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电子书籍 The goal of this thesis is the development of traffic engineering rules for cellular packet radio n
The goal of this thesis is the development of traffic engineering rules for cellular packet
radio networks based on GPRS and EDGE. They are based on traffic models for typical
mobile applications. Load generators, representing these traffic models, are developed
and integrated into a simulation envi ...
matlab例程 Incorporating Prior Knowledge in Cubic Spline Approximation - Application to the Identification of R
Incorporating Prior Knowledge in Cubic Spline Approximation - Application to the Identification of Reaction Kinetic Models
技术资料 期刊论文：Marching cube algorithm review and trilinear interpolation a
·Marching cube algorithm review and trilinear interpolation adaptation for image- based dosimetric models
通讯/手机编程 Rayleigh Fading Channels in Mobile Digital Communication Systems Part II: Mitigation Bibles for
Rayleigh Fading Channels in Mobile
Digital Communication Systems
Part II: Mitigation
Bibles for Fading Models
技术资料 期刊论文：Variable Bandwidth Image Denoising Using Image-based Noise M
·Variable Bandwidth Image Denoising Using Image-based Noise Models [cvpr07]
数学计算 HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999 Matlab toolbox for Hidden Markov Mod
HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999
Matlab toolbox for Hidden Markov Models
数学计算 VARHMMBOX, version 1.1, Iead Rezek, Oxford University, MAR 2002 Matlab toolbox for Hidden Markov Mo
VARHMMBOX, version 1.1, Iead Rezek, Oxford University, MAR 2002
Matlab toolbox for Hidden Markov Models
技术资料 复合系数PSPICE模型结构的介绍以及如何利用PSPICE模型对MPX2000系列和MPX5000系列传感器性能进行评估
PSPICE models for Uncompensated, MPX2000 series, andMPX5000 series pressure sensors are presente
技术资料 复合系数PSPICE模型结构的介绍以及如何利用PSPICE模型对MPX2000系列和MPX5000系列传感器性能进行评估
PSPICE models for Uncompensated, MPX2000 series, and MPX5000 series pressure sensors are presented h
驱动编程 EM算法（英文）A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaus
EM算法（英文）A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models