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.
DEMO_COND demonstrates the role of the condition
number of a matrix (with respect to inversion)
in the role of linear system solving.
Matthias Heinkenschloss
Department of Computational and Applied Mathematics
Rice University
Feb 22, 2001
Novell.Press.Linux.Kernel.Development
linux內(nèi)核開發(fā)的經(jīng)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
This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
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.
The object detector described below has been initially proposed by
P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a
Dalal-Triggs detector that uses a single filter on histogram of
oriented gradients (HOG) features to represent an object category.
This detector uses a sliding window approach, where a filter is
applied at all positions and scales of an image. The first
innovation is enriching the Dalal-Triggs model using a
star-structured part-based model defined by a “root” filter
(analogous to the Dalal-Triggs filter) plus a set of parts filters
and associated deformation models. The score of one of star models
at a particular position and scale within an image is the score of
the root filter at the given location plus the sum over parts of the
maximum, over placements of that part, of the part filter score on
its location minus a deformation cost easuring the deviation of the
part from its ideal location relative to the root. Both root and
part filter scores are defined by the dot product between a filter
(a set of weights) and a subwindow of a feature pyramid computed
from the input image. Another improvement is a representation of the
class of models by a mixture of star models. The score of a mixture
model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
Optical communication technology has been extensively developed over the
last 50 years, since the proposed idea by Kao and Hockham [1]. However, only
during the last 15 years have the concepts of communication foundation, that
is, the modulation and demodulation techniques, been applied. This is pos-
sible due to processing signals using real and imaginary components in the
baseband in the digital domain. The baseband signals can be recovered from
the optical passband region using polarization and phase diversity tech-
niques, as well as technology that was developed in the mid-1980s.
Multi-carrier modulation? Orthogonal Frequency Division Multi-
plexing (OFDM) particularly? has been successfully applied to
a wide variety of digital communications applications over the past
several years. Although OFDM has been chosen as the physical layer
standard for a diversity of important systems? the theory? algorithms?
and implementation techniques remain subjects of current interest.
This is clear from the high volume of papers appearing in technical
journals and conferences.
