Persons
doc. Ing. Jiří Novák, Ph.D.
Archive of PhD students
Ing. Jan Sobotka, Ph.D.
Methods for Verification and Validation of Automotive Distributed Systems
Ing. Petr Ježdík, Ph.D.
Department of Circuit Theory
Possibility of Vehicular Electric Equipment Diagnostics Using Power Supply Network Transient Analysis
Dissertation topics
Methods for testing of vehicle distributed systems
- Branch of study: Electrical Engineering and Communications
- Department: Department of Measurement
-
Description:
Vehicle distributed applications use heterogenous communication infrastructure. Its functionality in wide range of environmental conditions directly influences reliability and functional safety of vehicles. Research is focused on methods for validation of internal parametrization of vehicle network nodes, methods for monitoring and failure prediction in communication infrastructure and methods of automated integration testing of vehicle distributed applications. http://measure.feld.cvut.cz/jnovak
Methods for testing of vehicle distributed systems
- Branch of study: Electrical Engineering and Communications
- Department: Department of Measurement
-
Description:
Today vehicles use heterogeneous communication infrastructure based on different technologies. Its functionality in wide range of environmental conditions directly influences reliability and functional safety of vehicles. Research is focused on methods for validation of internal parametrization of vehicle network nodes and methods for monitoring and failure prediction in communication infrastructure.
Model-based testing of vehicle electronics
- Branch of study: Electrical Engineering and Communications
- Department: Department of Measurement
-
Description:
Particular functions of today vehicles are usually implemented by distributed applications, executed in parallel by several electronics control units, which are connected by the communication infrastructure. Research is focused on methods of functional and integration testing of vehicle distributed applications, which are based on automated test generation from the functional models and on using machine learning methods to optimize the testing process.
