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Introduction to MATLAB
for Biomedical Engineering
BME 1008 Introduction to Biomedical Engineering
FIU, Spring 2015
Instructor (Matlab)
Adam Kapela, Ph.D.
[email protected]
Learning Assistants (LA's)
Karla Montejo [email protected]
Elizabeth Solis [email protected]
Teresa Milan [email protected]
Christina Moya [email protected]
Computing in BME
Data analysis and visualization, parameter estimation
Signal processing, feature extraction
ECG
Image Reconstruction and Analysis
MRI
Modeling and Simulations
And more ...
Heart
Model
MATLAB (matrix laboratory)
Programming language and software environment
for technical and scientific computing.
MATLAB allows
matrix manipulations,
plotting of functions and data,
implementation of algorithms,
creation of user interfaces.
Matlab toolboxes
Statistics Toolbox
Signal Processing Toolbox
Image Processing Toolbox
Bioinformatics Toolbox
SimBiology
Data Acquisition Toolbox (laboratory exercise)
and more ...
Schedule
Week 2
Introduction to Matlab, Basic commands and functions
Week 3
Reading/writing data files, Visualizing data
Week 4
Basics of Analog-to-Digital conversion,
Week 5
National Instruments AD/DA converter
Week 7
Data Aqusition Toolbox
Test (21% of total grade)
Access to Matlab
Developed and sold by MathWorks, MA
FIU
Connect from anywhere through Citrix
elabs.fiu.edu (elabs.fiu.edu/Citrix/GetStarted.pdf )
EIC apps (eic.fiu.edu/apps/)
Computer labs (Engineering Center)
HPC Panther Cluster (research only)
Student licence: $50, MATLAB Suite $99, Add-On
Products $29
Free alternatives to Matlab
SciLab
excellent for standard computations and plotting
easy installation
not fully compatible with Matlab
less toolboxes
GNU Octave
syntax mostly compatible with Matlab
hard installation, GUI unofficial
Matlab environment
Workspace
window
>> command prompt
Command window
Command
history
Elements of Matlab language
>> % This is Matlab Comment
>>
Basic operations:
>> 3.67 + 4 - 3.67^3
ans =
-41.7609
+ addition, - subtraction, * multiplication, / division, ^ power
>> ((3.67+7)*10)^2 %()operator precedence
Variable - a named storage for information (numbers
or text)
variable name must start with a letter
>> x = 3.67
% = assignment operator
>> x + 4 - x^3
>> ((x+7)*10)^2
ans =
-41.7609
ans - automatic variable that stores most recent result
variables can store text strings
>> s1 = 'time'
%string variable
s1 =
time
>> s2 = '(seconds)'
s2 =
(second)
>> s = [s1 s2]
%string concatenation
s=
time (seconds)
Variable names are case sensitive
>> a1 = 0.00025
>> A1 = 10^5
>> b = A1 * a1
% = 2.5e-4
% = 1e5
% = 25
"Scientific notation" - "10^" replaced by "e"
10^7 = 1e7
2.15*10^-3 = 2.15e-3.
"e" does not mean the Euler's number
exp(1) = 2.71... (Euler's number)
Scalar
>> x = 5
Vector (1 dimensional array)
>> vr = [10 20 30] % row vector
vr =
10 20 30
>> vc = [-5 10 1/5]' % ' transpose operator
vc =
% column vector
-5
10
0.2
Vectors can store 1-dimesional signals
Matrix (2 dimensional array)
>> A=[1 2 3; 4 5 6] %2-by-3 matrix
A =
1
2
3
4
5
6
>> B=[1
B =
1
3
5
2; 3 4; 5 6] %3-by-2 matrix
2
4
6
Matrices can store images
Managing commands
>> whos %List current variables
>> clear
%Removes items from workspace
and memory
>> clc %clears command window, keeps variables
>> a = 2; b = 2^16;
%semicolon (;) terminates a
command and prevents
displaying result
>> help %lists all primary help topics and toolboxes
>> help sqrt %square root
>> lookfor 'square root' %search matlab directories
%for keywords
When looking for help, try also
offline and online Matlab documentation
Just Google "Matlab square root" !
Basic functions
>> A = [0 1 -2 20 30 20];
%vector
>> mean(A) %mean (average)
>> ans =
11.5
>> maxA = max(A) %maximum value
>> maxA =
30
>> minA = min(A) %minimum value
>> minA =
-2
Mean from a matrix
>> A = [0 1 -2; 20 30 20] %2-by-3 matrix
A =
0
1
-2
20
30
20
>> mean(A) %mean(A,1) - mean along dimension 1
ans =
10.0000
15.5000
9.0000
>> M2 = mean(A,2) %mean along dimension 2
M2 =
-0.3333
23.3333
Max from a matrix
>> A = [0 1 -2; 20 30 20] %2-by-3 matrix
A =
0
1
-2
20
30
20
>> max(A) %or max(A,[],1) - max along dimension 1
ans =
20
30
20
>> M2 = max(A,[],2) %max along dimension 2
M2 =
1
30
>> A = [0 1 -2 20 30 20];
%vector
>> std(A) %standard deviation
ans =
13.5
>> B = [-2 5 4i 3+4i] %vector with complex
numbers
>> m = abs(B) %absolute value (magnitude)
m =
2
5
4
5