Apartment_Management

在源信号和传输信道未知情况下，只利用接收天线的观测数据抽取源信号，称为盲信号分离.盲信号分离不仅是信号处理界、而且也是神经网络界的研究热点课题，在无线数据通信、雷达、图像、语音、医学以及地震信号处理等领域都具有广阔的应用前景.采用自然梯度法和分阶段学习法。(The source signal and transmission channel is unknown circumstances, using only observational data extraction receiving antenna source signal, known as blind signal separation. Blind signal separation is not only the signal processing community, but also a neural network hot topic in research circles, in the wireless data communications , radar images, audio, medical and seismic signal processing and other fields have broad application prospects. and the phases using the natural gradient learning method.)

打靶法的matlab实现，需要输入的变量较多，程序不是十分简便(Matlab implementation of the shooting method)

matlab下对图像进行fft变换，平滑，锐化，中心化傅立叶正变换得到大正方形，小正方形，斜正方形。对其频谱进行中心化处理。图像的平滑和锐化，利用拉普拉斯算子。(Matlab right image fft transform, smoothing, sharpening, is the center of the Fourier transform large square, small square, ramp square. Its spectrum treatment center. Image sharpening and smoothing, using Laplace operator.)

Used to annotate a method parameter to indicate that this method will close the resource.

用matlab实现kalman滤波器condensation滤波器跟踪自由落体的小球(Using matlab to achieve condensation filter kalman filter tracking of free-falling ball)

周期图法，bt进行功率谱估计，matlab仿真程序(Periodogram, bt power spectrum estimation, matlab simulation program)

fixed point iteration for finding roots

chen混沌系统的分数阶的吸引子仿真，很容易实现的，希望可以帮助你。一起学习混沌吧。(chen' s chaotic system of Fractional attractor simulation, it is easy to achieve, I hope you can help. With learning chaos.)

Matlab中开发的时频联合分析软件，使用的方法是短时傅立叶变换(Time frequency analysis use stft in matlab)

We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.