BRL-CAD is a powerful constructive solid geometry solid modeling system that includes an interactive geometry editor, ray-tracing support for rendering and geometric analysis, path-tracing for realistic image synthesis, network distributed framebuffer support, and image and signal-processing tools.
Kannel is a WAP gateway. It attempts to provide this essential part of the WAP infrastructure freely to everyone so the market potential for WAP services, both from wireless operators and specialized service providers, will be realized as efficiently as possible. It also works as an SMS gateway for GSM networks. Almost all GSM phones can use it to send and receive SMS messages, so this is a way to serve many more clients than just those using a WAP phone. Kannel was among the first WAP gateways to be certified as WAP 1.1 compliant.
Zsh is a UNIX command interpreter (shell) which of the standard shells most resembles the Korn shell (ksh). It includes enhancements of many types, notably in the command-line editor, options for customising its behaviour, filename globbing, features to make C-shell (csh) users feel more at home and extra features drawn from tcsh.
SPF is a new strategy for preventing junk mail. The present SMTP standard for email allows anyone to forge anyone else's email address. SPF verifies that the Sender address of an email message matches (according to some policy) the client IP address that submitted it. libspf2 is a complete and robust implementation of SPF which provides support for many MTAs. Support for new MTAs is in progress.
coNCePTuaL is a domain-specific programming language for rapidly generating programs that measure the performance and/or test the correctness of networks and network protocol layers. A few lines of coNCePTuaL code can produce programs that would take significantly more effort to write in a conventional programming language.
PCP (Pattern Classification Program) is a machine learning program for supervised classification of patterns. It runs in interactive and batch modes, and implements the following machine learning algorithms and methods: k-means clustering, Fisher's linear discriminant, dimension reduction using Singular Value Decomposition, Principal Component Analysis, feature subset selection, Bayes error estimation, parametric classifiers (linear and quadratic), pseudo-inverse linear discriminant, k-Nearest Neighbor method, neural networks, Support Vector Machine algorithm (SVM), model selection for SVM, cross-validation, and bagging (committee) classification.