The technology we use, and even rely on, in our everyday lives—computers, radios, video, cameras, cell phones, driverless vehicles, drones—is enabled by signal and information processing, a branch of electrical engineering that models and analyzes data representations of physical events. Signal and information processing is at the heart of our modern world, powering today’s entertainment and tomorrow’s technology. It’s at the intersection of biotechnology and social interactions. It enhances our ability to communicate and share information, between humans, between humans and machines, or between machines. Signal and information processing is behind every aspect of our digital lives.

Over the past six decades, the types of signals and the functionalities on which this branch of science has focused have broadened to include radar signals, geophysical signals, audio, speech, music, images, videos, 3D graphics, social network data, biomedical data, bio signals, and more. Sensors feed data into a signal and information processing module that interprets the captured data and enables visualization, analysis, manipulation, and control for the SIP engineer. The discipline includes several applications of math and principles of electrical engineering to represent signals and information in ways that are beneficial for the application at hand. In today’s applications, you will find SIP in your mobile phone representing your images and videos via compression and analytics, in autonomous vehicles providing an understanding of the surrounding environment for safe and effective driving experience by machines, in healthcare establishments imaging our bodies and brains, in energy companies, and in every social media platform. Signal and information processing is the core of today’s artificial intelligence (AI) and machine learning, previously known as pattern recognition. Have you shared "what’s on your mind” with social media friends or video chatted with family today? These are just two of the many ways we use machine learning every day. Machine learning and SIP bring together mathematics, computer science, and statistics to harness predictive power, and provide the technology behind other applications, including detecting credit card fraud, medical diagnostics, stock market analysis, speech recognition, and video understanding. 

Sample of related career paths:

  • Consumer electronic and hardware (Texas Instruments, Intel, IBM, Apple, Samsung, Nvidia etc.)
  • Autonomous vehicles (Ford Motor Company, Tesla, GM, BMW, etc.)
  • Communications, 5G/6G (AT&T, Verizon, Qualcomm, Broadcom, etc.)
  • Energy (Oil & Gas Companies, GE, Siemens, smart grids companies, etc.)
  • Social media (Facebook, Twitter, etc.)
  • Aviation (SpaceX, JPL, NASA, Boeing, Airbus, etc.)
  • Gaming (Sony, Wii, EA Sport, etc.)
  • Content delivery and streaming services (Netflix, Hulu, YouTube, Google, etc.)
  • Music, sound, and audio (Bose, Apple, JBL, SONY, etc.)
  • Defense (DoD, GTRI, Boeing, Lockheed Martin, etc.)
  • Government labs (USGS, FCC, NIST, NOAA and NWS, plus DoD and other classified national security work)
  • Robotics and medical robotics (Boston Dynamics, iRobot, Raytheon, Amazon Robotics, Accuray)
  • Smart cities (Several companies in the metro Atlanta area and across the nation)
  • Remote sensing (Several companies)



Signal Processing and AI
ECE 2026 - Intro Signal Processing (part of EE common core; must be added for CmpE) 3
Pick 1
Course Hours
ECE 4270 - Fundamentals of DSP 3
ECE 4252 - Fundamentals of Machine Learning 3
ECE 3251 - Optimization for Information Systems 3
Pick 2
ECE 3084 - Signals and Systems 3
ECE 4271 - Applications of DSP 4
ECE 4260 - Random Signals and Applications  3
ECE 4273 - Design Synthesis of Application Specific Signal Processors  3
ECE 4122 - Advanced Programming Techniques for Engineering Applications 3
ECE 4783 - Introduction to Medical Image Processing 3
ECE 4258 - Digital Image Processing 3
ECE 4270 - Fundamentals of DSP 3
ECE 4252 - Fundamentals of Machine Learning 3
ECE 3251 - Optimization for Information Systems 3
ECE 4180 - Embedded Systems Design 4
Choose ECE 3000/4000 Elective
ECE 3000/4000 Elective 3