The course will cover the major principles in signal acquisition and evaluation. Topics will cover the main constituents of measurement including electronic, mathematical, computational and statistical aspects. Emphasis will be on the understanding of the fundamentals which must be understood in order to obtain meaningful data, and to understand its significance. Examples will be used from the physical and life sciences. Course requires a rudimentary mathematical and physics background or willingness to learn the same.
Mathematical background:
Fourier transforms, reciprocal space, Convolution and deconvolution, Error analyses (independent sources, systematic errors, etc), Digitization, A/D and D/A conversion.
Data Acquisition:
Sampling considerations, Nyquist thereom, bit noise, Transfer functions, Bandwidth issues, Amplifiers and their characteristics.
Data Processing (in spectroscopy and in imaging):
Noise sources and signal/noise considerations , Filtering (real space and Frequency space), Resolution and object recognition.
Data evaluation:
fitting types and their evaluation , Significance, Estimations, Errors and uncertainty analyses.