ISAT 300: Lab Manual

This website contains the laboratory manual and resources for ISAT 300: Applied Computing, Instrumentation, and Measurements taught by Tobias Gerken at James Madison Unversity.

This is an evolving document that draws on many resources and the efforts of previous instructors of this course and course predecessors acknowledged in the descriptions of individual labs.

Motivation

We live in a data-rich world. Advances in sensors, measurement systems, and computing provide humans with an unprecedented amount of data about the world around us. To make this data useful for making decisions, big and small, we need to be able to collect, process, and analyze this data in a responsible way. All data is incomplete. It has errors, biases, and uncertainties. This means that we need to be careful and make sure that we understand what the data tells us.

The laboratory sequence is designed to accompany lecture content and concepts centered around conducting measurements and analyzing data to make it useful for data-informed decision making, including understanding what measurements we collect can and crucially cannot tell us.

The lab is designed as a hands-on experience; with most labs using a Raspberry Pi microcomputer to build and program a measurement system to explore fundamental concepts around measurements. You will then use computational tools including the python programming language to analyze your data and communicate your results in lab reports.

This mimics the basic workflow of using measurements for data-informed decision making:

Measurements for Decision Making Workflow

Course Learning Goals

By the end of this course, our goal will be for you to be able to:

  • Explain the fundamental nature of measurement in the practice of science and technology.
  • Apply computing technology to the collection, storage, processing, and presentation of data.
  • Discuss how and why measurements are taken and why different measurement systems are appropriate for different applications.
  • Recognize the uncertainty involved in all measurements.
  • Compare computation methods for data analysis and data visualization including statistical methods used for analysis.
  • Analyze measurement systems, recognizing possible capabilities and limitations of different measurement systems.
  • Create networks using wireless and wireline technology for measurement systems to augment data acquisition, transmission, analysis, integration, and presentation to peers and to the public.
  • Develop and conduct experiments, test hypotheses, analyze and interpret data and use scientific judgment to draw conclusions.
  • Work effectively in multidisciplinary teams to design, create and utilize measurement systems in the context of one or more projects that address community needs.
  • Analyze, present, and defend the results and conclusions from projects that involve applied computing, instrumentation, and measurement.