Due to an increase in demand for and questions about direct disk
access for Micrososft platforms, and due to the fact that Microsoft
has no API for direct disk access, I am releasing this library
much earlier than I intended at that start. I am still working on
this code.
W2kPrintDrvSample
Feature
=======
* Support two page directions, portrait and landscape
* Support just one page size, A4
* Support two resolutions, 200 x 200 and 100 x 100 dpi
* Support two color mode, color(24bpp) and monochrom
* Support halftoning in monochromatic mode
* Support color identifying(7 colors)
* Support type identifying(3 types)
...
Usage
=====
* 在monochromatic mode下，可以通過設置黑色的輸出類型來控制輸出
* 在color mode下，可以通過設置各個顏色的輸出類型來控制輸出，7種
顏色以外的顏色都作為黑色來識別
* 在輸出多頁文件時，可以分別指定每頁的輸出文件名，也可以使用自動
添加頁號的功能
...
Known Problems
==============
Developer Notes
===============
* 在windows ddk 命令行環境下編譯
參考步驟：
1. 將W2kPrintDrv解壓至DDK安裝目錄
2. 執行“開始”菜單->Developement Kits->Windows 2000 DDK->Checked Build Enviroment
3. 在命令行環境輸入
> cd W2kPrintDrv
> build
Author
======
terrificskyfox <terrificskyfox@yahoo.com.cn>
Beginning with an introduction to 802.11b in general, 802.11 Security gives you a broad basis in theory and practice of wireless security, dispelling some of the myths along the way. In doing so, they provide you with the technical grounding required to think about how the rest of the book applies to your specific needs and situations. If you are a network, security, or systems engineer, or anyone interested in deploying 802.11b--based systems, you ll want this book beside you every step of the way.
fastDNAml is an attempt to solve the same problem as DNAML, but to do so
faster and using less memory, so that larger trees and/or more bootstrap
replicates become tractable. Much of fastDNAml is merely a recoding of the
PHYLIP 3.3 DNAML program from PASCAL to C.
annie is an ANN, ie, Artificial Neural Network library developed in C++. It can be used to implement various kinds of neural networks like Multi-Layer Perceptron, Radial basis function networks, Hopfield networks etc.
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.
The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
