Fast Fourier Transform power point
The rectangular window introduces broadening of any frequency components [`smearing鈥? and sidelobesthat may overlap with other frequency components [`leakage鈥?.
鈥he effect improves as Nincreases
鈥owever, the rectangle window has poor properties and better choices of wncan lead to better spectral properties [less leakage, in particular] 鈥搃.e. instead of just truncating the summation, we can pre-multiply by a suitable window function wnthat has better frequency domain properties.
鈥ore on window design in the filter design section of the course
This paper presents a novel technique to increase
the quality of medical images based on Histogram
Equalization. In the proposed method first we have
applied a noise reduction method and then we apply
some suitable preprocessing on histogram of the
medical images and after that histogram equalization
has been applied on the new histogram. Our proposed
method in despite of its simplicity has better results in
compare to other usual methods based on histogram
equalization. The quality of resulted images after
applying our proposed methods has been tested on a
database (medical images) with a confirmed criterion
by viewer. Also we have considered a mathematical
criterion for comparing our proposed algorithm with
other available methods for contrast enhancement.
Results show the better efficiency of the proposed
method.
CRFsuite is a very fast implmentation of the Conditional Random Fields (CRF) algorithm. It handles tens of thousands sentences in merely one second.
In comparison to CRF++, CRFSuite yields substantially better efficiency performance
The most straightforward approximation is the standard Gaussian approximation, where the MAI is approximated by a Gaussian random variable. This approximation is simple, however it is not accurate in general. In situations where the number of users is not large, the Gaussian approximation is not appropriate. In-depth analysis of must be applied. The Holtzman?s improved Gaussian approximation provides a better approximation to the MAI term. The approximation conditions the interference term on the operation condition of each user.
Novell.Press.Linux.Kernel.Development
linux內核開發的經典書籍之一
The Linux kernel is one of the most interesting yet least understood open-source projects. It is also a basis for developing new kernel code. That is why Sams is excited to bring you the latest Linux kernel development information from a Novell insider in the second edition of Linux Kernel Development. This authoritative, practical guide will help you better understand the Linux kernel through updated coverage of all the major subsystems, new features associated with Linux 2.6 kernel and insider information on not-yet-released developments. You ll be able to take an in-depth look at Linux kernel from both a theoretical and an applied perspective as you cover a wide range of topics, including algorithms, system call interface, paging strategies and kernel synchronization. Get the top information right from the source in Linux Kernel Development
An important book that cover wavelet domain. It help a lot in understanding wavelets and their applications.
Beside explanations of the transform and application of this, there are also presents the reasons why wavelets are better than Fourier transform.
G
UILLAIN-BARRéSYNDROME(GBS)is an uncommon disorder,but one
whose impact is far out of proportion to its incidence.Despite a
usually good prognosis,GBS is a particularly frightening and often life-
altering experience for those diagnosed with the disorder.Many patients
are acutely aware of the rapid loss of control of their muscular function,
including vital functions such as breathing and swallowing,and fre-
quently feel that they are dying.The experience is almost as unnerving
for the families of affected individuals.During the acute phase of the ill-
ness GBS patients experience the indignity of helplessness in addition to
their fear of death or permanent disability.Prolonged disability is com-
mon and some permanent residual effects are becoming increasingly
recognized.It has been our experience in meeting patients at support
groups,that individuals who have been affected by GBS have a great
desire for a better understanding of the disorder,even years after the
acute experience.
ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
