kftry

Formant_tracker based on LPC Analysis format:[formants]=formant_tracker(input,dimension,step,window_size,p,threshold)

read text,image,audio and calculate entropy and information

multilevel boost convertermultilevel boost convertermultilevel boost convertermultilevel boost convertermultilevel boost convertermultilevel boost converter

设计一种快跳频通信系统，仿真其中的序列和性能，并给出系统框图。(Design of a fast frequency-hopping communication systems, simulation and performance of the sequence, and gives the system diagram.)

连接各路口节点，统计道路条数，求出各道路的长度，并求出所有顶点对间的最短距离(Connect each node of the intersection, statistics, road number, find the length of the road, and calculated the shortest distance between all vertices)

扩展卡尔曼滤波器 卡尔曼滤波 卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全包含噪声的测量(英文:measurement)中，估计动态系统的状态。(Extended Kalman Filter（EKF）)

Estimation of Tool Pose Based on Force–Density Correlation During Robotic Drilling

信号与系统基础知识包括连续信号与模型、离散信号与模型；常用信号变换包括Z变换、Chirp Z变换、FFT变换、DCT变换和Hilbert变换等；离散系统结构包括IIR、FIR和Lattice结构；IIR滤波器设计包括模拟和数字低通、高通、带通与带阻滤波器设计，以及基于冲激响应不变法和双线性Z变换法的IlR滤波器设计等；FIR滤波器设计包括基于窗函数、频率抽样法和切比雪大逼近法的FIR滤波器设计；平稳信号分析包括经典功率谱估计、基于参数模型的功率谱估计和基于非参数模型的功率谱估计；非平稳信号分析包括STFT变换、Gabor展开、Wigner-Ville分布与Choi-Williams分布；非高斯信号分析包括基于非参数法的双谱估计、基于参数模型的双谱估计，以及双谱估计的应用；信号处理的GUI实现包括滤波器设计与分析的FDATool工具和滤波器设计与信号分析的SPTool工具。(signals and systems based on knowledge, including continuous signal with the model, discrete signal and model; Common signals include transform Z transform, chirp Z-transform, FFT transform. DCT and Hilbert transform; Discrete system architecture including IIR, FIR and Lattice structure; IIR filter design, including analog and digital low-pass, high-pass, band-pass and band stop filter design, Based on the impulse response of the same law and bilinear Z transform the IlR filter design; FIR filter design including on the basis of the window function, frequency sampling and snow all over the big approximation of FIR filter design; stationary signal analysis include classical power spectrum estimation, Based on the model parameters of the power spectrum estimation and non- parametric model b)

使用K近邻法设计分类器，对一组数据进行分类(K-nearest neighbor algorithm to design the classifier, to classify a set of data)

We see from the graphs , that as the number of samples increased, the estimation gets better. As now for increased number of samples , variance of estimators are reduced. So, we get better estimation.