DIGITAL IMAGERY is pervasive in our world today. Consequently,
standards for the efficient representation and
interchange of digital images are essential. To date, some of
the most successful still image compression standards have resulted
from the ongoing work of the JOINT Photographic Experts
Group (JPEG). This group operates under the auspices of JOINT
Technical Committee 1, Subcommittee 29, Working Group 1
(JTC 1/SC 29/WG 1), a collaborative effort between the International
Organization for Standardization (ISO) and International
Telecommunication Union Standardization Sector (ITUT).
Both the JPEG [1–3] and JPEG-LS [4–6] standards were
born from the work of the JPEG committee. For the last few
years, the JPEG committee has been working towards the establishment
of a new standard known as JPEG 2000 (i.e., ISO/IEC
15444). The fruits of these labors are now coming to bear, as
JPEG-2000 Part 1 (i.e., ISO/IEC 15444-1 [7]) has recently been
approved as a new international standard.
This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the JOINT posterior distribution of the network parameters and the number of basis function. It performs a global search in the JOINT space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL
UWB 功率控制 容量
Main Matlab script is in runsim.m. It generates random topologies,
optimizes, and display results.
IMPORTANT: you may need to add manually the lib path in Matlab in order to
get all the necessary functions.
Reference: Radunovic, Le Boudec, "JOINT Power Control, Scheduling and Routing in UWB networks"
A new cable fault location method based on
wavelet reconstruction is proposed. In this method the
difference between the currents of faulty phase and sound
phase under the high voltage pulse excitation is used as the
measured signal and is decomposed in multi-scale by wavelet
transform, then reconstructed in single scale. Comparing with
traditional fault location method by travelling wave, the
presented method will not be interfered by the reflected wave
from the branch JOINT of cables or from other positions where
the impedances are not matched and not be influenced by fault
types, otherwise, the reflected waves can be recognized even
the faulty position is near to the measuring terminal, at the
same time, the influence of the wave speed uncertainty can be
reduced. The correctness of the proposed method is proved by
simulation results.
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]
The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating JOINT histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
