MATLAB Code for Optimal Quincunx Filter
Bank Design
Yi Chen
July 17, 2006
This file introduces the MATLAB code that implements the two algorithms (i.e., Algorithms
1 and 2 in [1], or Algorithms 4.1 and 4.2 in [2]) used for the construction of
quincunx filter banks with perfect reconstruction, linear phase, high coding Gain, certain
vanishing moments properties, and good frequency selectivity. The code can be
used to design quincunx filter banks with two, three, or four lifting steps. The SeDuMi
Matlab toolbox [3] is used to solve the second-order cone programming subproblems
in the two algorithms, and must be installed in order for this code to work.
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.
The TW2835 has four high quality NTSC/PAL video decoders, dual color
display controllers and dual video encoders. The TW2835 contains four
built-in analog anti-aliasing filters, four 10bit Analog-to-Digital converters,
and proprietary digital Gain/clamp controller, high quality Y/C separator to
reduce cross-noise and high performance free scaler. Four built-in motion,
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to Gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.
The program uses fminsearch to obtain the transfer function of a tank’s height. This tank is then controlled using a real PID controller. Controller’s tuning (determination of optimum controller’s parameters: Gain and time constants) is achieved using the genetic algorithm toolbox. Finally, result are plotted using both matlab commands as well as simulink.
This sample program generates two sine waves called X and Y.
It will then calculate the normalized magnitude and phase of
the two waveforms using the following formulas:
Mag = sqrt(X^2 + Y^2)/sqrt(GainX^2 + GainY^2)
Phase = (long) (atan2PU(X,Y) * 360)
The program will prompt the user to change the Gain and
frequency of the X and Y waveforms.