In recent years, the UNIX operating system has seen a huge boost in its popularity, especially with the
emergence of Linux. For programmers and users of UNIX, this comes as no surprise: UNIX was designed to
provide an environment that s powerful yet easy to use.
One of the main strengths of UNIX is that it comes with a large collection of standard programs. These
programs perform a wide variety of tasks from listing your files to reading email. Unlike other operating
systems, one of the key features of UNIX is that these programs can be combined to perform complicated
tasks and solve your problems.
One of the most powerful standard programs available in UNIX is the shell. The shell is a program that
provides you with a consistent and easy-to-use environment for executing programs in UNIX. If you have
ever used a UNIX system, you have interacted with the shell.
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 details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
//
// Histogram Sample
// This sample shows how to use the Sample Grabber filter for video image processing.
// Conceptual background:
// A histogram is just a frequency count of every pixel value in the image.
// There are various well-known mathematical operations that you can perform on an image
// using histograms, to enhance the image, etc.
// Histogram stretch (aka automatic gain control):
// Stretches the image histogram to fill the entire range of values. This is a "point operation,"
// meaning each pixel is scaled to a new value, without examining the neighboring pixels. The
// histogram stretch does not actually require you to calculate the full histogram. The scaling factor
// is calculated from the minimum and maximum values in the image.
A new PLL topology and a new simplified linear model are presented. The new fractional-N synthesizer presents no reference spurs and lowers the overall phase noise, thanks to the presence of a SampleJHold block. With a new simulation methodology it is possible to perform very accurate simulations, whose results match closely those obtained with the linear PLL model developed.
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, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Develop Portfolio Optimization Models." The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it
Interface for Microsoft Audio Compression Manager. - Delphi Source
The ACM uses existing driver interface hooks to override the default mapping algorithm for waveform audio devices. This allows the ACM to intercept device-open calls. After a call has been intercepted, the ACM can perform a variety of tasks to process the audio data, such as inserting an external compressor or decompressor into the sequence.
直接IRP操作文件的實現問題，其實對于大部分接口（諸如：
ZwReadFile、ZwWriteFile、ZwSetInformationFile、ZwDeleteFile等）在OSR上的“ Rolling Your Own - Building IRPs to perform I/O ”一文中已經實現，但是那里面的實現還不夠全。所以，我就翻出了前段時間在網上淘到的資料，特與大家共享！尤其值得注意的是，這里面還實現了ZwCreateFile接口！
大家都知道使用IRP的好處就是：效率提高了，對中斷級要求更寬了，還解決了常見的重入問題等等。 附件中一個是接口的實現代碼，另外一個是應用實例。
MATFOR FFT Visual Fortran program.
MATFOR Extensions, as implied in the name, are data or components external to MATFOR modules that can be used to add versatility to MATFOR programs. Currently there are two types of extensions available with MATFOR, they are MATLAB Interface and Tecplot FileIO. MATLAB Interface, a new feature in MATFOR 4.1, provides MATFOR users access to MATLAB functions so they can obtain functionality that has not been built into MATFOR. The ability of using MATLAB functions makes MATFOR an ideal tool for those who create prototypes using MATLAB, then do implementation in C++ and other programming languages. Through the use of MATFOR, users may perform quick results comparisons between MATLAB and other languages.
