Vacancy No. IPE 16-19
IPE 16-19 Master Thesis or Internship: Detecting lightning strikes in high-speed videos
The Aragats Space Environmental Center (ASEC) studies the first stages of the lightning initiation and develops models of electron acceleration in the thunderclouds. Multiple detectors located close to the summit of Mt. Aragats record the electric field and the inciting fluxes of neutral and charged particles at very high rates. Additionally, multiple high-speed visible-light cameras continuously monitor the sky. Due to limitations of the available data storage, it is impossible to record the complete video stream. Instead, an intelligent data acquisition software is required to identify frames with lightning events and store only these.
During the internship the student will analyze methods to detect lightnings in the video stream and develop a real-time implementation of these methods. These methods should be robust enough to handle also low quality videos acquired with the low exposure times and night scenes. As the required frame rates exceeds 1000 fps, an efficient parallel implementation for the modern GPU accelerators is needed. Additional speed-up can be achieved by correlating the data from other detectors with lower data rates to pre-filter time intervals when the lightnings are likely to happen. The project is carried out in collaboration between KIT and ASEC in Armenia.
Good background in computer vision, strong knowledge of C/C++ programming.
Prior experience in parallel programming using CUDA or OpenCL is a plus.
Institute for Data Processing and Electronics (IPE)
as soon as possible
limited, according to the study regulations
Suren Chilingaryan firstname.lastname@example.org, IPE, Phone: +49 721 / 608 26579
Andreas Kopmann email@example.com, IPE, Phone: +49 721 / 608 24910
Please apply online using the button below for this vacancy number IPE 16-19.
Personnel support is provided by
phone: +49 721 608-25184,
Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
If qualified, severely disabled persons will be preferred.