In the last decade the processing of polygonal meshes has
emerged as an active and very productive research area. This
can basically be attributed to two developments:
Modern geometry acquisition devices, like laser scanners
and MRT, easily produce raw polygonal meshes of
ever growing complexity
Downstream applications like analysis tools (medical
imaging), computer aided manufacturing, or numerical
simulations all require high quality polygonal meshes
as input.
The need to bridge the gap between raw triangle soup data
and high-quality polygon meshes has driven the research on
ecient data STRuctures and algorithms that directly operate
on polygonal meshes rather than on a (most often not
feasible) intermediate CAD representation.
A digital filter structure for wideband filtering
is proposed. It consists of two allpass filters and a linearphase
FIR filter. One major advantage of this structure is
that the allpass filters are functions of zM which implies
that the maximal sample frequency is M times higher for
this structure than for the corresponding conventional
STRuctures.
Because WDM networks are circuit switched loss networks blocking may occur because of lack of resources. Also in circuit switched networks many paths use the same links. This toolbox answers the question how different paths with different loads influence on each other and what is the blocking on each of the defined path. Toolbox is capable of computing blocking for three different WDM network types: with no wavelength conversion, with full wavelength conversion and with limited range wavelength conversion. It is worth noting that case for full conversion can be usefull for any circuit switched network without additional constraints (i.e. wavelength continuity constraint in WDM), for example telephone network.
Toolbox contains also scripts for defining network STRuctures (random networks, user defined networks) and traffic matrixes. Three graph algorithms for shortest path computation are also in this toolbox (they are used for traffic matrix creation).
Java數(shù)據(jù)結(jié)構(gòu)和算法中文第二版源碼,
The files in this folder, \ReaderFiles\, contain both source code
and executable code for the example programs presented in the text
of "Data STRuctures and Algorithms in Java," 2nd Edition. They are
available to any reader of the book.
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.
When joining Siemens in 2001, I also extended my research interest towards radio net-
work planning methodologies. This area of research brought together my personal interest
in mobile communications and in the design of efficient algorithms and data STRuctures.
Between 2001 and 2003, I participated in the EU project Momentum, which was target-
ing the performance evaluation and optimization of UMTS radio networks. I
