This code can be used to model a microstrip line or a microstrip
patch antenna (the Particular problem being modeled is determined
at compile-time via various declarations).
This a collection of sample processes that provide examples ranging from
how to use a Particular BPEL activity such as pick or scope, to more complex
examples of processes that invoke external Web services or show techniques
such as handling multiple start messages.
vs.net技術內幕
The release of the Microsoft Visual Studio .NET (and Visual C++ .NET in Particular) has underscored Microsoft’s increasing focus on Internet technologies, which are at the heart of the Microsoft .NET architecture. In addition to supporting the .NET initiative, Visual C++ .NET keeps all the productivity-boosting features you’re familiar with, such as Edit And Continue, IntelliSense, AutoComplete, and code tips. Visual C++ .NET also includes many new features such as managed code extensions for .NET programming, support for attributed code, and a more consistent development environment. These features take Visual C++ .NET to a new level. This book will get you up to speed on the latest technologies introduced into Visual C++.
Samples are artificially generated and in no way represent Particular individuals. Human names, company names, colleges, software product names are intentionally made up.
The production of this book required the efforts of many people, but two in Particular deserve to be singled out for their diligent, sustained, and unselfish efforts. Sally Stickney, the book s principal editor, navigated me through that minefield called the English language and contributed greatly to the book s readability. Marc Young, whose talents as a technical editor are nothing short of amazing, was relentless in tracking down bugs, testing sample code, and verifying facts. Sally, Marc: This book is immeasurably better because of you. Thanks.
The release of the Microsoft Visual Studio .NET (and Visual C++ .NET in Particular) has underscored Microsoft’s increasing focus on Internet technologies, which are at the heart of the Microsoft .NET architecture. In addition to supporting the .NET initiative, Visual C++ .
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
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
