matlab-tuxiangchuli

linear tank model on matlab

潮流样本。能运用于电力系统潮流计算，MATLAB编写(Tide samples. Can be applied to power system load flow calculation, MATLAB prepared)

说明：  mat lab designed filter

盲信号语音分离的程序，基于独立分量的分离程序，若输入两个源信号，则可以输出一组混合信号和分离后的信号(Blind signal speech separation procedure, based on independent component separation procedure, enter the two source signals, the output signal after a group of mixed-signal and separation)

等间距线性阵列，阵元数M=8，阵元间距d= λ /2，等幅加权，目标方位φr =0°。产生空间白噪声矢量（复高斯分布）。 设阵列信号矢量元素s(m)=exp[j mψr]，其信号幅度为1，该阵元接收机附加的高斯噪声为n(m)=nmr+jnmi，其中实虚部均为独立同分布N(0, σ2)的高斯随机数，则该通道合成信号x(m)=s(m) + n(m)，其中信噪比为： SNR = 10 lg[1/(2σ2)] = – 3 – 10 lg(σ2) (dB) (The spacing of the linear array, and the array element number M = 8, the array element spacing d = λ/2, equal amplitude weighted target azimuth φR = 0. Space generated white noise vector (complex Gaussian distribution). Let array signal vector elements s (m) = exp [j mψr], the signal amplitude, the element receiver additional Gaussian noise n (m) = nmr+jnmi which the real and imaginary parts are independent and identically distributed N ( Gaussian random numbers of 0, σ2), the channel the synthesis signal x (m) = s (m) the PPLS n (m), where SNR is: SNR = 10 lg [1/(2σ2)] =- 3- 10 lg (σ2) (dB))

Compensator by the method of frequency response using Bode

为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。(In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms, and two and a support vector machine decision tree classification algorithm of moving objects.)

Jazoon NFC (near Field Communication) ebook

遗传算法求旅行商，送货商，邮递员，灾情巡视等问题，是一种近似算法。(Genetic algorithm to solve the traveling salesman, delivery companies, mail carriers, disaster inspections and other issues, is an approximation algorithm.)

高分辨率雷达 距离向压缩算法滤波器设计 三种方法 全面设计(clc clear all close all 2013年9月28日 ---------------参数---------- Tr=42e-6 fs=9.5e6 K=0.7e11 Nfft=2048 -------------匹配滤波 时域生成-------------- t=(-Tr/2+1/fs:1/fs:Tr/2-1/fs) w = kaiser(length(t),2.5) w=w w = ones(1,length(t)) ht1=w.*exp(-1i*pi*K*t.^2) ht2=w.*exp(1i*pi*K*t.^2) hf1=fft(ht1,Nfft) hf2=fft(ht2,Nfft) figure subplot(211) plot(abs(hf1)) title( 匹配滤波器1 幅频响应 ) subplot(212) plot(phase(hf1)) title( 匹配滤波器1 相位 ) figure subplot(211) plot(abs(hf2)) title( 匹配滤波器2 幅频响应 ) )