Es 314 Programming, simulation and modeling

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Es 314Programming, simulation and modeling

• lecture
• M W 3 450 PM
• 2008, Salazar Hall

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Course web site

• http//ravi.cs.sonoma.edu/es3
  14fa10
• this years web site is not functional yet, but
  take a look at last years web site
  http//ravi.cs.sonoma.edu/es314fa09

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Catalog Description of the course Lecture 4
hours laboratory 0 hours. Pointers and dynamic
allocation of storage linked lists an
introduction to the object oriented programming
(OOP) paradigm classes and objects
encapsulation member variables and member
functions. Static arrays, dynamic arrays, stacks
and queues, linked lists, hashing. System
modeling techniques and applications such as
generation of noise (random numbers) and
correlated signal with different pdfs,
measurement of statistical parameters like
moments, queuing systems and system simulation.
Prerequisite CS 115 Programming I.
Co-requisites MATH 345 Probability Theory and
ES 220 Electric Circuits, or consent of
instructor.
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• Goals of the course To introduce software
  design for engineering applications through
  MATLAB programming, computational modeling of
  physical systems and software simulation of
  simple physical systems.
• Specifically, the following topics will be
  covered
• MATLAB programming iteration, library and
  user-defined functions, scripts, structured data
  and objects, image and audio files, plotting and
  visualization, recursion, project design and
  development.
• performing statistical analyses of data
• fundamental algorithms for sorting, searching,
  solving system of equations etc.
• computational modeling
• simulation of physical systems and models

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Text bookKaplan, Daniel T. Introduction to
Scientific Computation and Programming,
Brooks/Cole-Thomson Learning, 2004.

http//www.macalester.edu/kaplan/ScientificProgra
mming/index.html We will cover most of
Chapters 1 to 12. For simulation and modeling,
we will use other sources.
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• course overview
• Matlab programming
• simulation and modeling
• case studies
• database, graphics and plotting, audio, image
  processing, modeling of biosignals etc.
• new this semester final project each student
  will choose one problem to work on.

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• course overview
• Matlab programming
• simulation and modeling
• case studies
• database, graphics and plotting, user interface
  design, stochastic modeling, queuing and network
  models, audio signal processing, image and video
  processing, modeling of biosignals etc.

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• Matlab programming language
• most widely used by (Electrical) engineers
• other similar ones mathematica, maple, mathcad,
  macsyma
• vector and matrix centered. (Name comes from
  MATrix LABoratory). Very high level operations
  for processing vectors and matrices.
• extensive support for mathematical operations,
  plotting, visualization, user interface design
• library functions for mathematical operations
  (solving equations etc.)
• toolboxes signal processing, wavelets etc.

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Matlab programming language

• Why useful for engineering applications
• rich numeric/image library functions
• very useful for displaying, visualizing data
• high-level focus on algorithm structure, not on
  low level details (especially for vector, matrix
  )
• Much smaller code size compared to others
• allows quick prototype development
• interfacing with other languages and systems

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• User interface
• interactive mode
• result appears immediately after the command is
  entered
• interpreted language
• scripts for sequence of operations, programs
• save scripts as files with .m extension

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• Main features of Matlab
• simple data types
• numbers, characters, string, boolean
• structured types
• vector, matrix, indexing, mixed data types
• files and scripts

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• Main features of Matlab
• functions
• user defined functions
• more advanced support for functions
• conditionals
• if statement, if then .. else, switch
  statement
• loops
• for loop, nested loop, loop termination,
  conditional loop

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• Main features of Matlab
• scope
• environment and scope
• resolving name conflicts
• events
• reactive program
• user interface design

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• tutorials
• http//www.mathworks.com/ has links to
  tutorials, video clips on various topics
• http//www.youtube.com/user/MATLAB
• tutorials from educators
• http//www.math.utah.edu/lab/ms/matlab/matlab.htm
  l
• http//amath.colorado.edu/computing/Matlab/Tutori
  al/
• http//www.math.utah.edu/lab/ms/matlab/matlab.htm
  l
• numerous other tutorials (engineering design)

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Grading short quizzes 5 to 10 points The
quizzes will be in-class for a duration to 10 to
15 minutes, one for each class. programming
assignments and projects 40 points Most of
the assignments will involve problem solving and
implementing the solution using MATLAB. There
will be a final project chosen by each student
individually. mid-semester tests (2) 20 to 25
points in-class, may or may not be open book
(depending on your choice). final examination
20 to 25 points This exam will be in-class and
comprehensive. It will take place at the time
scheduled by registrars office. You can find out
from the web page http//www.sonoma.edu/
university/classsched/finals_sched.pdf
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Stochastic models example What is the
probability that a web page is visited? Model
each web page is represented by a node and each
link from page A to page B is an edge in the
graph. Assume a visitor to a page will randomly
click on one of the links on the page. Also
assume that one starts with probability 1/n at
any page (where n total number of pages). This
model is called a Markov chain.
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Markov chain simulation Shown below is a
simple Markov chain. What is the average number
of steps it takes to move from state 1 to state 3?
Assume all outgoing arcs have the same probability
1
3
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Application assuming that the server has
probability 0.55 of winning a game, what is the
average length of a game in tennis?
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signal generation and display simple example
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signal generation and display simple example
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Image models 2-d image

