This directory includes matlab interface of the curvelet transform
using usfft.
Basic functions
fdct_usfft.m -- forward curvelet transform
afdct_usfft.m -- adjoint curvelet transform
ifdct_usfft.m -- inverse curvelet transform
fdct_usfft_param.m -- returns the location of each curvelet in phase-space
Useful tools
fdct_usfft_dispcoef.m -- returns a matrix contains all curvelet coefficients
fdct_usfft_pos2idx.m -- for fixed scale and fixed direction, returns
the curvelet which is closest to a certain point on the image
Demos
fdct_usfft_demo_basic.m -- display the shape of a curvelet
fdct_usfft_demo_recon.m -- partial reconstruction using curvelet
fdct_usfft_demo_disp.m -- display all the curvelet coefficients of an image
fdct_usfft_demo_denoise.m -- image denoising using curvelet
this directory
contains the following:
* The acdc algorithm for finding the
approximate general (non-orthogonal)
joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices.
[acdc.m]
* The acdc algorithm for finding the
same for a set of Symmetric matrices.
[acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version
[acdc.m], rather than the Symmetric version[acdc_sym] is preferable.
* A function that finds an initial guess
for acdc by applying hard-whitening
followed by Cardoso s orthogonal joint
diagonalizer. Note that acdc may also
be called without an initial guess,
in which case the initial guess is set by default to the identity matrix.
The m-file includes the joint_diag
function (by Cardoso) for performing
the orthogonal part.
[init4acdc.m]
A Matlab toolbox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction.
KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, approximate system identification, MPUM.
MATLAB的SVM算法實現，Matlab Support Vector Machine Toolbox，This toolbox was designed as a teaching aid, which matlab is
particularly good for since source code is relatively legible and
simple to modify. However, it is still reasonably fast if used
with the supplied optimiser. However, if you really want to speed
things up you should consider compiling the matrix composition
routine for H into a mex function. Then again if you really want
to speed things up you probably shouldn t be using matlab
anyway... Get hold of a dedicated C program once you understand
the algorithm.
The program constructs girth-twelve column-weight QC-LPDC codes. The rate can be changed by changing k(row-weight), size is changed by varying m(sub-matrix size).
The algorith divides rows in to four equal groups. The rows are then used to from a distance graph which is then transformed into a matrix. girth of eight is maintained by avoiding six-cycles in the graph construction
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
