wcdma的多徑環境下的上下行仿真，EITS標準，written by The Mobile and Portable Radio RESEARCH Group The Bradley rtment of Electrical and Computer Engineering Virginia Polytechnic Institute and State University
Blacksburg, Virginia
The Netlab toolbox is designed to provide the central tools necessary for the simulation of theoretically well founded
neural network algorithms and related models for use in teaching, RESEARCH and applications development. It contains
many techniques which are not yet available in standard neural network simulation packages
The Spectral Toolkit is a C++ spectral transform library written by Rodney James and Chuck Panaccione while at the National Center for Atmospheric RESEARCH between 2002 and 2005. The library contains a functional subset of FFTPACK and SPHEREPACK, including real and complex FFTs in 1-3 dimensions, and a spherical harmonic transform. Multithreading is supported through POSIX threads for the multidimensional transforms. This reference guide provides details of the public interface as well as the internal implementation of the library.
ReBEL is a Matlabtoolkit of functions and scripts, designed to
facilitate sequential Bayesian inference (estimation) in general state
space models. This software consolidates RESEARCH on new methods for
recursive Bayesian estimation and Kalman filtering by Rudolph van der
Merwe and Eric A. Wan. The code is developed and maintained by Rudolph
van der Merwe at the OGI School of Science & Engineering at OHSU
(Oregon Health & Science University).
2005
Center for Biological & Computational Learning at MIT and MIT
All rights reserved.
Permission to copy and modify this data, software, and its documentation only
for internal RESEARCH use in your organization is hereby granted, provided
that this notice is retained thereon and on all copies. This data and software
should not be distributed to anyone outside of your organization without
explicit written authorization by the author(s) and MIT.
penMesh is a generic and efficient data structure for representing and manipulating polygonal meshes. OpenMesh is developed at the Computer Graphics Group, RWTH Aachen , as part of the OpenSGPlus project, is funded by the German Ministry for RESEARCH and Education ( BMBF), and will serve as geometry kernel upon which the so-called high level primitives (e.g. subdivision surfaces or progressive meshes) of OpenSGPlus are built.
It was designed with the following goals in mind :
Flexibility : provide a basis for many different algorithms without the need for adaptation.
Efficiency : maximize time efficiency while keeping memory usage as low as possible.
Ease of use : wrap complex internal structure in an easy-to-use interface.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical RESEARCH on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
In 1960, R.E. Kalman published his famous paper describing a recursive solution
to the discrete-data linear filtering problem. Since that time, due in large part to advances
in digital computing, the Kalman filter has been the subject of extensive RESEARCH
and application, particularly in the area of autonomous or assisted
navigation.
