Analytical constant-modulus algorithm, to separate linear combinations of CM sourcesThe algorithm
is robust in the presence of noise, and is tested on measured data,
collected from an experimental set-up.
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
HTML Parser is a Java library used to parse HTML in either a linear or nested fashion. Primarily used for transformation or extraction, it features filters, visitors, custom tags and easy to use JavaBeans. It is a fast, robust and well tested package.
Imperfect C++ Practical Solutions for Real-Life Programming
C++, although a marvelous language, isn t perfect. Matthew Wilson has been working with it for over a decade, and during that time he has found inherent limitations that require skillful workarounds. In this book, he doesn t just tell you what s wrong with C++, but offers practical techniques and tools for writing code that s more robust, flexible, efficient, and maintainable. He shows you how to tame C++ s complexity, cut through its vast array of paradigms, take back control over your code--and get far better results.
15篇光流配準(zhǔn)經(jīng)典文獻(xiàn)，目錄如下：
1、A Local Approach for robust Optical Flow Estimation under Varying
2、A New Method for Computing Optical Flow
3、Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms
4、all about direct methods
5、An Introduction to OpenCV and Optical Flow
6、Bayesian Real-time Optical Flow
7、Color Optical Flow
8、Computation of Smooth Optical Flow in a Feedback Connected Analog Network
9、Computing optical flow with physical models of brightness Variation
10、Dense estimation and object-based segmentation of the optical flow with robust techniques
11、Example Goal Standard methods Our solution Optical flow under
12、Exploiting Discontinuities in Optical Flow
13、Optical flow for Validating Medical Image Registration
14、Tutorial Computing 2D and 3D Optical Flow.pdf
15、The computation of optical flow
Description: Microsoft?Windows?HTTP Services (WinHTTP) provides developers with a server-supported, high-level interface to the HTTP/1.1 Internet protocol. WinHTTP is designed to be used primarily in server-based scenarios by server applications that communicate with HTTP servers. WinHTTP
is also designed for use in system services and HTTP-based client applications. WinHTTP is more secure and robust than WinInet. However, single-user applications that need FTP or gopher functionality, cookie persistence, caching, automatic credential dialog handling, Internet Explorer compatibility, or downlevel platform support should still consider
using WinInet.
FreeBREW is a collection of open source software for BREW. BREW Foundation Framework provides a multitasking-aware kernel which supports a layered, per-task and per-state event dispatching mechanism to enable robust, state-pattern programming in BREW
密碼學(xué)界牛人Victor Shoup用C++編寫(xiě)數(shù)論類(lèi)庫(kù)。
NTL is a high-performance, portable C++ library providing data structures and algorithms for arbitrary length integers for vectors, matrices, and polynomials over the integers and over finite fields and for arbitrary precision floating point arithmetic.
NTL provides high quality implementations of state-of-the-art algorithms for:
* arbitrary length integer arithmetic and arbitrary precision floating point arithmetic
* polynomial arithmetic over the integers and finite fields including basic arithmetic, polynomial factorization, irreducibility testing, computation of minimal polynomials, traces, norms, and more
* lattice basis reduction, including very robust and fast implementations of Schnorr-Euchner, block Korkin-Zolotarev reduction, and the new Schnorr-Horner pruning heuristic for block Korkin-Zolotarev
* basic linear algebra over the integers, finite fields, and arbitrary precision floating point numbers.
The salience distance transform incorporates edge strength information into the distance transform. The output is more robust and informative as input for techniques such as snakes
