The members of the SCS lab are currently involved in two major research directions:
a) Signal processing using nonlinear circuits and systems - cellular neural networks (CNN), chaotic circuits and systems and hysteretic circuits - in cooperation with the Microtechnology Institute of Neuchatel, Switzerland (prof. Michael Ansorge), and the Institute of Computer Science and Automation from the Hungarian Academy of Sciences Budapest, Hungary (prof. Tamas Roska).
We conduct studies concerning the possibilities of signal processing and classification using CNN's, pattern formation in first and second order CNN's, Turing patterns, using hysterons in signal processing (including structures of CNN's with hysteretic cells), hysteresis in chaotic signal generation and chaos sinchronisation.
Other research directions are related to analog and discrete recurrent neural networks (other than CNN's). Implementation of the "computing with attractors" paradigm was considered in the framework of associative memory design. Applications include time series analysis, particularly short-time prediction, and complex classification tasks.

b) High-frequency controlled analog filter design, in cooperation with Electronics Department of University of Southern California, USA (prof. John Choma), University of New-York at Stony Brook (prof. Adrian Leuciuc), and Chip-Express Co., USA.
We focus on high frequency active filter structures, stability, frequency response, distortion evaluation and VLSI implementation.

Since 2000 our group is recognized as a Research Centre of Excellence, ranked 6th among all research institutions in Romania, and 2nd among those in Electrical and Electronics Engineering.

• Applications of the CNN Universal Machine in pattern formation and recognition
• Hysteretic CNN: a new paradigm in pattern formation and recognition
• New methods in EEG signal processing with applications in diagnosis
• Symbolic methods applications in electrical engineering
• Low sensitivity high order modulators for oversampling A/D converters
• Core functionalities for low-power intelligent/biometric signal processing for secured access to teleservices