RANCS lab participated in the Creepy Crawly STEM Fab Outreach on Friday, 10/15/2021, from 5-8 PM. STEM Fab Friday event is focused on getting children […]
Dr. Sargolzaei has been promoted to the grade of IEEE Senior member
Congratulations to Dr. Sargolzaei, director of the RANCS research lab, on his elevation to the grade of IEEE Senior member. IEEE Senior Membership is an […]
Research platform for testing connected and autonomous vehicles in a virtual environment
Thanks to the National Science Foundation grant enabling Dr. Arman Sargolzaei’s research in the development of a Vehicle-in-the-Loop (ViL) facility for testing connected and autonomous […]
Driving simulator helps students to learn control theory
We developed assignments for graduate and undergraduate students in the Automatic Control course to use the driving simulator located in the Mechatronics Lab at Tennessee […]
Our recent paper has been accepted by IEEE-ACC
Our paper entitled “A Secure Control Design for Networked Control System with Nonlinear Dynamics under False-Data-Injection Attacks” has been accepted for publication by IEEE American […]
Our paper has been published by IEEE Access Journal
Our paper entitled “Autonomous Vehicles Scenario Testing Framework and Model of Computation: On Generation and Coverage” has been accepted and published by the IEEE Access […]
Career paths for graduate students event
We are delighted to have Dr. Jeff Daniels, program director of Lockheed Martin Corporation, as our speaker on April 5, at 11 AM CST to […]
RANCS Lab participated in the university showcase
RANCS lab’s director and members participated in the university showcase on Saturday 03/06/2021. James Holland discussed the Formula SAE and our effort to make it […]
Our recent survey paper on the security of connected and automated vehicles has been published
Our paper entitled “Blockchain Applications to Improve Operation and Security of Transportation Systems: A Survey” has been accepted and published by the MDPI Electronics journal. […]
Our paper has been published by Electronics Journal
Our paper entitled “A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data” has been accepted and published by the […]