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Apache Empire-db is an Open Source relational data persistence component which allows database vendor independent dynamic query definition as well as safe and simple data retrieval and updating. Compared to most other solutions like e.g. Hibernate, TopLink, iBATIS or JPA implementations, Empire-db takes a considerably different approach, with a special focus on compile-time safety, reduced redundancies and improved developer productivity.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

An low latency, lock free, primitive bounded blocking queue backed by an int[]. This class mimics the interface of {@linkplain java.util.concurrent.BlockingQueue BlockingQueue}, however works with primitive ints rather than objects, so is unable to actually implement the BlockingQueue. This class takes advantage of the Unsafe.putOrderedObject, which allows us to create non-blocking code with guaranteed writes. These writes will not be re-orderd by instruction reordering. Under the covers it uses the faster store-store barrier, rather than the the slower store-load barrier, which is used when doing a volatile write. One of the trade off with this improved performance is we are limited to a single producer, single consumer.

This package supports the parsing of AIS messages in Java. AIS, the Automatic Identification System, is a system aiming at improving maritime safety by exchanging messages between ships, other vehicles in particular aircraft involved in search-and-rescue (SAR), and (fixed) base stations. To be precise, this package support the ITU-R M.1371-4 AIS standard. See our extensive javadoc and in particular the class AISParser for more information on how to use this package. The parser was used in the Poseidon project, and is improved in the Metis project to better handle uncertain information. Both projects were led by the Embedded Systems Institute. In both projects Thales Nederlands was the carrying industrial partner, and multiple Dutch universities participated.

The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. The current version can be used for classification using numeric predictors. For more information see: I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support. In: 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003; V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas, V. Petridis (2003). Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.