Care2x (formerly Care 2002) is software for hospitals and health care organizations. It is designed to integrate the different information systems existing in these organizations into a single efficient system. It solves the problems inherent in a network of multiple programs that are incompatible with each other. It can integrate almost any type of services, systems, departments, clinics, processes, data, or communication that exist in a hospital. Its design can even handle non-medical services or functions like security or maintenance. All of its functions can be accessed with a Web browser, and all program modules are processed on the server side.
Lindenmayer Systems in Python provides a simple implementation of Lindenmayer systems (also called "L-systems" or "substitution systems"). In basic form, a Lindenmayer system consists of a starting string of symbols from an alphabet which has repeated transitions applied to it, specified by a list of transition search-and-replace rules. In addition to the standard formulation, two alternative implementations are included: sequential systems (in which at most one rule is applied) and tag systems (in which the transition only takes place at the beginning and end of the string). Despite being implemented entirely in Python, for reasonable rules on a modern machine, the system is capable of running thousands of generations per second. Lindenmayer systems are found in artificial intelligence and artificial life and can be used to generate fractal patterns (usually via mapping symbols from the alphabet to turtle commands), organic-looking patterns that can simulate plants or other living things, or even music.
RISO is an implementation of heterogeneous, distributed belief networks in Java. A belief network is a probability model defined on an acyclic directed graph; distributed means nodes can be on different hosts, and heterogeneous means allowing different conditional distributions. The calculations involved are multidimensional integrations; exact results are known for a catalog of special cases. If a partial result cannot be calculated as a special case from the catalog, RISO computes an approximate result by numerical integration. Partial results are passed from one node in the graph to another as messages; if nodes live on different hosts, the belief network is said to be distributed. Messages are passed via RMI. Many example belief networks and lengthy documents are included in the RISO release bundle.
ZOE (formerly OGLE) is a simple OpenGL graphics engine written entirely in Python. Its primary focus is rapid prototyping and experimentation, so it only supports the barest essentials, with focus on wire frames. Special emphasis is placed on particle systems (in which non-interacting particles follow simple rules). Some familiarity with OpenGL is expected, although when exploiting the particle system abstractions, no specific OpenGL knowledge is required. The demos included are the obligatory spinning polyhedra, static views of conic sections and the Solar System, a 3D surface plotter, a fountain of sparks, a swarming behavior model, a random walk example, a whirlpool effect using gravity and drag, and an example of chaos theory and sensitivity to initial conditions.
Allegro Common Lisp is a full ANSI Common Lisp (1994) implementation. It contains many extensions, including 32- and 64-bit native compilation, efficient built-in memory management, foreign functions (for interfacing with other languages), multiprocessing, UNICODE and locale support, XML/HTML parsers, a Web client and server, GTK+ interface (1.2 and 2.0), Java interface, OLE interface (Windows only), profiler, regular expressions, an XML RPC implementation, native Lisp RPC, sockets, DLL and shared library support, and more.
SIP provides image processing, pattern recognition, and computer vision routines for SciLab, a Matlab-like matrix-oriented programming environment. SIP is able to read/write images in almost 90 major formats, including JPEG, PNG, BMP, GIF, FITS, and TIFF. It includes routines for filtering, segmentation, edge detection, morphology, curvature, fractal dimension, distance transforms, multiscale skeletons, and more.
Stochastic discrimination is a general methodology for constructing classifiers appropriate for pattern recognition. It is based on combining arbitrary numbers of very weak components, which are usually generated by some pseudorandom process, and it has the property that the very complex and accurate classifiers produced in this way retain the ability, characteristic of their weak component pieces, to generalize to new data as complexity increases. These utilities provide an implementation of this algorithm.