Course Coordinator: 
Denis Konstantinov

Measurement is fundamental to scientists in all disciplines.   This course will look at ways to make measurements and to avoid many of the pitfalls encountered in common and unusual measurements.  A sound theoretical basis will be provided to allow students to go on to make their own choices with confidence and experience. Topics will include instrumentation, physical noise processes, signal transduction, models of small signal amplification, as well as modulation, detection, synchronous and lock-in detection, signal sampling techniques, digitization, signal transforms, Fourier analysis. Theoretical techniques to be presented will be centered around probability, probability theory, probability distributions, statistical inference, information theory, exact cases, and Gaussians.

This course describes fundamental problems in measurement and cutting-edge solutions to them.
Detailed Syllabus: 
  1. Information theory: signals, background and noise.
  2. Probability, distributions, Gaussians, Boltzmanns
  3. Sample size and Power of analysis
  4. Signal sampling techniques
  5. Frequency and digitization
  6. Fourier and other transforms
  7. Instrumentation
  8. Amplifiers
  9. Modulation
  10. Time-locked measurements, synchronous and asynchronous events
  11. Analog instruments
  12. Noise reduction
  13. Small signals
  14. Projects
  15. Projects
Course Type: 
Projects (2 x 30%) 60%; Final Exam 40%.
Text Book: 
Modern Instrumentation for Scientists and Engineers, by John Blackburn (2000) Springer
Essentials of Mathematical Methods in Science and Engineering, by Selcuk Bayin (2008) Wiley-Interscience
Reference Book: 
The Art of Electronics 2 edn, by Horowitz and Hill (1989) Cambridge University Press
The Electrical Engineering Handbook 2 edn, by Richard C Dorf (1997) CRC Press