acoustic_communication

BPSK 与QPSK 调制解调系统的Matlab 仿真程序,发送滤波器与接收滤波器均为根升余弦滚降滤波器；信道噪声为加性高斯白噪声 绘制BPSK 与QPSK 调制下的误码率与信噪比曲线图，并与理论曲线进行对比(BPSK and QPSK modulation and demodulation system Matlab simulation program, send and receive filters are root raised cosine filter roll-off filter channel noise, BER and SNR draw BPSK and QPSK modulation under additive white Gaussian noise than the graph and compare with the theoretical curve)

说明：  提取远场图,提取参数后处理，可以完成多种数据处理的科学计算(extract farfield pattern)

Gibbs现象在MATLAB上的仿真程序。(Gibbs on the situation in the MATLAB simulation program.)

Implements "Computationally Convenient Distributional Assumptions for Common Value Auctions" by Michael B. Gordy, Federal Reserve Board FEDS 1997-5, January 1997.

Image Compression using Artificial Neural Networks This code is developed in MATLAB 6.5 and can used in Windows platform. Program contain all necessary files. For training the network use Train.m and Codec.m compress images by trained network.

PLSAMA MATERIAL PROGRAME

Takes random binary serial bit sequence and generates the QPSK modulated signal after converting ser

pmsg wind inverter and boost converter

本文在介绍MIMO无线通信系统模型的基础上，通过对比分析几种常用空时编码方法，选定空时分组码对信号进行编码并建立瑞利衰落信道模型，通过对两发一收及两发两收系统的仿真，研究接收天线数对系统接收信号可靠性的影响。之后进一步扩展到不同发射矩阵条件下研究多发一收系统的频谱利用率及误符号率，分析不同天线数目，不同发射矩阵条件下空时正交分组码检测算法的性能。(This paper introduced the MIMO wireless communication system model, through the comparative analysis of several commonly used method of space-time coding, grouping code for the selected space-time signal is encoded and establish Rayleigh fading channel model, through two rounds one and two rounds of closing Simulation of two closed systems, research receiving antennas to receive signals affect the reliability of the system. After further extended to study the conditions under different emission matrix of multiple spectral efficiency and symbol error rate of a closed system, analyze the different number of antennas, the performance under different emission matrix orthogonal space-time block codes condition detection algorithm.)

Power electronics plays an important role in controlling the grid-connected renewable energy sources. This paper presents a novel adaptive neuro-fuzzy control approach for the renewable interfacing inverter. The main objective is to achieve smooth bidirectional power flow and nonlinear unbalanced load compensation simultaneously, where the conventional proportional-integral controller may fail due to the rapid change in the dynamics of the highly nonlinear system. The combined capability of neuro-fuzzy controller in handling the uncertainties and learning from the processes is proved to be advantageous while controlling the inverter under fluctuating operating conditions. The inverter is actively controlled to compensate the harmonics, reactive power, and the current imbalance of a three-phase four-wire (3P4W) nonlinear load with generated renewable power injection into the grid simultaneously.