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Real-Time Embedded Systems Concepts and Practices

Course Overview

  • Institution: University of Colorado Boulder
  • Course code: Real-Time Embedded Systems 1
  • Track: Real-Time and Cyber-Physical Systems
  • Tier: A
  • Role: Alternative
  • Level: Not standardized by provider (use prerequisites)
  • Last reviewed: 2026-07-28

University of Colorado Boulder's Real-Time Embedded Systems Concepts and Practices establishes a practical real-time embedded entry through Raspberry Pi, Linux, videos, labs, and code, with explicit hardware and potentially paid access.

Why choose this course

Alternative course. A reliable option that can serve as a main course or strong alternative.

Before you start

  • Recommended foundation: Embedded Systems
  • Recommended foundation: Signals and Systems

Verifiable learning outcomes

  • Explain the core models in Real-Time and Cyber-Physical Systems, including their assumptions and limits
  • Solve representative derivations and problems, checking units, limiting cases, or numerical results
  • Complete a reproducible experiment or implementation with raw data, parameters, versions, and verification

Workload and pacing

11 weeks at 9 hours/week. This maintainer planning estimate is derived from course role and the density of public practice and labs; it is not a provider workload promise. Pilot two weeks while logging instruction, practice, lab, and review time, then adjust the remaining plan when actual effort differs by more than 25%.

Safety level

Low energy. Keep work isolated, current-limited, and low energy; verify ratings, grounding, short-circuit risk, and emergency shutdown before power-up.

Course Resources

Software, hardware, and cost

Software

  • Maintainer-suggested open-source/free verification path: Zephyr or FreeRTOS source, GCC or LLVM, CMake, GDB, and Renode or QEMU
  • The resource inventory lists public code coverage; pin interpreter, dependencies, toolchain, datasets, and PDK versions where applicable

Hardware

  • The resource inventory lists lab coverage; prefer borrowing or sharing the following equipment: a course-supported real-time control board, USB debugger, logic analyzer, and low-voltage sensors/actuators. Verify ratings, authorization, and safety conditions only after the provider lab manual explicitly calls for them

Cost note

The suggested software stack is available open source or free; this is not a provider requirement or bill of materials. The actual boards, components, fabrication, and instruments—and their costs—depend on the provider lab manual, region, and local availability; prefer simulation, borrowing, or sharing before purchase.

Public resource coverage

Resource type Completeness
Video Complete
Notes Partial
Practice Complete
Labs Complete
Exams Partial
Code Complete

Resources and access

Resource Access License Status Verified
Course home Registration required Coursera Terms of Use Listed by official page 2026-07-28
Introduction to Embedded Systems Software and Development Environments Registration required Coursera Terms of Use Listed by official page 2026-07-28

“Listed by official page” means the link was discovered on a successfully fetched official source on the verification date; it does not guarantee that every region or account can open the target directly. Access does not grant redistribution rights. Re-check the provider page, target link, and third-party notices before downloading, adapting, or publishing material.

Practice and Verification

Practice loop

Real-Time Embedded Systems Concepts and Practices · University of Colorado Boulder Real-Time Embedded Systems 1: Real-Time Closed-Loop Deadline Stress Test

This is a maintainer-suggested self-study project for Real-Time Embedded Systems Concepts and Practices · University of Colorado Boulder Real-Time Embedded Systems 1, not an official course assignment. Build a discrete-event scheduler and simulated plant for Real-Time and Cyber-Physical Systems, quantifying how jitter, missed deadlines, and sensor loss affect closed-loop safety margin.

Origin: Maintainer-suggested project

Deliverables

  • A task set with period, deadline, and WCET assumptions, scheduling policy, and plant model
  • An executable scheduler, closed-loop simulation, fault injector, and monitoring assertions
  • Raw response-time, jitter, miss, and state trajectories across multiple loads
  • A report defining the schedulability boundary, control degradation, and safe degraded state

Verification

  • Produce zero deadline misses at nominal load and keep analytic response-time bound within 10% of the simulated worst case
  • Cover zero load, near-100% utilization, burst blocking, and clock-drift boundaries
  • Cross-check schedulability with a second analysis or exhaustive enumeration over a short hyperperiod
  • Increase WCET until the first miss and report miss rate and closed-loop error growth

Reproducibility

  • Commit scheduler, plant, fault scenarios, assertions, and analysis sources
  • Pin event ordering, random seeds, time units, solver, and dependency versions
  • Preserve raw event and state logs and automatically generated timelines and report

Safety boundary: Simulation only — Inject deadlines and faults only into a simulated plant; do not connect unvalidated scheduling or degradation logic to real machinery, vehicles, medical, or power systems.

Risks, gaps, and boundaries

The Raspberry Pi and Linux hardware requirement is explicit, and Coursera access may require payment.

Completion evidence

  • Weekly learning log with time, questions, corrected errors, decisions, next steps, and links to that week's reproducible artifacts
  • Design-review package with requirements and constraints, trade-offs, editable sources, applicable ERC/DRC/timing/stability checks, exports, and a reproduction test
  • Code repository with pinned dependencies and toolchain, a minimal run command, tests or waveform/benchmark checks, expected output, and license notes
  • Experiment package with schematic/setup, calibration record, raw data, uncertainty, safety checks, failed runs, and steps to rebuild plots from raw data