Sensor, signal & information processing certificate

Sensors and signal processing algorithms are now embedded in billions of mobile devices and have been deployed for several applications including health, security, sustainability and integrated media. The Sensor Signal and Information Processing (SenSIP) center established this graduate certificate to support industry training and workforce creation in this area. The Sensor signal and information processing (SENSIP) graduate certificate is within the Ira A. Fulton Schools of Engineering (IAFSE) with the goal to offer opportunities for focused study of signal processing and systems algorithms for sensor related applications.

The rationale for a professional SENSIP certificate is multifold: a) a master’s degree is not needed to position an individual to work in the sensor industry, b) the certificate will enable students or professionals to have certified specialization in this area, c) the certificate will enable engineers in industry having somewhat dated degrees to retrain and position themselves to be redeployed in higher paying jobs, d) the certificate will support the creation of a specialized post-baccalaureate workforce in an area of state and national economic importance.

Admission requirements

Admission into this program is continuous, normal program deadlines are used.

Applicants who have a GPA of at least 3.0 (out of 4.0) and hold a bachelor’s degree in an engineering or science discipline, such as physics, chemistry and mathematics, from a regionally accredited institution are eligible to apply to the program. Applicants are required to submit an official ASU graduate online application, official transcripts of all undergraduate and graduate coursework, and a statement of career and educational goals.

Course requirements

This certificate requires the successful completion of 16 credits (six courses)- 4 core courses (10 credits) and 2 elective courses (at least 6 credits). See below for the core courses and some examples of electives. Some of the courses are listed as “EEE 591”. These courses are cross listed with 400 level courses. A maximum of 1/3 of the courses (two courses total) can be cross listed courses.

Core courses

  • EEE 509- DSP Algorithms and Software (could be replaced with EEE 407/591- Digital Signal Processing)
  • EEE 554- Random Signal Theory
  • EEE 517- Sensors and Machine Learning
  • EEE 556- Detection and Estimation Theory

Possible elective courses

  • EEE 404/591- Real-time DSP Systems
  • EEE 455/591- Topic: Communication Systems
  • EEE 459/591- Topic: Communication Networks
  • EEE 505- Time Frequency Signal Processing
  • EEE 506- Digital Spectral Analysis
  • EEE 508- Digital Image and Video Processing and Compression
  • EEE 511- Artificial Neural Computation
  • EEE 552- Digital Communications
  • EEE 557- Broadband Networks
  • EEE 581- Filtering of Stochastic Processes
  • EEE 589- Linear Algebra and Convex Optimization
  • EEE 598- Topic: Sensor Systems; Algorithms and Applications
  • EEE 598- Topic: Theory and Algorithms for Big Data Analysis
  • EEE 606- Adaptive Signal Processing
  • BME 598- Topic: Biomedical Signal Processing
  • BMI 501- Introduction to Biomedical Informatics
  • CSE 575- Statistical Machine Learning

For more information contact:

Andreas Spanias, Ph.D.
[email protected]