matching Pursuit (OMP) is used in sparse decomposition and compressive sensing reconstruction. Sparse decomposition is proposed prior to compressed sensing. The purpose of signal sparse representation is to represent the signal with as few atoms as possible in a given overcomplete dictionary. The more concise representation of the signal allows us to more easily obtain the information contained in the signal, making it easier to further process the signal, such as compression and coding.)