BNNS is a research tool for interactive training of artificial neural networks based on the Response Function Plots visualization method. It enables users to simulate, visualize, and interact in the learning process of a Multi-Layer Perceptron (MLP) on tasks that have a 2D character. Tasks include the famous two-spirals task or classification of satellite image data.
Biomechanical ToolKit (BTK) is a cross-platform library for biomechanical analysis. It can read and write a large variety of file formats used in biomechanics, and can modify them. All these operations can be done with the C++ API or with the wrappers included (Python, Octave, and Matlab). The goal of this project is to help the community share data without the restriction of the file format or the biomecanical model provided by the manufacturer of the acquisition system.
Mokka (MOtion Kinematics and Kinetics Analyzer) is a software solution for analyzing biomechanical data. It reads and writes C3D files and many other file formats, and allows you to visualize marker trajectories in 2D and 3D, and force platforms, segments, joint angles, forces, moments, and analog signals like EMGs.
Gcmc is a front-end language for generating G-code, SVG, and DXF for CNC mills, lathes, laser cutters, and other numerically controlled machines employing G-code, SVG, or DXF. The language is a context-free grammar created to overcome the archaic format of G-code programming, but can be used more generally for many targets. Gcmc aims to be more readable and understandable than G-code and enable programmatic designing. Gcmc makes extensive use of vector mathematics to support the 3D nature of CNC machining. It handles units as millimeters, mils (inch), degrees, and radians and performs automatic conversions where necessary.
The Graphical Models Toolkit (GMTK) is a toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). It can be used for speech and language processing, bioinformatics, activity recognition, and any time series application. It features exact and approximate inference, many built-in factors including dense, sparse, and deterministic conditional probability tables, native support for ARPA backoff-based factors and factored language models, parameter sharing, gamma and beta distributions, dense and sparse Gaussian factors, heterogeneous mixtures, deep neural network factors, and time-inhomogeneous trellis factors, arbitrary order embedded Markov chains, a GUI graph viewer, and much more.
WordBash is a Wordpress clone written in Bash, the GNU Bourne-Again SHell. Everything is generated and output by Bash. No AWK. No SED. No TR. It is all Bash (with one exception). It is a CGI script with an attempt at an OO-like design. It currently supports posts (categories, tags), comments (with spam rejection), themes (true templates), and code highlighting as an administration addon. The current script size is 21 files of about 2500 lines and only 44 kilobytes.