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Resistance is Useless
R
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
Copley Medal – 1841
Member Royal Society London – 1842
Professor for Physics in Munich – 1849
Bavarian Academy of Sciences – 1850
Ohm‘s Experiment
Silk Thread
Magnetic Needle
Magnifying Lense
Mercury Pads for
Connecting Copper Wires
Bismuth
Copper
Ohm‘s Experiment
Experimental Setup – Schematically
Data Acquisition – Measurements
Copper wires
of length [inches]
2, 4, 6, 10 18, 34, 66 and 130
and of thickness 7/8 inch
The Model
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
Experimentally
measured data
Data calculated
from model
Ex
Copper Wire
1
2
3
4
5
6
I
326 ¼
300 ¾
277 ¾
238 ¼
190 ¾
134 ½
83 ¼
48 ½
II
311 ¼
287
267
230 ¼
183 ½
129 ¾
80
46
III
307
284
263 ¾
226 ¼
181
128 ¾
79
44 ½
IV
305 ¼
281 ½
259
224
178 ½
124 ¾
79
44 ½
V
305
282
258 ¼
223 ½
178
124 ¾
78
44
Cal
7
8
Copper Wire
1
2
3
4
5
6
7
8
I
328
300 ½
277 ½
240 ¾
190 ½
134 ½
84 ¼
48 ½
II
313
287 ¼
265 ½
230 ¼
182
128 ½
80 ¾
46 ½
III
309 ½
284
262 ½
228
180
127
79 ¾
45 ¾
IV
305 ½
280 ½
259
224 ¾
177 ¾
125 ¼
79
45
V
305 ½
280 ½
259
224 ¾
177 ¾
125 ¼
79
45
Ohm‘s Law
Scientific discovery in physics via
- empirical measurements followed by
- mathematical modeling
- no explanation of physical processes
 Impact on Modern Engineering Sciences
Modern Engineering Science
Black-Box Model
Abstract Input-Output Description
observable
Output
Input
measurable
Example: Compressed Digital Video
Encoder
Video
Decoder
Data Stream
(Compression)
Control
Visual Quality
 Modeling Human Visual Perception
Subjective Testing
Test persons judge visual quality of video  entries in
- Vector
Subjective testing
Main - Tempete - CIF
5
4
3
2
1
AVC_768 AVC_384 MP4_768 AVC_192 MP4_384 MP4_192 AVC_096 MP4_096
Black-Box Modeling
measurbale
observable
Subj. Quality
Video Input
Blur
Blocking
HVS
Noise
Find a (linear) model for input-output relation
measurable features in video
 u – variables
observable video quality
 y – variable
Black-Box Model for subjective image quality
Objective Measurement
Subjective test
Video
[Blur, Blocking, Detail, Motion, …,Noise]
Video1
Video2
Video3
Video4
:
VideoN









x
x
x
x
M
x
x
x
x
x
M
x
x
x
x
x
M
x
x
x
x
x
M
x
L
L
L
L
L
x 

x 
x 
x 
M
x 
[Visual Quality]
PLSR, PCR …
Regressiontechniques
(PCR ≈ SVD)
 y
 y
 
 y
 
 y
 
 
 y 
Results
Thanks