MOA: Massive Online Analysis, a framework for stream classification and clustering.

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. It contains collection of offline and online for both classification and clustering as well as tools for evaluation. In particular, for classification it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. For clustering, it implements StreamKM++, CluStream, ClusTree, Den-Stream, D-Stream and CobWeb. Researchers benefit from MOA by getting insights into workings and problems of different approaches, practitioners can easily apply and compare several algorithms to real world data set and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license

Application of multivariate analysis in the processing of medical data

Medical data frequently represent multidimensional datasets as investigated factors and clinical and laboratory parameters coverage is huge. This research area is very important in terms of practical applications. We were given monthly lipid metabolism and hormonal status data of children (including children suffering from obesity) of Siberian region during a year. In this article some research results appear

Prediction of body composition in mirror carp (Cyprinus carpio) by using linear measurements in vivo and computed tomography post-mortem

The mirror carp (Cyprinus carpio) belongs to the cyprinids, the world's largest and most important fish family in aquaculture. The fat content and the fillet yield are important parameters in the marketing of carp. Although the influence of the environment on the body composition of the carp has been well studied, there is little research in the field of breeding. For this purpose, precise phenotyping is indispensable. Therefore, during this study a total of 33 mirror carps were examined using computed tomography (CT) technology. First, the fish were examined alive. Total body weight and linear measurements such as lengths, height and circumferences were measured, and ultrasound was used to determine the back-fat thickness. The fish were then slaughtered and whole body scans of all fish using CT were made. The carps were filleted and the fillets with skin were chemically analyzed. In order to predict the chemical fillet fat content, thickness measurements and volume calculations of the back fat were carried out using CT. Compared to the CT-based back-fat thickness measurement correlated with the results from the chemical analysis (R-2 = 0.62), the CT-based volume measurement of the back fat leads to a higher coefficient of determination (R-2 = 0.85). Prediction results can still be improved by adding linear measurements. The in vivo ultrasound (US) examination of the back-fat thickness was compared with the CT back-fat thickness results. The measurements of the back-fat thickness took place at similar positions in the fish. Coefficients of determination (R-2) of 0.63 to 0.77 were obtained. The back fat in mirror carp proved to be an interesting area for determining the fillet fat content. The evaluation of the fillet yield resulted in a mean value of 42.89 % with a standard deviation of +/- 2.43. Fillet yield (%) correlated with CT-based fillet thickness measurement resulted in a moderate coefficient of determination (R-2 of 0.45). A similar coefficient of determination was achieved with selected linear measurements

Ground verification of the feasibility of telepresent on-orbit servicing

In an ideal case telepresence achieves a state in which a human operator can no longer differentiate between an interaction with a real environment and a technical mediated one. This state is called transparent telepresence. The applicability of telepresence to on-orbit servicing (OOS), i.e., an unmanned servicing operation in space, teleoperated from ground in real time, is verified in this paper. For this purpose, a communication test environment was set up on the ground, which involved the Institute of Astronautics (LRT) ground station in Garching, Germany, and the European Space Agency (ESA) ground station in Redu, Belgium. Both were connected via the geostationary ESA data relay satellite ARTEMIS. Utilizing the data relay satellite, a teleoperation was accomplished in which the human operator as well as the (space) teleoperator was located on the ground. The feasibility of telepresent OOS was evaluated, using an OOS test bed at the Institute of Mechatronics and Robotics at the German Aerospace Center (DLR). The manipulation task was representative for OOS and supported real-time feedback from the haptic-visual workspace. The tests showed that complex manipulation tasks can be fulfilled by utilizing geostationary data relay satellites. For verifying the feasibility of telepresent OOS, different evaluation methods were used. The properties of the space link were measured and related to subjective perceptions of participants, who had to fulfill manipulation tasks. An evaluation of the transparency of the system, including the data relay satellite, was accomplished as well

Hydrothermal alteration mapping of Siberian gold-ore fields based on satellite spectroscopy data

The mapping of the hydrothermal alterations in Urjahskoe and Fedorov-Kedrov gold-ore fields was conducted by applying channel relationship method (band ratio) based on ASTER spectral-zonal satellite image data. It was determined that the calculated mineral indices in ore-bearing structures are zonal. Outer ore-bearing structures revealed increased ferric mineral index values, while inner - high epidote- chlorite- calcite and muscovite- siderite mineral index values. Detected regularities could be used in identifying potential gold-ore bearing areas within identical fields based on remote sensing survey data