ShearLab-PPFT-1.0

神经网络训练拟合函数曲线，拟合复杂函数曲线(The neural network training function curve fitting)

three phase induction motor using simulink

说明：  惯性导航解算，得出飞行器的经度纬度高度，陀螺仪和加速度计的值(Inertial Navigation Solution, the longitude, latitude, altitude, gyroscope and accelerometer of the aircraft are obtained.)

基于matlab实现语音信号的采集和处理，包括频谱分析，显示实时A声级(Based on matlab realize the voice signal acquisition and processing, including spectral analysis, display real-time A sound level)

本程序实现多模态故障检测与故障诊断，用的方法是PCA_GMM，GMM是用的FJ算法，效果很好(This procedure realize multimodal, fault detection and fault diagnosis method is PCA_GMM, GMM FJ algorithm is used, the effect is very good)

说明：  通过峭度指标与互相关系数筛选IMF进行SVD分解去噪(Filtering IMF for SVD denoising by kurtosis index and correlation number)

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is a part of the biometric identification methodologies which also include facial, fingerprint, palm and many other biological traits. They all offer new solutions for person identification, authentication and security. Currently users have to carry security badges or know certain pin/pass codes in order to get into secure zones or to log into a computer. In this paper we propose an algorithm for iris recognition, which can be applied in real time with standard webcam and with good matching result.

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)

OFDM系统中用PTS算法降低系统的峰值平均功率比(OFDM system PTS algorithm used to reduce the peak-to-average power ratio)

格子Boltzmann方法，D2Q9模型，模拟多孔介质流动(Lattice Boltzmann method, D2Q9 model to simulate the flow in porous media)