Agenda

Smart Sensor Systems 2018

Smart Sensor Systems 2018

This course addresses the design and development of smart sensor systems. After a general overview, various key aspects of sensor systems are discussed: measurement and calibration techniques, the design of precision sensor interfaces, analog-to-digital conversion techniques, and sensing principles for the measurement of magnetic fields, temperature, capacitance, acceleration and rotation. The state-of-the-art smart sensor systems covered by the course include smart magnetic-field sensors, smart temperature sensors, physical chemosensors, multi-electrode capacitive sensors, implantable smart sensors, DNA microarrays, smart inertial sensors, smart optical microsystems and CMOS image sensors. A systematic approach towards the design of smart sensor systems is presented. The lectures are augmented by case studies and hands-on demonstrations.

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Overview of Conferences

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