Den här sidan är utskriven från Högskolan i Halmstads webbplats (www.hh.se). Texten uppdaterades senast den 2016-05-13. Besök webbplatsen om du vill vara säker på att läsa den senaste versionen.
CAISR, the Center for Applied Intelligent Systems Research, is a long-term research program on intelligent systems established by Halmstad University. The program is funded by the University and the Knowledge Foundation with support from Swedish Industry.
The subject expertize in the center is in signal analysis, machine learning and mechatronics. The center also has an emphasis on cooperating systems, in line with the research focus for the larger EIS environment. Several industrial partners are collaborating with researchers from the University in joint projects, and also take an active part in the development of CAISR. The key application areas that the center does research in are intelligent vehicles and health care technology. The industrial partners include multinational companies as well as research-based growing companies.
The mission of CAISR is to serve and promote the development of industry and society. It is a center for industrially motivated research on the future technologies for and application opportunities with aware intelligent systems. CAISR will serve as a partner for industry´s own research and development, as a recruitment base for those who seek staff with state-of-the-art knowledge in intelligent systems technologies, and as a competence resource for industry and society. All research is conducted within different research projects.
Download CAISR Annual report 2015 (pdf, 2.2 MB)
Automatic Inventory and Mapping of Stock (AIMS) is a research project within CAISR in collaboration with three industrial partners – Toyota Material Handling, Kollmorgen and Optronic. The goal of the project is to build a robotic system for warehouse inventory. The research will contribute to automated guided vehicles becoming more safe, intelligent and flexible.
"Research on the design of systems that, as autonomously as possible, can construct knowledge from real life data created through the interaction between a system and its environment. This data necessarily includes streaming data. Such systems should be able to handle events that are unknown at the time of design."