BIOMEDE 2700
Transcript Abbreviation:
Num Sim in BME
Course Description:
Focuses on the application of computer-based numerical and graphical display skills for solving problems relevant to biomedical engineering.
Course Levels:
Undergraduate (1000-5000 level)
Designation:
Required
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
2.00
Max Credit Hours
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 2000, and enrollment in BiomedE major, or permission of instructor
Concur: Math 2174
Concur: Math 2174
Electronically Enforced:
No
Exclusions
(N/A)
Course Goals / Objectives:
Students will be confident with the implementation of de-novo code and numerical methods to solve BME problems.
Students will identify their own preconceived limitations on coding and learn how to tackle them.
Students will understand the utility of coding as a necessary and important skill for problem solving.
Students will recognize that real-world BME problems are open-ended and complex.
Students will be able to develop and execute MATLAB programs to graph and visualize biologically relevant data (ABET 2).
Students will be able to develop and execute MATLAB programs to find numerical solutions for sets of linear and non-linear algebraic equations describing biological phenomena.
Students will be able to develop and execute MATLAB programs to find numerical solutions for differential equations describing biological phenomena (ABET 1, B)
Students will be able to perform parameter estimation using MATLAB to approximate equations describing biological phenomena (ABET 1, B)
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Fundamentals: Modeling and Simulation, Extensive Properties, Intensive Properties: Accounting and Conservation, Equations, Review of MATLAB Environment (calculator, scripts/functions, graphics), Vectors (vector manipulation, force representation, bio | 4.0 | 0.0 | 3.5 | 3.5 |
| Graphics and Visualization: Point Plots of Experimental Data, Line Plots of ECG Data, Curve Fits of Stress Relaxation Data, Image Processing (digital image fundamentals, histograms) | 2.0 | 0.0 | 2 | 1.5 |
| Algebraic Balance Equations: Systems of Linear and Non-linear Equations (direct methods, iterative methods) | 2.0 | 0.0 | 2 | 1.5 |
| Differential Balance Equations: Differential Equations (Hodgkin Huxley equation, cell differentiation, constitutive equations of viscoelastic tissues) | 2.0 | 0.0 | 2 | 1.5 |
| Numerical Data Analysis: Numerical Integration & Differentiation, Interpolation & Extrapolation, Least Squares Regression, Parameter Estimation (pharmacokinetic model fitting) | 4.0 | 0.0 | 3.5 | 3.5 |
| Total | 14 | 0 | 13 | 11.5 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Lab
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Lab/Homework Assignments | 30% |
| Exams | 40% |
| Final Project | 25% |
| Quizzes/Attendance | 5% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Numerical Methods in Biomedical Engineering, Academic Press | Dunn, S.M., Constantinides, A., Moghe, P.V. | 2006 |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Some contribution (1-2 hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Some contribution (1-2 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
Embedded Literacies Info:
Data Analysis - Quantitative
1.1A Explain basic concepts of statistics and probability1.2A Apply methods needed to analyze and critically evaluate statistical arguments1.3A Recognize the importance of statistical ideas
Data Analysis - Qualitative
1.1B Explain the utility of different approaches to qualitative data analysis1.2B Apply key methods and tools in qualitative data analysis1.3B Interpret the results of qualitative data analysis to answer research questions
Attachments
(N/A)
Additional Notes or Comments
(N/A)