Online Education for Working Professionals
Focus on technologies used in digital and analog signal analyses.

Signal and Image Processing—Graduate Certificate

Signal and Image Processing

Applications range from medical imaging to autonomous vehicle technology.

Learn data manipulation techniques to improve signal or image fidelity. Understand the theory of probability and stochastic processes. Detect and estimate signals. Use computer programming in digital image processing. Autonomous navigation through sensors relies on process control and computer vision.

3 courses in 3 semesters.

Department Electrical and Computer Engineering
Admissions requirement Electrical or computer engineering, data science, or related degree.
Contact Paul L. Bergstrom
Length 3 courses in 2-3 semesters
Effort 3 hours per credit per week
Each course 3 credits
Total credits 9
Course type Online or on-campus
Modality Watch class recordings on demand
Cost Based on credits and course type
Already enrolled? Speak with your advisor.

Apply

Progress quickly with a compact curriculum.

Work with the program advisor to select courses that fit your interests and pre-requisite skills.

Take a 3 credit required course.

EE 5500 - Probability and Stochastic Processes

Theory of probability, random variables, and stochastic processes, with applications in electrical and computer engineering. Probability measure and probability spaces. Random variables, distributions, expectations. Random vectors and sequences. Stochastic processes, including Gaussian and Poisson processes. Stochastic processes in linear systems. Markov chains and related topics.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering, Electrical & Computer Engineer; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

Take 6 credits of elective courses.

EE 5300 - Mathematical and Computational Methods in Engineering

Overview of problem-solving tools and techniques in engineering, considered from both the analytical and computational point of view. Systems of linear equations, eigenvalue and eigenvector computations, boundary value and initial value problems, Fourier analysis, large-scale systems, optimization. Mathematical modeling and computar programming.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

EE 5521 - Detection & Estimation Theory

Detecting and estimating signals in the presence of noise. Optimal receiver design. Applications in communications, signal processing, and radar.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering
  • Pre-Requisite(s): EE 5500

EE 5522 - Digital Image Processing

Fundamentals of image processing are covered including image representation, geometric transformations, binary image processing, compression, space and frequency domain processing. Computer programming in MATLAB and Python required.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering

EE 5532 - Sensing and Processing for Robotics

Sensing modes, signal and image processing for industrial robotic automation processes. Emphasis placed on widely used sensors, including cameras and 3-D sensors for process control and computer vision for autonomous navigation.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): EE 5522

The minimum completion time is one semester.

Here are typical schedules.

Sample Plan 1

Fall

EE 5300
EE 5500
EE 5522

Sample Plan 2

Fall Spring

EE 5500
EE 5522

EE 5532

Interested in taking a single, online course? Enroll as a non-degree seeking student.

Upon completion of the Certificate the student should be able to:

  1. Demonstrate proper utilization of signal and image processing techniques.

Students receiving this certificate will have demonstrated the ability to solve open-ended problems in statistical signal and/or image processing from fundamental principles, and be able to apply their solution to real world problems.

Michigan Tech was founded in 1885.

The University is accredited by the Higher Learning Commission and widely respected by fast-paced industries, including automotive development, infrastructure, manufacturing, and aerospace. Michigan Tech graduates deliver on rapid innovation and front-line research, leaning into any challenge with confidence.

The College of Engineering fosters excellence in education and research.

We set out as the Michigan Mining School in 1885 to train mining engineers to better operate copper mines. Today, more than 60 percent of Michigan Tech students are enrolled in our 17 undergraduate and 29 graduate engineering programs across nine departments. Our students and curriculum embrace the spirit of hard work and fortitude our founders once had. Our online graduate courses are the same, robust classes taken by our doctorate and masters candidates, taught directly by highly regarded faculty, with outstanding support from staff. We invite working professionals to join these courses, bring their own experience and challenges as part of the discussion. Leverage the national reputation of Michigan Tech to advance your career in tech leadership.

Meet the online certified instructors.

Students have the flexibility to review class recordings later.

Jeffrey Burl

Jeffrey Burl

Associate Professor, Electrical and Computer Engineering

Teaching Statement

Dr. Burl teaches courses in control systems, digital and non-linear control, and probability and stochastic processes.

View Profile

Michael Roggemann

Michael Roggemann

Professor, Electrical and Computer Engineering

Teaching Statement

Dr. Roggemann teaches on sensing and processing in robotics, digital image processing, Fourier optics, and algorithms and optimizations.

View Profile

Timothy Schulz

Timothy Schulz

University Professor, Electrical and Computer Engineering

Teaching Statement

Dr. Schulz teaches courses in detection and estimation theory, math and computational methods in engineering, and electric circuits.

View Profile