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

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

Late Reconition in Germany

Ohm‘s Experiment

Silk Thread

Magnetic Needle

Magnifying Lense

Mercury Pads for Connecting Copper Wires

Copper

Bismuth

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

Cal 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

Ex Copper Wire

1 2 3 4 5 6 7 8

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

Data calculated from model

Ohm‘s Law

Scientific discovery in physics via

- empirical measurements followed by

- mathematical modeling

- no explanation of physical processes

Impact on Modern Engineering Sciences

Black-Box ModelModern Engineering Science

Inpu

tO

utput observablemeasurable

Abstract Input-Output Description

Example: Compressed Digital Video

Modeling Human Visual Perception

Video

Visual Quality

Data StreamEncoder

(Compression)

Encoder

(Compression)

Decoder

Control

Subjective Testing

Test persons judge visual quality of video entries in - Vector

Subjective testing

Main - Tempete - CIF

1

2

3

4

5

AVC_768 AVC_384 MP4_768 AVC_192 MP4_384 MP4_192 AVC_096 MP4_096

Black-Box Modeling

Find a (linear) model for input-output relation

measurable features in video u – variables

observable video quality y – variable

Vid

eo I

nput

Subj. Q

uality observablemeasurbale

Blur

Blocking

Noise

HVS

Black-Box Model for subjective image quality

Video

[Blur, Blocking, Detail, Motion, …,Noise] [Visual Quality]

Subjective testObjective Measurement

Video1

Video2

Video3

Video4

:

VideoN

Blu

r

Blo

ckin

g

Det

ail

Mo

tio

n

No

ise

PLSR, PCR …

Regression- techniques

(PCR ≈ SVD)

x x x x L xx x x x L xx x x x L xx x x x L xM M M M Mx x x x L x

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥

y

y

y

y

y

Results

Thanks