Mining the VVV: star formation and embedded clusters

The aim of this study is to locate previously unknown stellar clusters from the VISTA variables in the V\'ia L\'actea Survey (VVV) catalogue data. The method, fitting a mixture model of Gaussian densities and background noise using the expectation maximization algorithm to a pre-filtered NIR survey stellar catalogue data, was developed by the authors for the UKIDSS Galactic Plane Survey (GPS). The search located 88 previously unknown mainly embedded stellar cluster candidates and 39 previously unknown sites of star formation in the 562 deg2 covered by VVV in the Galactic bulge and the southern disk

Monitoring near-Earth-object discoveries for imminent impactors

Aims. We present an automated system called neoranger that regularly computes asteroid-Earth impact probabilities for objects on the Minor Planet Center's (MPC) Near-Earth-Object Confirmation Page (NEOCP) and sends out alerts of imminent impactors to registered users. In addition to potential Earth-impacting objects, neoranger also monitors for other types of interesting objects such as Earth's natural temporarily-captured satellites. Methods. The system monitors the NEOCP for objects with new data and solves, for each object, the orbital inverse problem, which results in a sample of orbits that describes the, typically highly-nonlinear, orbital-element probability density function (PDF). The PDF is propagated forward in time for seven days and the impact probability is computed as the weighted fraction of the sample orbits that impact the Earth. Results. The system correctly predicts the then-imminent impacts of 2008 TC3 and 2014 Lambda Lambda based on the first data sets available. Using the same code and configuration we find that the impact probabilities for objects typically on the NEOCP, based on eight weeks of continuous operations, are always less than one in ten million, whereas simulated and real Earth-impacting asteroids always have an impact probability greater than 10% based on the first two tracklets available.Peer reviewe

Software Newsroom – an approach to automation of news search and editing

We have developed tools and applied methods for automated identification of potential news from textual data for an automated news search system called Software Newsroom. The purpose of the tools is to analyze data collected from the internet and to identify information that has a high probability of containing new information. The identified information is summarized in order to help understanding the semantic contents of the data, and to assist the news editing process. It has been demonstrated that words with a certain set of syntactic and semantic properties are effective when building topic models for English. We demonstrate that words with the same properties in Finnish are useful as well. Extracting such words requires knowledge about the special characteristics of the Finnish language, which are taken into account in our analysis. Two different methodological approaches have been applied for the news search. One of the methods is based on topic analysis and it applies Multinomial Principal Component Analysis (MPCA) for topic model creation and data profiling. The second method is based on word association analysis and applies the log-likelihood ratio (LLR). For the topic mining, we have created English and Finnish language corpora from Wikipedia and Finnish corpora from several Finnish news archives and we have used bag-of-words presentations of these corpora as training data for the topic model. We have performed topic analysis experiments with both the training data itself and with arbitrary text parsed from internet sources. The results suggest that the effectiveness of news search strongly depends on the quality of the training data and its linguistic analysis. In the association analysis, we use a combined methodology for detecting novel word associations in the text. For detecting novel associations we use the background corpus from which we extract common word associations. In parallel, we collect the statistics of word co-occurrences from the documents of interest and search for associations with larger likelihood in these documents than in the background. We have demonstrated the applicability of these methods for Software Newsroom. The results indicate that the background-foreground model has significant potential in news search. The experiments also indicate great promise in employing background-foreground word associations for other applications. A combined application of the two methods is planned as well as the application of the methods on social media using a pre-translator of social media language.Peer reviewe

HybVIO : Pushing the Limits of Real-time Visual-inertial Odometry

We present HybVIO, a novel hybrid approach for combining filtering-based visual-inertial odometry (VIO) with optimization-based SLAM. The core of our method is highly robust, independent VIO with improved IMU bias modeling, outlier rejection, stationarity detection, and feature track selection, which is adjustable to run on embedded hardware. Long-term consistency is achieved with a loosely-coupled SLAM module. In academic benchmarks, our solution yields excellent performance in all categories, especially in the real-time use case, where we outperform the current state-of-the-art. We also demonstrate the feasibility of VIO for vehicular tracking on consumer-grade hardware using a custom dataset, and show good performance in comparison to current commercial VISLAM alternatives.acceptedVersionPeer reviewe

TETRA tukiasemien synkronointi GPS:n avulla

TETRA on digitaalinen radiopuhelinjärjestelmä, joka on tarkoitettu viranomais- ja erillisverkkokäytöön. Tämä työ kuvailee TETRA radioverkon tukiasemien synkronointia GPS:n avulla. Radioverkon tukiasemat täytyy synkronoida, jotta voidaan taata nopea puhelunsiirto silloin kun käytetään ilmatiesalausta. Tämän työn tavoite oli luoda säätöalgoritmi, joka mittaa TETRA kehyslaskureita ja säätää OCXO (tukiaseman pääkello) taajuutta. Algoritmi toteutetaan C kielellä. GPS on osoittautunut stabiiliksi ja luotettavaksi menetelmäksi verkon tukiasemien synkronointiin. Taajuus ja kehykset pysyvät hyvin määriteltyjen rajojen sisällä. OCXO taajuuden tarkkuus ja stabiilisuus on lisääntynyt paljon. Kehyssynkronointi ei tietenkään ollut ennen tätä mahdollista

HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry

We present HybVIO, a novel hybrid approach for combining filtering-based visual-inertial odometry (VIO) with optimization-based SLAM. The core of our method is highly robust, independent VIO with improved IMU bias modeling, outlier rejection, stationarity detection, and feature track selection, which is adjustable to run on embedded hardware. Long-term consistency is achieved with a loosely-coupled SLAM module. In academic benchmarks, our solution yields excellent performance in all categories, especially in the real-time use case, where we outperform the current state-of-the-art. We also demonstrate the feasibility of VIO for vehicular tracking on consumer-grade hardware using a custom dataset, and show good performance in comparison to current commercial VISLAM alternatives. An open-source implementation of the HybVIO method is available at https://github.com/SpectacularAI/HybVIOComment: 2022 IEEE Winter Conference on Applications of Computer Vision (WACV