Agenda

MSc TC Thesis Presentation

Blind Signal Identification

Dennis van der Geest

The capability to efficiently find signals of interest in a very dense electromagnetic spectrum is becoming increasingly important with the continuous increase in spectrum usage. In this research project, methods are developed to identify communication signals by estimating signal features (symbol rate, modulation scheme, etc.) in the absence of a-priori knowledge, i.e. blind. By modelling the received communication signal both as a stationary and a cyclostationary process, various feature estimation methods are evaluated based on their computational complexity, their estimation accuracy and their robustness in the presence of signal contamination, such as frequency offsets. By efficiently combining various estimation methods, a signal classification algorithm is derived which is aimed to provide an optimal tradeoff between computational complexity and classification performance.

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Overview of MSc TC Thesis Presentation

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