Plan of Study

Semester 1

  • Introduction to AIDC (Automatic Identification and Data Capture): This course is an introductory course that covers topics such as bar codes, biometrics, RFID, card technologies, and related enabling technologies. Each semester, students participate in research projects that either result in a published paper in a conference or journal, or a poster presentation. (3 credits)
  • Portfolio Development: A portfolio, to be viewed by the professor, fellow students, a committee or future employers, will be generated to showcase the student's ability to understand and describe biometric applications. This course will introduce the concepts to best display a portfolio and what is expected in the final draft of the portfolio. (1 credits)
  • STAT 501 or equivalent: Concepts and methods of applied statistics. Exploratory analysis of data. Sample design and experimental design. Normal distributions. Sampling distributions. Confidence intervals and tests of hypotheses for one and two samples. Inference for contingency tables, regression and correlation, and one-way analysis of variance. Use of the SAS statistical software. Intended primarily for students who have not had calculus. Not open to students in mathematical sciences or engineering. (3 credits)

Semester 2

  • Biometric Technology and Applications: This course is a gateway into the field of biometrics. Designed for upper level undergraduate and graduate students in any major, the course covers an introduction to biometric modalities, testing and evaluation (including design of experiments), and standards. (3 credits)
  • STAT502 or equivalent: Regression with several explanatory variables. Regression diagnostics. Analysis of variance for factorial designs. Multiple comparisons. Analysis of covariance. Repeated measures designs. Extensive use of the SAS statistical software. Intended primarily for students who have not had calculus. Not open to students in mathematical sciences or engineering. (3 credits)
  • Concentration Selective: (3 credits)

Semester 3

  • Project Management: This course introduces the application of knowledge, skills, tools, and techniques that project managers use to plan, staff, estimate, and manage information technology projects. Special emphasis is placed on learning and applying the concepts of managing scope, risk, budget, time, expectations, quality, people, communications, procurement, and externally provided services. Students will apply project management technology and techniques to business problems. (3 credits)
  • Biometric Performance and Usability: The focus of this course is to use many of the tools in the biometrics lab, including advanced matchers and image quality tools. Part of the work will be to replicate some of our previous studies in order to understand how the software works, and then start to propose research topics that will be published online as well as submitted to journals. The course will also undertake detailed analysis, including design of the experiment, data analysis and report writing. (3 credits)
  • Info Sec Concepts: The introductory course will expose the student to various design principles of trusted computing bases, legal regulations, investigation and compliance requirements, secure computing concepts, numerous security protocols and principles, practical networking security methodologies, and an introduction to business continuity and disaster recovery concepts. ‚Äč(3 credits)

Semester 4

  • Biometric Policy, Law & Ethics: Review and analyze policies, laws, and ethical behavior regulations of biometric technologies. (3 credits)
  • Business and Biometrics: This course is an introduction to how biometric technologies are used in business and industry environments. (3 credits)
  • Concentration Selective: (3 credits)

Concentration Selectives

  • Human Factors in Engineering: Survey of human factors in engineering with particular reference to human functions in human-machine systems, and consideration of human abilities and limitations in relation to design of equipment and work environments. (3 credits)
  • Topics in Industrial Engineering: Selected topics in industrial engineering for seniors and graduate students. Permission of instructor required. (1 to 6 credits) 
  • Applied Regression Analysis: Descriptive statistics; elementary probability; sampling distributions; interference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression. (3 credits)
  • Statistical Quality Control: A strong background in control charts including adaptations, acceptance sampling for attributes and variables data, standard acceptance plans, sequential analysis, statistics of combination, moments and probability distributions, applications. (3 credits)
  • Design of Experiments: Fundamentals, completely randomized design; randomized complete blocks; latin square; multi-classification; factorial; nested factorial; incomplete block and fractional replications for 2n, 3n, 2m x 3n; confounding; lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design. Use of existing statistical programs. (3 credits)
  • Sampling and Survey Techniques: Survey designs; simple random, stratified, and systemic samples; systems of sampling; methods of estimation; costs. (3 credits)