Our expert faculty receive a number of prestigious grant awards and collaborate with students on research projects.
Research Grant Projects
|Project Title||Sponsor||Amount||Principal Investigator|
|Collaborative Research: SaTC: CORE: Small: UAV-NetSAFE.COM: UAV Network Security Assessment and Fidelity Enhancement through Cyber-attack-ready Optimized Machine-learning Platforms||National Science Foundation||$200,000 (2020-2023)||Khair Al Shamaileh|
|Collaborative Research: FW-HTF-P: IntelEUI: Artificial Intelligence and Extended Reality to Enhance Workforce Productivity for the Energy and Utilities Industry||National Science Foundation||$79,000 (2021-2022)||Xiaoli Yang (PI), Quamar Niyaz (Co-PI)|
|Collaborative Research: IUSE: EHR: CyberMUG: Cybersecurity Modules aligned with UG Computer Science and Engineering Curriculum||National Science Foundation||$140,500 (2020-2022)||Quamar Niyaz (PI), Xiaoli Yang (PI)|
|SaTC: EDU: Collaborative: INteractive VIsualization and PracTice basEd Cybersecurity Curriculum and Training (InviteCyber) Framework for Developing Next-gen Cyber-Aware Workforce||National Science Foundation||$231,400 (2019-2022)||Xiaoli Yang (PI), Quamar Niyaz (PI)|
|Deep Learning Methods with Bio-signals for Control of Robotics||Indiana Space Grant Consortium||$11,726 (2020-2021)||Lizhe Tan (PI), Jean Jiang (PI)|
- Yuchen Li, Jered Pawlak, Joshua Price, Khair Al Shamaileh, Quamar Niyaz, Sidike Paheding, and Vijay Devabhaktuni, “Jamming Detection and Classification in OFDM-based UAVs via Feature- and Spectrogram-tailored Machine Learning,” in IEEE Access, Vol. 10, pp. 16859-16870, 2022.
- Jeevan Devagiri, Sidike Paheding, Quamar Niyaz, Xiaoli Yang, and Samantha Smith, “Augmented Reality and Artificial Intelligence in Industry: Trends, Tools, and Future Challenges,” Elsevier Expert Systems with Applications, vol. 207, no. 118002, 2022.
- John Estrada, Sidike Paheding, Xiaoli Yang, and Quamar Niyaz, “Deep Learning Incorporated Augmented Reality Application for Engineering Lab Training”, MDPI Applied Sciences 12, no. 10: 5159, 2022.
- Xiaofan Liu, Jason Centeno, Juan, Alvarado, and Lizhe Tan, “One dimensional convolutional neural networks using sparse wavelet decomposition for bearing fault diagnosis,” IEEE Access, vol. 10, pp. 86998-87007, August 2022.
- O. Hussein, K. Al Shamaileh, H. Sigmarsson, S. Abushamleh, N. Aboserwal, and V. Devabhaktuni, “A Half-mode substrate integrated waveguide filtering power divider with Fourier-varying via holes,” Microwave and Optical Technology Letters, vol. 63, no. 12, pp. 2964–2968, December 2021.
- Akshay Mathur, Laxmi Podilla, Keyur Kulkarni, Quamar Niyaz, and Ahmad Y. Javaid, “NATICUSdroid: A Malware Detection System in Android using Native and Custom Permissions,” Elsevier Journal of Information Security and Applications, Vol. 58, No. 102696, 2021, Impact Factor: 3.872
- P.C. Srinivasa Rao, A. J. Sravan Kumar, Quamar Niyaz, Sidike Paheding, and Vijay Devabhaktuni, “A Chemical Reaction Optimization based Feature Selection Technique for Machine Learning Classification Problems,” Elsevier Expert Systems and Applications, Vol. 167, No. 114169, 2021, Impact Factor: 6.954.
- Kyle Greene, Deven Rodgers, Henry Dykhuizen, Quamar Niyaz, Khair Al Shamaileh, and Vijay Devabhaktuni, “A Defense Mechanism Against Replay Attack in Remote Keyless Entry Systems Using Timestamping and XOR Logic,” IEEE Consumer Electronics Magazine, Vol. 10, Issue 1, pp. 101─108, 2021, Impact Factor: 3.789.
- A. Lendek, L. Tan, “Mitigation of derivative kick using time-varying fractional-order PID control,” IEEE Access, vol. 9, pp. 55974-55987, April 2021.
- J. Chen, J, J. Jiang, X. Guo, L. Tan, “An efficient CNN with tunable input-size for bearing fault diagnosis,” International Journal of Computational Intelligence Systems, vol. 14, no. 1, pp. 625–634, 2021.
