Introduction

The research area of the Circuits and Systems group covers the theory and applications of signal processing, including high-level digital system design.

Presentations

This link will direct you to some slide presentations which are used to introduce the group to starting MSc students.
The complexity of electronic circuits is ever increasing, and so is their design. Two drivers are (i) already phenomenal integration densities are still doubling every 18 months (Moore's law), and (ii) new advanced applications require integrated solutions with increased intelligence and immense processing power. The main goal in our research program is to provide a sound mathematical framework for synthesis and analysis problems in the complete trajectory from system application, algorithm design, mapping to a hardware architecture or embedded system, VLSI circuit design, and finally the design verification.

In the news: This simple piece of plastic makes 3D ultrasound easy

The CAS group participates in four MSc tracks: MSc Telecommunication and Sensing Systems, MSc Signals and Systems, MSc Microelectronics, MSc Computer Engineering.

CAS consists of 9 professors and about 40 researchers.

Agenda

Microelectronics Colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Tenure track colloquium

Daniele Cavallo (TS group); wideband phased arrays for future wireless communication terminals, Massimo Mastrangeli (ECTM Group); Towards smart organs-on-chip, Sten Vollebregt (ECTM group) Emerging electronic materials: from lab to fab

Signal Processing Seminar

Krishnaprasad Nambur Ramamohan

Signal processing algorithms for acoustic vector sensors

Symposium MRI for Low-Resource Setting

Steven Schiff, Johnes Obungoloch

Sustainable Low-Field MRI Technology for Point of Care Diagnostics in Low-Income Countries

Kick-off meeting of the project "A sustainable MRI system to diagnose hydrocephalus in Uganda"

Signal Processing Seminar

Peter Gerstoft

Machine learning in physical sciences

Machine learning (ML) is booming thanks to efforts promoted by Google. However, ML also has use in physical sciences. I start with a general overview of ML for supervised/unsupervised learning. Then I will focus on my applications of ML in array processing in seismology and ocean acoustics. This will include source localization using neural networks or graph processing. Final example is using ML-based tomography to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a 5200-element array. This method exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data.

Signal Processing Seminar

Aydin Rajabzadeh

manufacturing defect detection