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Resistance is Useless

R. Resistance is Useless. Georg Simon Ohm and Modern Engineering Sciences. Georg Simon Ohm – Monument at TUM. Born on 16.3.1789 in Erlangen Died on 6.7.1854 in Munich. Ohm Resistor. Late Reconition in Germany. Ohm’s Law published the Galvanic Chain – Mathematically Treated – 1827

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Resistance is Useless

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  1. R Resistance is Useless Georg Simon Ohm and Modern Engineering Sciences

  2. Georg Simon Ohm – Monument at TUM Born on 16.3.1789 in Erlangen Died on 6.7.1854 in Munich

  3. Ohm Resistor

  4. Late Reconition in Germany • Ohm’s Law published • the Galvanic Chain – Mathematically Treated – 1827 • Copley Medal – 1841 • Member Royal Society London – 1842 • Professor for Physics in Munich – 1849 • Bavarian Academy of Sciences – 1850

  5. Ohm‘s Experiment Silk Thread Magnetic Needle Magnifying Lense Mercury Pads for Connecting Copper Wires Bismuth Copper

  6. Ohm‘s Experiment

  7. Experimental Setup – Schematically

  8. Data Acquisition – Measurements Copper wires of length [inches] 2, 4, 6, 10 18, 34, 66 and 130 and of thickness 7/8 inch

  9. The Model

  10. Mathematical Model for Measurement Data • – Strength of magnetic force • – Length of copper wire • – Constant, only dependent on exciting force • – Constant, only dependent on the invariant part of the setup

  11. Experimentally measured data Data calculatedfrommodel

  12. Ohm‘s Law • Scientific discovery in physics via • - empirical measurements followed by • - mathematical modeling • - no explanation of physical processes •  Impact on Modern Engineering Sciences

  13. Modern Engineering Science Black-Box Model measurable observable Output Input Abstract Input-Output Description

  14.  Modeling Human Visual Perception Example: Compressed Digital Video Decoder Encoder (Compression) Data Stream Video Visual Quality Control

  15. Test persons judge visual quality of video  entries in - Vector Subjective Testing

  16. Subjective testing

  17. Black-Box Modeling • Find a (linear) model for input-output relation • measurable features in video  u – variables • observable video quality  y – variable measurbale observable Blur Blocking HVS Subj. Quality Video Input Noise

  18. Black-Box Model for subjective image quality Objective Measurement Subjective test Video [Visual Quality] [Blur, Blocking, Detail, Motion, …,Noise] Blocking Motion Detail Noise Blur Video1 Video2 Video3 Video4 : VideoN PLSR, PCR … Regression- techniques (PCR ≈ SVD)

  19. Results

  20. Thanks

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