models
共 232 篇文章
models 相关的电子技术资料，包括技术文档、应用笔记、电路设计、代码示例等，共 232 篇文章，持续更新中。
Smart Card Applications Design Models for Using and Programming, SIM card, Payment System,EMV2000,..
Smart Card Applications Design Models for Using and Programming, SIM card, Payment System,EMV2000,...
SUI channel models MATLAB.
SUI channel models MATLAB.
These are matlab and simulink files to model the membrane crystallization system, including the matl
These are matlab and simulink files to model the membrane crystallization system, including the matlab file to get the optimation point of this system, and 3 simulink files, which are static model and
a document about "Baseball Playfield Segmentation Using Adaptive Gaussian Mixture Models"
a document about "Baseball Playfield Segmentation Using Adaptive Gaussian Mixture Models"
EM algorithm for Gausian mixture models
EM algorithm for Gausian mixture models
This code and doucument describe the image segementation using active contore models [snake].
This code and doucument describe the image segementation using active contore models [snake].
MATHEMATICAL MODELS OF DC-DC CONVERTERS
MATHEMATICAL MODELS OF DC-DC CONVERTERS
Dynasty Maxstar 350 700
Dynasty 350, 700<br />
208/575 Volt Models W/Auto-Line<br />
<br />
Maxstar 350, 700<br />
File: TIG (GTAW)<br />
Including Optional Cart And Cooler<br />
CE And Non-CE Models<br />
介绍计算机上实现gsm modem短消息收发的模式
介绍计算机上实现gsm modem短消息收发的模式，描述gsm modem PDU 模式，包括PDU 模式下的gsm modem模块UCS2 编码、解码原理，以及gsm modem发送与接收PDU 串的编制方式， VB 中的MSCOMM 控件，实现gsm modem短消息收发的核心内容。-briefed on computer modem gsm SMS transceiver model, de
.安装好Proteus 6.9 SP4,需要用Keil for ARM的或IAR的同样需要安装好。 2.安装Proteus VSM Simulator驱动 可以在官方网站上下载到)。Keil装Vdm
.安装好Proteus 6.9 SP4,需要用Keil for ARM的或IAR的同样需要安装好。
2.安装Proteus VSM Simulator驱动 可以在官方网站上下载到)。Keil装Vdmagdi.exe,IAR装Vdmcspy.exe
3.将附件中的Prospice.dll和Licence.dll文件Copy到..\bin目录下，MCS8051.dll和ARM7TDMI.dll文件
Creating Flight Simulator Landing Gear Models Using Multidomain Modeling Tools
Creating Flight Simulator Landing Gear Models Using Multidomain Modeling Tools
Probabilistic graphical models in matlab.
Probabilistic graphical models in matlab.
802.11n WLAN PHY Simulation models of 802.11n (modified from 11a PHY model): wlan/IEEE80211n.m
802.11n WLAN PHY
Simulation models of 802.11n (modified from 11a PHY model):
wlan/IEEE80211n.mdl
Multipath and Doppler effects and Models与下一个PDF文档匹配
Multipath and Doppler effects and Models与下一个PDF文档匹配
This function calculates Akaike s final prediction error ％ estimate of the average generalization e
This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
k-step ahead predictions determined by simulation of the ％ one-step ahead neural network predictor.
k-step ahead predictions determined by simulation of the
％ one-step ahead neural network predictor. For NNARMAX
％ models the residuals are set to zero when calculating the
％ predictions. The predic
state of art language modeling methods: An Empirical Study of Smoothing Techniques for Language Mod
state of art language modeling methods:
An Empirical Study of Smoothing Techniques for Language Modeling.pdf
BLEU, a Method for Automatic Evaluation of Machine Translation.pdf
Class-based n-gram mo
We address the problem of predicting a word from previous words in a sample of text. In particular,
We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
Extension packages to Bayes Blocks library, reported in "Nonlinear independent factor analysis by hi
Extension packages to Bayes Blocks library, reported in "Nonlinear independent factor analysis by hierarchical models" (Valpola, Ö stman and Karhunen, 2003).
The library is a C++/Python implementation of the variational building block framework introduced in
The library is a C++/Python implementation of the variational building block framework introduced in our papers. The framework allows easy learning of a wide variety of models using variational Bayesi