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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