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Fingerprint Dive into the research topics where Hamid Parsaei is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Cellular manufacturing Engineering & Materials Science
Students Engineering & Materials Science
Economics Engineering & Materials Science
Manufacturing Mathematics
Process planning Engineering & Materials Science
Computer integrated manufacturing Engineering & Materials Science
Teaching Engineering & Materials Science
Process Planning Mathematics

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Research Output 1984 2019

Applications of virtual reality (VR) as teaching tool to enrich students learning

Ismail, M. Y., Parsaei, H. & Parsaei, B., 1 Jan 2019, In : Proceedings of the International Conference on Industrial Engineering and Operations Management. 2019, MAR, p. 1202-1203 2 p.

Research output: Contribution to journalConference article

Virtual reality
Teaching
Students
Augmented reality
Student learning

Capacity building through strengthening professional skills in engineering graduates

Retnanto, A., Parsaei, H. & Parsaei, B., 1 Jan 2019, Advances in Human Factors in Training, Education, and Learning Sciences - Proceedings of the AHFE 2018 International Conference on Human Factors in Training, Education, and Learning Sciences, 2018. Springer Verlag, p. 146-150 5 p. (Advances in Intelligent Systems and Computing; vol. 785).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Accreditation
Students
Curricula
Education
Planning
Petroleum engineering
Accreditation
Visibility
Curricula
Students

A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems

Vijayaraghavan, V., Kianfar, K., Ding, Y. & Parsaei, H., 4 Dec 2018, 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. IEEE Computer Society, Vol. 2018-August. p. 836-842 7 p. 8560604

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Integer programming
Sensors
Sensor networks
Monte Carlo methods

An L1-minimization based algorithm to measure the redundancy of state estimators in large sensor systems

Vijayaraghavan, V., Kianfar, K., DIng, Y. & Parsaei, H., 12 Jan 2018, 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE Computer Society, Vol. 2017-August. p. 424-428 5 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Integer programming
Heuristic algorithms
Computational efficiency
Sensor networks
Redundancy