• Digital image is represented by a collection of
  pixels. Each pixel has a color value represented
  by 32 bits. (R, G, B, A) values.
• Digital images can be processed in various ways
• compression
• restoration, de-blurring
• enhancement, noise reduction

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Image processing

• original image
  restored image

deblurring
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Image merging
input
output
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Image merging a more complex example
Input images
output
Example taken from http//www.graficaobscura.com/m
erge/index.html
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• Volume Data Representation and Visualization
• Typical scalar volume data is composed of a 3-D
  array of data and three coordinate arrays of the
  same dimensions. The coordinate arrays specify
  the x-, y-, and z-coordinates for each data
  point.
• For example, flow data might have coordinate
  units of inches and data units of psi.
• A number of MATLAB functions are useful for
  visualizing scalar data
• Slice planes provide a way to explore the
  distribution of data values within the volume by
  mapping values to colors.
• You can orient slice planes at arbitrary angles,
  as well as use nonplanar slices. You can specify
  the data used to color isosurfaces, enabling you
  to display different information in color and
  surface shape
• Contour slices are contour plots drawn at
  specific coordinates within the volume. Contour
  plots enable you to see where in a given plane
  the data values are equal.

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MRI Data Visualization

• MRI data typically contains a number of slice
  planes taken through a volume, such as the human
  body.
• MRI data formats that can be accessed directly
  through Matlab
• A series of 2-D images representing slices
  through the head
• 2-D and 3-D contour slices taken at arbitrary
  locations within the data
• An isosurface with isocaps showing a cross
  section of the interior.

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contour slices

Example taken from http//www.mathworks.com/acces
s/helpdesk/help/techdoc/index.html?/access/helpdes
k/help/techdoc/matlab.htmlhttp//www.cs.dartmouth
.edu/farid/teaching/cs136/summer.08/
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Matlab working windows
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• Matlab Introduction (Ch 2)
• Variables

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Variable definition

• need not be declared
• Variable names can contain up to 63 characters
• Variable names must start with a letter followed
  by letters, digits, and underscores.
• Variable names are case sensitive.
• Key words cant be used as variable names.
• (Key words list is in the next slide.)

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• Matlab Special Variables
• ans Default variable name for results
• pi Value of p
• gtgt pi
• ans
• 3.14159265358979
• eps smallest incremental number
• inf Infinity
• NaN Not a number e.g. 0/0
• realmin The smallest usable positive real
  number
• realmax The largest usable positive real number

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• Matlab Introduction (Ch 2)
• key words
• if, else, end, for, while, break, switch, case,
  try, catch, return, global, function, persistent
  etc.
• arithmetic operations
• (or .) latter used for
  component-wise in vector
• / (or ./)
• \ (or .\)
• (or .) ab stands for ab

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Other MATLAB symbols

• gtgt prompt
• . . . continue statement on next line
• , separate statements and data
• start comment which ends at end of line
• (1) suppress output
• (2) used as a row separator in a matrix
• specify range

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• Matlab Introduction (Ch 2)
• Exercise 2.1 Evaluate the expression
• 3 5 4/6 8 42
• Exercise 2.2 Write (3 (5 2 8))/4 in
  functional style using plus, minus, times and
  rdivide.
• a b is written as plus (a, b) in functional
  style.

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some helpful commands

• gtgt whos
• Lists all the variables currently active in
  environment
• gtgt lookfor ltwordgt gives all sentences
  containing ltwordgt in the manual.
• Use up arrow to repeat the previous command.
• gtgt help

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Chapter 3 numbers, string, booleans integer M
ATLAB stores numeric data as double-precision
floating point (double) by default. To store data
as an integer, you need to convert from double to
the desired integer type. Example To store
 325 as a 16-bit signed integer assigned to
variable x gtgt x int16(325) If the number
being converted to an integer has a fractional
part, MATLAB rounds to the nearest integer.
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• If the fractional part is exactly 0.5, then from
  the two equally nearby integers, MATLAB chooses
  the one for which the absolute value is larger in
  magnitude
• gtgt x 325.499
• gtgt int16(x)
• ans 325
• gtgt x x .001
• gtgt int16(x)
• ans 326

Built-in functions that convert to int

• Other related functions floor, ceil

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long floating-point format gtgt format long gtgt x
1.52.3 gtgt x x 2.54103060477792 gtgt
format short gtgt x x 2.5410 gtgt x
2.564593653 gtgt x x 2.5646 gtgt
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Complex numbers
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Strings Character alphabetical upper/lower
(a .. z, A .. Z)
digits 0, 1, , 9 special
characters - , etc.
control characters - \n (end of line) etc. Each
character is encoded by 8 bits (ASCII) or 16 bits
(unicode) Unicode allows encoding of alphabets
from many languages such as Hungarian, Chinese,
Swahili etc. String - sequence of
characters. gtgt greeting hello
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String operations gtgt length(word) ans 5 gtgt
strcmp(word, hello!) ans 0
1 if the Boolean expression is true, 0
else. Strcmp compares two strings.