1. Ssekidde, P., Steven Eyobu, O., Han, D. S., & Oyana, T. J. (2021). Augmented cwt features for deep learning-based indoor localization using WiFi RSSI data. Applied Sciences, 11(4), 1806.
2. Enebuse, I., Foo, M., Ibrahim, B. K. K., Ahmed, H., Supmak, F., & Eyobu, O. S. (2021). A comparative review of hand-eye calibration techniques for vision guided robots. IEEE Access.
3. Matovu, J., Mirembe, D.P. and Eyobu, O.S. (2021) ‘Evolution of mobile payment systems and level of security awareness among mobile money users in Uganda’, Int. J. Information Technology, Communications and Convergence, Vol. 4, No. 1, pp.73–94.
4. Nassuna, H., Eyobu, O. S., Kim, J. H., & Lee, D. (2020). Feature Selection for Abnormal Driving Behaviour Recognition Based on Variance Distribution of Power Spectral Density. IEMEK. J. Embed. Sys. Appl.
5. Poulose, A., Eyobu, O. S., & Han, D. S. (2019). An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data. IEEE Access. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8606925

6. Steven Eyobu, O., Joo, J., & Han, D. S. (2018). CMD: A Multichannel Coordination Scheme for Emergency Message Dissemination in IEEE 1609.4. Mobile Information Systems, 2018.

7. S. Eyobu, J. Joo, and D. S. Han, (2017). “A broadcast scheme for vehicle-to-pedestrian safety message dissemination,” International Journal of Distributed Sensor Networks, vol. 13, Issue. 11, DOI:10.1177/1550147717741834, 2017. http://journals.sagepub.com/doi/abs/10.1177/1550147717741834

8. S. Eyobu and D. S. Han, Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network, Sensors 2018, 18, 2892. http://www.mdpi.com/1424-8220/ 18/9/2892

9. Joo, O. S. Eyobu, J. H. Kim, H.-J. Jeong, and D. S. Han, “Analysis of Radio Propagation Characteristics             for V2V Scenarios in WAVE Standard Based Vehicular Communication System,”          The Journal of Korean Institute of Communications and Information Sciences, vol. 42, pp. 1175-1184,2017. DOI: 10.7840/kics.2017.42.6.1175 http://www.kics.or.kr/Home/UserContents/20170712/170712_153120412.pdf

Conferences

10. Poulose, A., Žiga, E., O.S. Eyobu & D.S. Han. (2020, October). An Accurate Indoor User Position Estimator For Multiple Anchor UWB Localization. The 11th International Conference on ICT Convergence, Ramada Plaza Hotel, Jeju Island, Korea.
11. Poulose, A., Eyobu, O. S., Kim, M., & Han, D. S. (2019, July). Localization Error Analysis of Indoor Positioning System Based on UWB Measurements. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 84-88). IEEE.
12. Poulose, A., Eyobu, O. S., & Han, D. S. A Combined PDR and Wi-Fi Trilateration Algorithm for Indoor Localization. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) , Feb. 2019, (pp. 072-077). IEEE.
13. Nassuna, H., Eyobu, O. S., Kim, J. H., & Lee, D. Feature Selection Based on Variance Distribution of Power Spectral Density for Driving Behavior Recognition. In 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Jun. 2019, (pp. 335-338). IEEE.
14. S. Eyobu, P. Alwin and D. S. Han, An Accuracy Generalization Benchmark for Wireless Indoor Localization based on IMU sensor Data,” in Consumer Electronics (ICCE), 2018 IEEE International Conference on, Berlin, 2–5 September 2018, pp. ..
15. S. Eyobu, J. Joo, B. Senouci, and D. S. Han, “A distance-based event-Driven broadcast (DEB) mechanism for V2V safety communications,” in Ubiquitous and Future Networks (ICUFN), 2017 Ninth International Conference on, Milan, Italy, 2017, pp. 200-202.
16. Eyobu, O.S.; Kim, Y.; Han, D.S. Activity Recognition for Infant Health Care Based on Wireless Inertial Measurement Unit Motion Data. In Proceedings of the Korean Institute of Communication Sciences Conference, Jeju, Korea, 21–23 June 2017; pp. 436–436.
17. S. Eyobu, J. Joo, and D. S. Han, “Cooperative multi-channel dissemination of safety messages in VANETs,” in Region 10 Conference (TENCON), IEEE, Singapore, 22-26 November, 2016, pp. 1867-1870.
18. Joo, O. S. Eyobu, D. S. Han, and H.-J. Jeong, “Measurement based V2V path loss analysis in urban NLOS scenarios,” in Ubiquitous and Future Networks (ICUFN), Eighth IEEE International Conference on,  Vienna, Austria 5-8, July, 2016, pp. 73-75.
19. S. Eyobu, Y. W. Kim, D. Cha, and D. S. Han, “A real-time sleeping position recognition system using    IMU sensor motion data,” in Consumer Electronics (ICCE), 2018 IEEE International Conference on, Las Vegas, 12-14 January, 2018, pp. 1-2.
20. Kim, O. S. Eyobu, and D. S. Han, “ANN-based stride detection using smartphones for Pedestrian dead reckoning,” in Consumer Electronics (ICCE), 2018 IEEE International Conference on, Las Vegas, 12-14 January, 2018, pp. 1-2.
21. S. Eyobu and D. S. Han, “Reinforcement Learning-based Adaptive Transmission Control for V2P Safety Communications,” Summer Conference 2018, Korean Institute of Communication Sciences, 2018.
22. O. S. Eyobu, J. Joo, and D. S. Han, “V2P Communications for Safety,” 2015 Korean Society of Broadcast Engineers (KSOBE), pp. 13-16, 2015.