Binary-Image

slam进阶学习，老外做的，很完善的东西，对于同时定位与地图创建的friend有很大的帮助(slam Advanced study, foreigners do things very well, for localization and mapping at the same time created a great friend to help)

Application module support for IPVS.

利用matlab分析地震波场速度特征,是进行速度建模的基础(Matlab analysis of the use of the speed of seismic wavefield characteristics, speed of modeling is the basis of)

功率谱估计是利用有限长的数据估计信号的功率谱，广泛应用于各个领域。功率谱估计主要分为经典谱估计与现代谱估计。常用的经典谱估计方法有周期图法，相关法，周期图的改进法，常用的现代谱估计方法有最大熵谱估计，AR模型，MA模型，ARMA模型。经典谱估计适用于长序列的信号，其主要缺陷是描述功率谱波动的数字特征方差性能差，频率分辨率低，现代谱估计适用于短序列的信号，旨在改善谱估计的分辨率，并将其应用于实际地震资料的谱分析。 (Power spectrum estimation is the use of a finite length data to estimate the power spectrum of the signal, widely used in various fields. Power spectrum estimation is divided into classic and modern spectral estimation spectral estimation. Commonly used methods of classical periodogram spectrum estimation method, the relevant law, the cycle graph Improvement Act, commonly used in modern spectral estimation methods have maximum entropy spectral estimation, AR model, MA model, ARMA model. Classical spectral estimation applied to signal a long sequence, its main drawback is the fluctuation power spectrum describing digital signature variance performance is poor, the frequency resolution is low, modern spectral estimation applied to signal short sequences designed to improve spectral estimation resolution, and The analysis applied to real seismic data spectrum.)

in this project we use the neural network to segment textured image using a multilayer perceptron.

说明：  数字滤波设计，包括有限冲击响应和无限冲击响应滤波器设计(digital filter design, including finite impulse response and infinite impulse response filter design)

DVB_S2_LDPC整体累积编码算法文档及MATLAB仿真(DVB_S2_LDPC overall cumulative encoding algorithm documents and MATLAB simulation)

说明：  通过调节入射光的波矢和偏振的配置，求得需要的晶格参数(By adjusting the incident light s wave vector and the polarization configuration of the lattice parameters obtained need)

matlabA关于金融建模中数据的基本统计分析书籍和实例(matlabA basic statistical data on financial modeling analysis books and instances)

Particle Swarm Optimization