1. Es 314 Programming, simulation and modeling lecture: M W 3 – 4:50 PM 2008, Salazar Hall

2. Course web site http://ravi.cs.sonoma.edu/es314fa10 this year’s web site is not functional yet, but take a look at last year’s web site: http://ravi.cs.sonoma.edu/es314fa09http://ravi.cs.sonoma.edu/es314fa09

3. 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.

4. 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

5. Text book Kaplan, Daniel T. Introduction to Scientific Computation and Programming, Brooks/Cole-Thomson Learning, 2004. http://www.macalester.edu/~kaplan/ ScientificProgramming/index.html We will cover most of Chapters 1 to 12. For simulation and modeling, we will use other sources.

6. 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.

7. 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.

8. 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.

9. 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

10. 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

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

12. 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

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

14. tutorials http://www.mathworks.com/ has links to tutorials, video clips on various topicshttp://www.mathworks.com/ http://www.youtube.com/user/MATLAB tutorials from educators http://www.math.utah.edu/lab/ms/matlab/matlab.html http://amath.colorado.edu/computing/Matlab/Tutorial/ http://www.math.utah.edu/lab/ms/matlab/matlab.html numerous other tutorials (engineering design)

15. 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 registrar’s office. You can find out from the web page: http://www.sonoma.edu/ university/classsched/finals_sched.pdfhttp://www.sonoma.edu/ university/classsched/finals_sched.pdf

16. 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.

17. 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? 132 Assume all outgoing arcs have the same probability Application: assuming that the server has probability = 0.55 of winning a game, what is the average length of a game in tennis?

18. signal generation and display – simple example

20. 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

21. Image processing original image restored image deblurring

22. Image merging input output

23. Image merging – a more complex example Input images output Example taken from http://www.graficaobscura.com/merge/index.htmlhttp://www.graficaobscura.com/merge/index.html

24. 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.

25. 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.

26. contour slices Example taken from: http://www.mathworks.com/access/helpdesk/help/techdoc/index.html?/access/helpdesk/help/te chdoc/matlab.html&http://www.cs.dartmouth.edu/farid/teaching/cs136/summer.08/ http://www.mathworks.com/access/helpdesk/help/techdoc/index.html?/access/helpdesk/help/te chdoc/matlab.html&http://www.cs.dartmouth.edu/farid/teaching/cs136/summer.08/

28. Matlab – working windows

29. Matlab – Introduction (Ch 2) Variables:

30. 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 can’t be used as variable names. (Key words list is in the next slide.)

31. Matlab Special Variables ans Default variable name for results pi: Value of π >> 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

32. 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.^) a^b stands for a b

33. Other MATLAB symbols >> 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

34. Matlab – Introduction (Ch 2) Exercise 2.1: Evaluate the expression 3 – 5 + 4/6 – 8 *4^2 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.

35. some helpful commands >> whos Lists all the variables currently active in environment >> lookfor gives all sentences containing in the manual. Use up arrow to repeat the previous command. >> help

36. Chapter 3 – numbers, string, booleans integer: MATLAB 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: >> x = int16(325); If the number being converted to an integer has a fractional part, MATLAB rounds to the nearest integer.

37. 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: >> x = 325.499; >> int16(x) ans = 325 >> x = x +.001; >> int16(x) ans = 326 Built-in functions that convert to int Other related functions: floor, ceil

38. long floating-point format >> format long >> x = 1.5^2.3; >> x x = 2.54103060477792 >> format short >> x x = 2.5410 >> x = 2.564593653; >> x x = 2.5646 >>

39. Complex numbers

40. 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. >> greeting = ‘hello’

41. String operations >> length(word) ans = 5 >> strcmp(word, ‘hello!’) ans = 0 1 if the Boolean expression is true, 0 else. Strcmp compares two strings.