8/11/2019 Examination Embedded Intelligent Systems Winter Semester 2012

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Fachhochschule Frankfurt a.M. Prof. Dr. Peter Nauth

Fachbereich 2 Seite 1 von 1 Klausur Embedded Intelligent Systems

Examination Embedded Intelligent Systems Credits:

Winter Semester 2012 Grade:

Name, Surname: _____________________________ Matrikelnr.: __________________

Signature: ____________________________________________________________________________________________________________

Supporting Material:2 pages (1 piece of paper) with handwritten notes, 1 calculator

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1. Question (15 CP.)

a) Describe the architecture of an intelligent bar code reader.

b) What are the formulars to calculate the bar width independently of speed?

2. Question (10 CP.)

Describe the V-model used in management of embedded systems development projects.

3. Question (25 CP.)

The analog output Uana [unit: V] of a given proximity sensor is a function of distance d [unit:

cm] as follows: Uana(d) = 3 Vcm/ (d + 2cm) + 0.5V. Uana(d) is digitized by the ADC channel

6 of a controller ATMega 128 (Robonova) with a sampling width of 12 bit and a the sampling

range of 2V 4V.

a) Derive the formula for calculating the distance d from the digitized data Udig.

b) Write a Robobasic program that acquires the sensor output via AD6 and calculates the

distance d from the digitized data Udig. If the distance is less than 2 cm, servo motor 4

is set to the angle -90.

4. Question (25 CP)a) What are the most significant processing steps in pattern recognition? Explain each

step briefly.

b) Describe the approach for segmenting images.

c) What are the advantages of transforming color images into HSI- space?

d) Calculate the base spatial frequency of the checker board of a length of 30 cm and 12

fields (6 black and 6 white) in 1 direction

e) What are the differences between the Baysian classifier and the Nearest Neighbor

Classifier regarding the discrimination functions and deciding for a class?

f) Which features and classification methods are used in speech recognition?

5. Question (25 CP)

An Image Processing System uses feature vectors x consisting of the features x1(Area) und x2

(Brightness). It differentiates the classes1(Large bright object) and 2(Small grey object).

During learning phase, 4 cases per class are used. The extracted features are as follows:

1streference object for class 1: x1= 80, x2= 120 for class 2 x1= 170, x2= 190

2nd

reference object for class 1 x1= 110, x2= 140 for class 2 x1= 130, x2= 160

3rd

reference object for class 1 x1= 60, x2= 90 for class 2 x1= 170, x2= 210

4th

reference object for class 1 x1= 70, x2= 100 for class 2 x1= 180, x2= 220

a) Calculate the class a Bayes classifier trained with the reference objects above would

assign an unknown object with the features x1= 120, x2= 150 to.b) Calculate the co-variance between feature 1 and feature 2 of class 1 and decide

whether it can be really neglected in the K1Matrix.