Šio darbo tikslas – sukurti efektyvius įvykių atpažinimo filtrus duomenims, gautiems pasitelkiant pagreičių matavimo prietaisus mobiliosiose technologijose. Atlikus sukurtų filtrų nuodugnius tyrimus įvertinti, kurie iš filtrų įvykius atpažįsta efektyviausiai. Neuroninių tinklų pagalba išskirti duobę ir greičio ribojimo kalnelį iš bendro įvykių srauto. Sukurtą prototipą realizuoti praktiškai ir išsamiai atvaizduoti gaunamus rezultatus. Taip pat susipažinti su kitų autorių sukurtais atpažinimo modeliais bei sistemomis, palyginti jų sukurtų algoritmų bei šio darbo atpažinimo rezultatus, praktiškai realizuoti išnagrinėtą modelį. Užfiksuotoms eismo įvykių koordinatėms pritaikyti tinkamiausią duomenų grupavimo algoritmą ir išsamiai atvaizduoti gautus rezultatus. / The aim of this work was to analyze the data which was gotten from accelerometer mounted in mobile device during the test drives through the city together with GPS (Global Positioning System) coordinates, to detect and report the surface conditions of roads as well as to find the way, how it could be represented in the map. The research was started by analyzing oscillation data from accelerometer. We had to keep in mind that there can be road bumps, pit holes, speed bumps and other road anomalies, car can accelerate quickly and break sharply or even crash into something, what would cause a sudden stop. In order to recognize events, different detection filters were applied on data. In addition to this, neuron network was used to recognize pit holes and speed bumps from all event flow. The results of event detection algorithms were compared with other scientist’s works. In order to represent results clearly, database was created holding coordinates of the road events and other information like time, etc. The results were represented using an application programming interface made-up by Google, which was really suitable solution in our case. The whole system was programmed using Java servlets, which allowed to gather data from database using SQL (Structured Query Language) queries. While trying to represent accelerometer data, we faced difficulties in representing these road events on the map, as GPS each time returned answer with small variation of coordinates. In this case, we... [to full text]