Sensor, signal & information processing certificate

The SenSIP graduate certificate is a programmatic or linked series of courses in a single field or in one that crosses disciplinary boundaries. The graduate certificate facilitates professional growth for people who already hold the baccalaureate degree, and it may be freestanding or linked to a degree program.

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.

Additional reasons and byproducts of such a program are: a) offering a certificate will bring in more students from a nationwide or even worldwide pool (ASU has large global relations with Vietnam and SenSIP has MOUs signed with Tech de Monterrey (ITESM), University of Cyprus (UCy), Imperial College, and University of British Columbia (UBC) as part of our NSF I/UCRC research activities), b) a certificate will boost ECEE’s class enrollment and SenSIP’s industry engagement and it will provide additional compelling reasons for attracting I/UCRC members to support graduate research, and c) it will enable us to enrich training activities planned with our minority institution partners Prairie View A&M (PVAMU) and Florida International University in our NSF Phase 3 Education and NSF traineeship grant collaborations.

Admission requirements

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

Applicants who 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. Regular admission may be granted to applicants who have achieved a grade point average of 3.0 (4.0 scale) or better in the last two years of work leading to a bachelor’s degree and are competitive in the applicant pool.

Core and elective 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

This certificate requires the successful completion of 16 credits (six courses). The core courses (listed above) are 10 credits total and are offered in-person at the Tempe Campus or online for an additional fee. Students will need to select two other courses as their electives to complete the required 16 credits. Some of the electives are offered online. See below for 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.

Possible elective courses

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

To see more information about the EEE courses (such as course descriptions, prerequisites, and instructor information), please refer to this page: Course prerequisites and topics.

Course descriptions

EEE 509- DSP Algorithms and Software (could be replaced with EEE 407/591- Digital Signal Processing)

  • Introduction to DSP, Use of MATLAB in DSP, Design of FIR and IIR digital filters using MATLAB, The z transform and its properties, MATLAB programming and code examples for Butterworth, Chebychev, and Elliptic Filter, Spectral Estimation using the FFT, MATLAB & J-DSP code examples of the FFT, Stationary and Ergodic Signals, The power spectrum, Adaptive Filters, Adaptive noise cancellation, speech processing applications with MATLAB, Speech and audio coding. Prerequisites: EEE 203 or equivalent. Text: Signal Processing, A Computer-Based Approach, Sanjit K. Mitra, McGraw-Hill 2001 3rd edition.

EEE 517- Sensors and Machine Learning

  • Integrated sensor devices and algorithms; microphone arrays, chemical sensing, mobile sensing; accelerometers and magnetometers, nanopore sensors, and flexible and patch sensors. Signal pre-processing for sensors; feature extraction; image sensing; voice processing; filters; frequency analysis; autocorrelation; principal components; k means algorithm; sensor fusion; neural nets; applications to health, security and mobile systems.

EEE 554- Random Signal Theory

  • Review of probability theory: Axioms of probability, experiments, outcomes, events, conditional probability and independence. , Continuous and discrete distribution and density functions, mean, variance, moments, characteristic functions, joint distributions, joint moments and characteristic functions, conditional distributions, Stochastic processes, white noise, Gaussian random processes, stationary processes, power spectrum, Markov chains. Prerequisites: EEE 350 or equivalent. Text: Intuitive Probability and Random Processes using MATLAB, S. Kay, Springer 2005.

EEE 556- Detection and Estimation Theory

  • Monte Carlo simulations, , Neyman-Pearson theorem, Detection of deterministic and random signals in noise, bias, variance, Cramer-Rao bound, Bayesian estimation, applications including biomedicine, sensors, communications, radar, and sonar. Prerequisites: EEE 554 or equivalent in Random Signal Theory. Text: Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, S. M. Kay,  Prentice Hall, 1998.
Sample plans of study

To assist with planning your future semesters in the program, here are two sample plans of study. You will be required to take at least one class each semester, but are able to take more than one course each semester. You have up to five years to complete this certificate.

Sample plan 1

  • EEE 509 – DSP Algorithms and Software- fall semester
  • EEE 554 – Random Signal Theory- fall semester
  • EEE 606 – Adaptive Signal Processing- spring semester
  • EEE 517 – Sensors and Machine Learning- fall semester
  • EEE 556 – Detection and Estimation- spring semester
  • CSE 575 – Statistical Machine Learning- spring semester

Sample plan 2

  • EEE 509 – DSP Algorithms and Software- fall semester
  • EEE 554 – Random Signal Theory- fall semester
  • EEE 556 – Detection and Estimation- spring semester
  • EEE 517 – Sensors and Machine Learning- spring semester
  • BME 598 – Biomedical Signal Processing- fall semester
  • BMI 501 – Introduction to Biomedical Informatics- spring semester

Program delivery mode

The program is primarily offered in person but could be completed online as well for an additional fee.

For more information contact:

Andreas Spanias, Ph.D.