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其他  * first open client.cpp and search for that USER_MSG_INTERCEPT(TeamInfo) over it u add this

* first open client.cpp and search for that USER_MSG_INTERCEPT(TeamInfo) over it u add this Code: USER_MSG_INTERCEPT(Health) { BEGIN_READ(pbuf,iSize) me.iHealth = READ_BYTE() return USER_MSG_CALL(Health) } * then we search for int HookUserMsg (char *szMsgName, pfnUserMsgHook pfn) a ...

https://www.eeworm.com/dl/534/249828.html

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J2ME KeePass for J2ME is a J2ME port of KeePass Password Safe, a free, open source, light-weight and easy

KeePass for J2ME is a J2ME port of KeePass Password Safe, a free, open source, light-weight and easy-to-use password manager. You can store passwords in a highly-encrypted database on a mobile phone, and view them on the go.

https://www.eeworm.com/dl/660/250990.html

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驱动编程  This the third edition of the Writing Device Drivers articles. The first article helped to simply ge

This the third edition of the Writing Device Drivers articles. The first article helped to simply get you acquainted with device drivers and a simple framework for developing a device driver for NT. The second tutorial attempted to show to use IOCTLs and display what the memory layout of Windows NT ...

https://www.eeworm.com/dl/618/252358.html

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软件设计/软件工程  Just what is a regular expression, anyway? Take the tutorial to get the long answer. The short answ

Just what is a regular expression, anyway? Take the tutorial to get the long answer. The short answer is that a regular expression is a compact way of describing complex patterns in texts. You can use them to search for patterns and, once found, to modify the patterns in complex ways. You can also u ...

https://www.eeworm.com/dl/684/277340.html

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人工智能/神经网络  n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional inde

n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ...

https://www.eeworm.com/dl/650/280629.html

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人工智能/神经网络  On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carl

On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and deta ...

https://www.eeworm.com/dl/650/280633.html

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数学计算  The software implements particle filtering and Rao Blackwellised particle filtering for conditionall

The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generi ...

https://www.eeworm.com/dl/641/284170.html

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matlab例程  In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional ind

In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of th ...

https://www.eeworm.com/dl/665/284182.html

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matlab例程  In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve r

In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: ...

https://www.eeworm.com/dl/665/284186.html

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数学计算  This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps t

This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, N ...

https://www.eeworm.com/dl/641/284866.html

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