This m file simulates a differential phase shift keyed (DPSK) ultra wide bandwidth(UWB) system using a fifth derivative waveform equation of a Gaussian pulse.
In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.
Generate Possion Dis.
step1:Generate a random number between [0,1]
step2:Let u=F(x)=1-[(1/e)x]
step3:Slove x=1/F(u)
step4:Repeat Step1~Step3 by using different u,you can get x1,x2,x3,...,xn
step5:If the first packet was generated at time [0], than the
second packet will be generated at time [0+x1],The third packet will be generated at time [0+x1+x2],
and so on ….
Random-number generation
1.static method random from class Math
-Returns doubles in the range 0.0 <= x < 1.0
2.class Random from package java.util
-Can produce pseudorandom boolean, byte, float, double, int, long and Gaussian values
-Is seeded with the current time of day to generate different sequences of numbers each time the program executes
The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory.
Consider a BPSK and a QPSK system for the following two cases: 1) The probability that the symbol 1 is sent and the probability that the symbol 0 is sent are all the same. 2) The probability that the symbol 1 is sent is two times than the probability that the symbol 0 is sent. Assume that the noise is Gaussian distributed with mean=0 and 2 = 1.
SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation, SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors.
