This activity enables students to apply concepts in systems engineering, measurement systems, sensor integration, signal conditioning, calibration, testing, and troubleshooting through the design of a Portable Environmental Monitoring Instrument. MATLAB and Simulink are used to model the instrument, simulate sensor behaviour, perform calibration and error analysis, evaluate system performance, and compare simulation results with hardware measurements. The activity develops higher-order skills such as critical thinking, problem solving, data analysis, system modelling, and the integration of theory and practice. It also strengthens technical report writing, oral presentation, teamwork, project management, prototype development, equipment operation, testing, and troubleshooting skills.

This activity is designed for third-year undergraduate students enrolled in different areas of engineering; namely, instrumentation, electronics, automation, mechatronics. It is implemented as a six- week long group project (typically 2-4 students per group) integrated across both lectures and laboratory sessions over approximately 10-12 weeks, requiring 40-60 hours of total student effort. Students use MATLAB and Simulink to develop a digital model of a Portable Environmental Monitoring Instrument. They perform sensor calibration, analyze measurement errors, investigate noise and filtering effects, evaluate dynamic response characteristics, and compare simulation results with data collected from a physical prototype. Students should possess basic MATLAB skills, including creating and running scripts, manipulating arrays, plotting data, performing simple calculations, and using basic functions such as plot, mean, polyfit, and polyval, while introductory Simulink knowledge is beneficial but not essential. Prior knowledge of basic electronics, measurement systems, sensors and transducers, circuit fundamentals, algebra, graph interpretation, and calibration concepts is assumed. The activity functions as a capstone-style design experience that reinforces systems engineering, instrumentation, testing, troubleshooting, and technical communication concepts developed throughout the course. Because the methodology is based on the generic measurement-system architecture of sensing, signal conditioning, signal processing, and data presentation, it can be readily adapted to other engineering contexts such as process instrumentation, industrial automation, mechatronics, embedded systems, or electronic measurement courses with only minor modifications to the application domain and selected sensors.

The MATLAB component focuses on the modeling, simulation, calibration, and validation of the Portable Environmental Monitoring Instrument. Students use Simulink to create a system-level model representing the sensor, signal-conditioning, processing, and display subsystems, and use MATLAB to analyze sensor data, develop calibration curves, calculate measurement errors, evaluate noise and filtering effects, and assess instrument performance. Simulation results are then compared with data collected from the physical prototype to support testing, troubleshooting, and design improvements. MATLAB and Simulink are used because they provide an integrated environment for modeling, simulation, visualization, and data analysis, although similar activities could be performed using software such as GNU Octave, or LabVIEW.
Environmental Monitoring Instrument Student Guide (Microsoft Word 2007 (.docx) 24kB Sep28 26)

This activity is most successful when MATLAB and Simulink are introduced as engineering design tools, not just software for plotting graphs. Instructors should emphasize that students are creating a digital model of their instrument to support design, calibration, testing, and troubleshooting. Encourage students to begin with a simple Simulink measurement chain (Sensor → Signal Conditioning → Processing → Display) before adding features such as noise, filters, and alarms. Common areas of confusion include distinguishing sensor noise from measurement error and understanding that calibration involves correcting measurements against a reference. Provide a starter script and review basic MATLAB functions such as plot(), polyfit(), polyval(), and movmean(). Reinforce the importance of comparing simulation results with prototype measurements and discussing differences between the two.

Students are evaluated through the Simulink model, MATLAB analysis scripts, calibration results, and performance evaluation report. Students demonstrate attainment of the learning outcomes by developing a valid measurement-system model, performing sensor calibration, calculating and interpreting measurement errors, analyzing noise and filtering effects, evaluating system response characteristics, and comparing simulation results with prototype data. Success is determined by the student's ability to use MATLAB and Simulink to model, analyze, validate, and communicate the performance of the environmental monitoring instrument using appropriate graphs, calculations, and engineering interpretations.

Online Resources

MATLAB Onramp
URL: https://matlabacademy.mathworks.com/details/matlab-onramp/gettingstarted
A free, self-paced introductory course from MathWorks that teaches the fundamentals of MATLAB, including variables, scripts, plotting, and data analysis. This resource is particularly useful for students who have little or no prior MATLAB experience.

Simulink Onramp
URL: https://matlabacademy.mathworks.com/details/simulink-onramp/simulink
A free introductory course that introduces the fundamentals of Simulink, including creating models, connecting blocks, running simulations, and analyzing results. It directly supports the development of the project's measurement-system model.

MathWorks Documentation: Simulink Fundamentals
URL: https://www.mathworks.com/help/simulink/
Comprehensive documentation covering Simulink modeling, simulation, block libraries, visualization tools, and model development workflows. Useful for students creating system-level models of their instruments.

MathWorks Documentation: Data Analysis and Visualization
URL: https://www.mathworks.com/help/matlab/data-analysis.html
Provides tutorials and examples for plotting, curve fitting, filtering, statistical analysis, and data visualization, supporting calibration and performance evaluation activities.

MathWorks Documentation: Transfer Functions and Dynamic System Modeling
URL: https://www.mathworks.com/help/control/
Useful for students modeling sensor response characteristics, evaluating system dynamics, and understanding first-order and higher-order measurement-system behavior.

Signal Processing Toolbox Documentation
URL: https://www.mathworks.com/help/signal/
Provides examples of filtering, noise reduction, signal conditioning, and frequency-domain analysis that are directly applicable to instrumentation and measurement systems.

Print Resources

Bentley, J. P. (2005). Principles of measurement systems (4th ed.). Pearson Education.
This text provides the theoretical foundation for measurement systems, calibration, measurement error, noise analysis, reliability, sensors, signal conditioning, and performance evaluation. It serves as the primary reference for the measurement-system concepts used throughout the activity.

Eren, H. (2004). Electronic portable instruments: Design and applications. CRC Press.
This book covers instrument architectures, sensor integration, signal processing, power considerations, and portable electronic instrument design. It supports the hardware-development aspects of the project.

Kossiakoff, A., Sweet, W. N., Seymour, S. J., & Biemer, S. M. (2011). Systems engineering principles and practice (2nd ed.). Wiley.
Provides guidance on systems thinking, requirements development, system architecture, interfaces, validation, and life-cycle considerations that support the systems-engineering aspects of the project.