Welcome to the Microsoft CRM 3.0 Software Development Kit (SDK). This SDK contains a wealth of resources, including code samples, that are designed to help you build powerful vertical applications using the Microsoft CRM platform. It includes the following sections:
1 Server Programming Guide
2 Client Programming Guide
3 ISV Programming Guide
4 Report Writers Guide
5 Appendix A
6 Glossary –
SharpZipLib之前叫做NZipLib，完全由 C# 開發(fā)的壓縮庫，支持Zip, GZip, Tar and BZip2 ，為2007年8月最新0852release版的源文件和文檔說明！
Changes for v0.85.2 release
Minor tweaks for CF, ZipEntryFactory and ZipFile.
Fix for zip testing and Zip64 local header patching.
FastZip revamped to handle file attributes on extract + other fixes
Null ref in path filter fixed.
Extra data handling fixes
Revamped build and conditional compilation handling
Many bug fixes for Zip64.
Minor improvements to C# samples.
ZIP-1341 Non ascii zip password handling fix.
ZIP-355 Fix for zip compression problem at low levels
SharpZipLib之前叫做NZipLib，完全由 C# 開發(fā)的壓縮庫，支持Zip, GZip, Tar and BZip2 ，為2007年8月最新0852release版的代碼實例！
Changes for v0.85.2 release
Minor tweaks for CF, ZipEntryFactory and ZipFile.
Fix for zip testing and Zip64 local header patching.
FastZip revamped to handle file attributes on extract + other fixes
Null ref in path filter fixed.
Extra data handling fixes
Revamped build and conditional compilation handling
Many bug fixes for Zip64.
Minor improvements to C# samples.
ZIP-1341 Non ascii zip password handling fix.
ZIP-355 Fix for zip compression problem at low levels
In addition to all the people who contributed to the first edition, we would like to thank the following individuals for their generous help in writing this edition. Very special thanks go to Jory Prather for verifying the code samples as well as fixing them for consistency. Thanks to Dave Thaler, Brian Zill, and Rich Draves for clarifying our IPv6 questions, Mohammad Alam and Rajesh Peddibhotla for help with reliable multicasting, and Jeff Venable for his contributions on the Network Location Awareness functionality. Thanks to Vadim Eydelman for his Winsock expertise. And finally we would like to thank the .NET Application Frameworks team (Lance Olson, Mauro Ottaviani, and Ron Alberda) for their help with our questions about .NET Sockets.
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
thinkinjava2English
Thinking in Java,
2nd Edition, Release 11
To be published by Prentice-Hall mid-June, 2000
Bruce Eckel, President,
MindView, Inc.
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How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
Make a graph from database record and either send it to a printer
directly selecting many print options or copy it in any file on disc.
This project also gives you print preview option for A4 size paper and
you can set your graph anywhere on the page.Also if you want to change
graph s height or width you can change.Zooming effect is also given
using hsrollbar.
高效的k-means算法實現，使用了k-d樹與局部搜索等提高k-means算法的執(zhí)行效率，同時包含示例代碼，用c++代碼實現。 Effecient implementation of k-means algorith, k-d tree and local search strategy are implementd to improve the effeciency, samples are included to show how to use it. All codes are implemented in C++.
