In mixed-signal test, we develop oscillation based test structures for testing selected classes of analog circuits, in particular active RC filters and switched-capacitor circuits. We explore the use of these structures for stimulus generation of a built-in self-test (BIST). Particular interest is given to the solutions based on the IEEE 1149.1 and IEEE 1149.4 infrastructure. We also explore possibilities of extending IEEE 1500 wrapper to mixed-signal test. Recently, a feasibility study of implementing the histogram based ADC built-in self-test within the IEEE 1500 test wrapper has been performed. In collaboration with Hyb, Sentjernej, we develop techniques for testing and diagnosing piezoresistive pressure sensors.
In digital test, we focus on the application-oriented testing of embedded processor cores implemented in SRAM-based FPGAs. We developed a concept that combines the whole instruction-set test into a compact test sequence, which can then be repeated with different input test patterns. This considerably improves the fault coverage with no additional memory requirements. Such a compact and efficient test solution is suitable for built-in self-test implementations.
We addressed the security problem of systems incorporating IEEE Std. 1149.1 infrastructure and developed a security scheme based on a locking mechanism that prevents unauthorized users to access system via standard test access port. Currently we work on test data encryption issues. [1, 2]
The most common definition of computer vision as a research area is to process images acquired with cameras in order
to produce a representation of objects in the world. It is an attempt to mimic the human visual ability with engineering.
Computer vision is a mixture of specialized research branches and application domains, including robot vision, machine vision,
image processing, image understanding, pattern recognition and some artificial intelligence techniques. We practice but are
not limited to 3D robot vision, machine vision, image processing and pattern recognition. We try to combine theoretical
approaches with practical implementations.
The applications of computer vision close to our expertise are: visual servoing of industrial robots, analysis of biomedical images, building extraction from aerial and satellite images, machine vision applications like on-line quality control, non-contact metrology, texture analysis, assembly of parts, inspection etc, visual analysis of two-phase bubble flow, digital map processing, satellite positioning on digital maps. 
VLSI technology offers several solutions for aggressive exploitation of the instruction-level parallelism in future generations of microprocessors. The research is oriented towards architectural mechanisms and implementation techniques that exploit the fine-and coarse-grain parallelism in microprocessor.