- J. Chen, J. Jiang, X. Guo, L. Tan, “A self-Adaptive CNN with PSO for bearing fault diagnosis,” Systems Science & Control Engineering, vol. 8, no. 1, pp. 11-22, 2021.
- J. Chen, J. Jiang, X. Guo, L. Tan, “Bit-error aware lossless image compression with 2D-layer-block coding,” Journal of Sensors, vol. 2021, November, 2021.
- Xinrun Zhang, Akshay Mathur, Lei Zhao, Safia Rahmat, Quamar Niyaz, Ahmad Javaid, and Xiaoli Yang, “An Early Detection of Android Malware Using System Calls based Machine Learning Model,” 17th International Conference on Availability, Reliability and Security (ARES 2022), August 23-26, 2022, Vienna, Austria.
- Jansen Tan, Divya Ravindra, Quamar Niyaz, Xiaoli Yang, Ahmad Y. Javaid, and Sidike Paheding, “Mini-projects based Cybersecurity Modules for an Operating System Course using xv6,” 2022 ASEE Annual Conference and Exposition, Minneapolis, Minnesota, USA, June 26-29, 2022.
- Jyothirmai Kothakapu, Ahmad Y Javaid, Quamar Niyaz, Xiaoli Yang, Sidike Paheding, and Charlene Czerniak, “Introducing Cybersecurity in a Discrete Structures Course Through a Visualization-based Plug-and-Play Cryptography Module,” 2022 ASEE Annual Conference and Exposition, Minneapolis, Minnesota, USA, June 26-29, 2022.
- Kenneth Guernsey, Jacob Tietz, Quamar Niyaz, Xiaoli Yang, Ahmad Y. Javaid, and Sidike Paheding, “Work-In-Progress: Enabling Secure Programming in C++ & Java through Practice Oriented Modules,” at 2022 ASEE Annual Conference and Exposition, Minneapolis, Minnesota, USA, June 26-29, 2022.
- Abel Reyes, Xiaoli Yang, Quamar Niyaz, Sidike Paheding, and Ahmad Javaid, “A Secure Software Engineering Design Framework for Educational Purpose” 2022 IEEE International Conference on Electro/Information Technology, pp. 375-381, Mankato, Minnesota, USA, May 2022.
- Joshua Price, Yucehn Li, Khair Al Shamaileh, Quamar Niyaz, Naima Kaabouch, and Vijay Devabhaktuni, “Real-time Classification of Jamming Attacks against UAVs via onboard Software-defined Radio and Machine Learning-based Receiver Module” 2022 IEEE International Conference on Electro/Information Technology, pp. 252-256, Mankato, Minnesota, USA, May 2022.
- Xiafan Liu, Yuanyang Cai, Yanan Song, and Lizhe Tan, “Bearing Fault Diagnosis Based on Multi-scale Neural Networks,” 2022 IEEE International Conference on Electro/Information Technology, pp. 80-85, Mankato, Minnesota, USA, May 2022.
- Tianshu Ruan, V. Amrusha Aryasomyajula, and Nasser Houshangi, “Performance of monocular and stereo camera in indoor environment for Visual SLAM using ORB method,” 2022 IEEE International Conference on Electro/Information Technology, pp. 273-278, Mankato, Minnesota, USA, May 2022.
- H. Jaradat, N. Dib, and K. Al Shamaileh, “A miniaturized ultra-wideband Wilkinson power divider using non-uniform coplanar waveguide,” International Congress on Engineering Technologies, September 2021.
- J. Pawlak, Y. Li, J. Price, M. Wright, K. Al Shamaileh, Q. Niyaz, and V. Devabhaktuni, “A machine learning approach for detecting and classifying jamming attacks against OFDM-based UAVs,” ACM Workshop on Wireless Security and Machine Learning, Abu Dhabi, UAE, July 2021.
- Doga Ozgulbas, and N. Houshangi, “Autonomous Navigation and Room Categorization for an Assistant Robot”, 7th International Conference on Engineering and Emerging Technologies, Istanbul, Turkey, October 2021.
- Akshay Mathur, Ethan Ewoldt, Quamar Niyaz, Xiaoli Yang, and Ahmad Javaid, “Permission Educator: App for Educating Users about Android Permissions,” at 13th International Conference on Intelligent Human-Computer Interaction, December 20-22, 2021, Kent, Ohio, USA.
- Y. Cai, L. Tan, J. Chen, “Evaluation of deep learning neural networks with input processing for bearing fault diagnosis,” 2021 IEEE International Conference on Electro/Information Technology, pp. 140-145, Mt. Pleasant, MI, May 2021.
- Y. Song, Y. Cai, L. Tan, “Video-audio emotion recognition based on feature fusion deep learning method,” 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 611-616, Lansing, MI, August 2021.