Fing is a command line tool for network and service discovery. It provides you a complete view of any network in a very short time. Its smart discovery automatically detects the network type and uses the best technique to scan it. The best results are achieved on Ethernet networks (including Wireless ones), where Fing is able to detect all network hosts, firewalled ones included. The service discovery feature quickly detects active TCP services on a target host or network. Fing is based on Look@LAN.
GoFigure2 is a cross-platform application for visualization, processing, and analysis of out-of-core multidimensional microscopy data (5D data sets). Users can visualize images, segment cells in 3d, track cells through time, and detect cell divisions to generate lineages. Results are stored in a MySQL database back-end. Once data has been processed, cell-based object features are quantified and can be used for sorting, color-coding, analysis, or exported to external tools. GoFigure2 was developed for biology research including studying the development of embryos (zebrafish and mouse), synthetic biology (signaling), and for drug screening.
PySide provides a full set of Qt bindings and automated binding generation tools. The binding generation tools can be useful for creators of Python bindings to any Qt-based library or to any C++ library in general. Although based on a different technical approach, PySide will initially be API-compatible with existing Python bindings for Qt.
I Only Think (IOT) is a brain-computer interface that allows you to control a computer by thought. It relies on the OpenViBE technology to analyze electrical signals emitted by the brain. The project is primarily intended for people with reduced mobility, allowing them more autonomy.
EO is a template-based, ANSI-C++ evolutionary computation library that helps you to write your own stochastic optimization algorithms quickly. Evolutionary algorithms form a family of algorithms inspired by the theory of evolution, and solve various problems. They evolve a set of solutions to a given problem in order to produce the best results. These are stochastic algorithms because they iteratively use random processes. The vast majority of these methods are used to solve optimization problems, and may be also called "metaheuristics". They are also ranked among computational intelligence methods, a domain close to artificial intelligence. With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems, from continuous to combinatorial ones.