BIHT-l1

噪声中正弦信号的现代法谱分析，采用了直接法、间接法、Bartlett法和Welch法，分别分析了不同窗长度、不同窗函数和不同信噪比对谱估计的影响。(Modern normal spectrum analysis of sinusoidal signal in noise adopts direct method, indirect method, Bartlett method and Welch method. The effects of different window lengths, different window functions and different signal-to-noise ratios on spectrum estimation are analyzed respectively.)

根据二维空间内目标作匀速直线运动和匀速圆周运动的特点，在建立目标运动模型和观测模型的基础上采用基于交互多模算法（IMM）的卡尔曼滤波器对机动目标进行跟踪。仿真结果表明，该算法不仅能够对匀速直线运动和匀速圆周运动的目标进行跟踪，而且在运动模型发生变化时，滤波误差也比较小。 关键词：卡尔曼滤波器；目标跟踪；机动；交互多模（IMM） (two-dimensional space under target for uniform linear motion and uniform circular motion characteristics, the establishment of the target model and observations on the basis of the model based on interactive multi-algorithm (IMM) Kalman Filter right track moving targets. Simulation results show that the method can not only on uniform linear movement and the uniform circular motion of the target tracking, and the campaign model change, the filter error is relatively small. Keywords : Kalman filter; Target tracking; Mobility; Interactive multi-mode (IMM))

一个关于BPSK误码率仿真的程序，内容只包括误码率仿真部分！(One on BPSK BER simulation procedures, including the signal generated by adding Gaussian white noise, and bit error rate!)

说明：  适合初学者ransac算法的基本原理，较为详细全面的介绍了ransac算法(Ransac algorithm suitable for beginners the basic principles of a more detailed and comprehensive introduction to the ransac algorithm)

说明：  布朗运动的模拟程序，能画出n个点的布朗运动的轨迹(Brownian motion simulation program that can draw n points in the trajectory of the Brownian motion)

AMR WB+ 基于3GPP标准的宽带Adaptive Multi-Rate - Wideband 实现(3rd Generation Partnership Project Technical Specification Group Services and System Aspects Speech codec speech processing functions Adaptive Multi-Rate- Wideband (AMR-WB) speech codec ANSI-C code )

说明：  spwm，光电并伏，PI控制光伏并网系统仿真模型(Simulation model of photovoltaic grid connected system controlled by SPWM and photovoltaic PI)

- mif generator on Altera using matlab

learnig based on computer for read

Matlab image inpainting using deep learning