Information and Measurement

Introduction to course Course goal and outline, class schedule, assignments and exams. Introduction to information theory and statistics -Introduction to information theory: information, binary choices, probability, entropy [bit], information transmission rate, sensor, calibration, signal vs. message, code, sampling, noise sources, effect of noise, channel capacity, noise can be helpful! (dithering) -Measurement errors and uncertainties: measurement, true value, random and systematic errors, uncertainty due to random errors, uncertainty due to systematic errors, total uncertainty, propagation of uncertainties, combination of separate measurements, correlation and regression -Examples and case studies: sensor fusion, rejection of data, error analysis in flow measurement (ASME standard on measurement uncertainty), stochastic resonance in natural systems Instrument and measurement -Sensor characteristics: transfer function, sensitivity, dynamic range, uncertainty, sensor classification -Dielectric materials for sensing: capacitive sensors, piezoelectric effect, piezoelectric crystal resonator -Optical fundamentals: wavefront, image quality (aberration, diffraction) -Optical microscopy: Kohler illumination, bright and dark fields, phase-shift microscopy (Zernike), differential interference contrast, fluorescence microscopy, confocal microscopy -Interferometry: Maxwell’s equations, interferometers (Michelson, Mach-Zender, Fabry-Perot), Fourier transform infrared spectrometer (case study), problems -Holography: scalar diffraction theory, wavefront reconstruction, in-line hologram (Gabor), off-axis hologram (Leith), Fourier hologram, applications -Light sensors: light interaction with matter, phototubes, photoresistors, p-n junction, p-i-n junction, metal-semiconductor junction -Magnetic sensors: spin, magnetic moment, diamagnetism, paramagnetism, ferromagnetism, Hall’s effect, magnetoresistance effects -Surface-plasmon-resonance based sensors: optical properties of metal, evanescent fields, surface plasmon resonance, bio-sensors Data analysis and modeling Signal processing: Fourier transform, sampling theorem, data pre-processing; Case study: lock-in amplification, zero padding Time series modeling: stationarity, autoregressive average models, nearest neighbor models, neural network models

Evaluation is based on assignments.

J. R. Taylor, An introduction to error analysis, University Science Books, 1997. J. Fraden, Handbook of modern sensors, AIP press, Springer, 2003. R. Figliola, D. Beasley, Theory and design for mechanical measurements, Wiley, 2006.

基礎を固める（工学部共通）