filtermdlforiirfir

基于小波分析理论实现的红外图像与可见光图像融合(Based on wavelet analysis theory of the realization of the infrared image and visible image fusion)

啁啾光纤光栅matlab仿真源程序,使用matlab对啁啾光纤光栅的反射光谱进行仿真，使用了传输矩阵方法。很有用，可以直接运行(Matlab simulation source chirp fiber grating, using matlab to chirp fiber grating reflection spectrum are simulated, using the transfer matrix method.)

信号的延迟,信号相加,信号相乘,信号翻转,信号和,信号积,信号能量等(Signal delays, the sum signal, signal multiplication, signal inversion, the signal and the signal plot, the signal energy, etc.)

用SVD-TLS法进行的AR参数估计并用Cadzow谱估计子估计出信号的功率谱密度。(With the SVD-TLS method of AR parameter estimation and spectral estimation Cadzow child with estimated signal power spectral density.)

DFT进行频谱分析时的三种现象 1、混叠：对连续信号采样，要求连续信号是带限的，采样频率要足够高。Fs应满足Nyquist采样定理才不产生混叠。 采样前加低通滤波器防混叠 2、频谱泄漏：DFT对时域信号进行了截断（相当于采样信号乘了一个窗函数），时域的乘积相当于频域卷积，改变了原信号频谱。 改用其他窗函数（非矩形）改善泄漏。 3、栅栏现象：DFT 只给出了频谱在采样点上的取值，采样点间的频谱内容丢失。(DFT spectrum analysis of a three phenomena, aliasing : continuous signal sampling, requiring continuous signal is band-limited, the sampling frequency should be high enough. Fs meet the Nyquist Sampling Theorem would not have aliasing. Sampling former low-pass filter plus anti-aliasing 2, spectrum leakage : DFT right time domain signal a disconnect (equivalent to a sampling signal by a window function). time domain is equivalent to the product of frequency-domain convolution, changes in the spectrum of the original signal. Window functions to switch to other (non-rectangular) to improve leakage. 3, fenced phenomenon : DFT spectrum is given only at the sampling point value, sampling points of the spectrum as lost.)

舰船辐射噪声的产生，可利用其模拟舰船辐射噪声，从而达到定位等目的(generation of Ship radiated noise)

基于matlab的立方体绘图案例，希望对各位有所帮助，如有不对，还请批评指正(Matlab-based cube drawing case, they hope to be helpful, if wrong, criticism and)

Spectral Efficiency in Large-Scale MIMO-OFDM Systems with Per-Antenna Power Cost

Kalman Filtering - Theory and Practice Using MATLAB - Mohinder Grewal (Wiley, 2001).pdf

高斯过程是一种非参数化的学习方法，它可以很自然的用于regression，也可以用于classification。本程序用高斯过程实现分类！(Gaussian process is a non- parametric method of learning, it is very natural for regression. can also be used for classification. The procedures used to achieve classification Gaussian process!)