ABOUT ME
My primary research interests lie in spatial systems, intelligent systems, and mobile/ubiquitous computing. I am dedicated to designing and implementing real-world systems that profoundly impact society and humanity. By leveraging advanced AI solutions, edge computing, and cutting-edge technologies, I aim to solve critical challenges in healthcare, indoor/outdoor localization for emergency services and transportation, disaster management and assessment, and energy, among other areas. My vision is to promote sustainability and eco-friendly practices, driving digital transformation and urban planning to create smart cities and an intelligent society for the betterment of humanity.
NEWS
Publications
Selected Journal Papers
2022
Hamada Rizk,
, Hirozumi Yamaguchi and Moustafa Youssef. 2022. “Cross-Subject Activity Detection for COVID-19 Infection Avoidance Based on Automatically Annotated IMU Data” in IEEE Sensors Journal.2022
Hamada Rizk, Ahmed Elmogy, and Hirozumi Yamaguchi. 2022. “A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI” Sensors 22, no. 7: 2700.
DOI: 10.3390/s22072700
2021
Hamada Rizk, Moustafa Abbas, and Moustafa Youssef. 2021. Device-independent cellular-based indoor location tracking using deep learning. Pervasive Mob. Comput. 75, C (Aug 2021).
2019
Hamada Rizk, Marwan Torki, and Moustafa Youssef. “CellinDeep: Robust and accurate cellular-based indoor localization via deep learning”. IEEE Sensors Journal 19.6 (2019): 2305-2312.
Selected Conference Papers
2020
The 17th IEEE International Conference on Pervasive Computing and Communications . OmniCells: Cross-Device Cellular-based Indoor Location Tracking Using Deep Neural Networks. Hamada Rizk, Moustafa Abbas, Moustafa Youssef..
2019
27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019) . MonoDCell: A Ubiquitous and Low-Overhead Deep Learning-based Indoor Localization with Limited Cellular Information. Hamada Rizk, Moustafa Youssef.
2019
The 17th IEEE International Conference on Pervasive Computing and Communications. WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. Moustafa Abbas, Moustafa Elhamshary, Hamada Rizk, Marwan Torki, and Moustafa Youssef. March 2019.
2019
The IEEE Wireless Communications and Networking Conference (WCNC). Effectiveness of data augmentation in cellular-based localization using deep learning. Hamada Rizk, Ahmed Shokry, Moustafa Youssef. April 2019.
SELECTED PATENTS
- Cellular-based Indoor Localization Using Deep Learning
Filed a patent with the Egyptian Academy of Scientific Research and Technology titled “Cellular-based Indoor Localization Using Deep Learning”. Patent ID: 1531/2018, Date of filing: 27/9/2018, Time of filing: 10:24 AM.
- Wearable Monitoring Device.
Wearable Monitoring Device. Patent number PCT/JP2022/042987, filed on November 21, 2022, by Osaka University, developed by Hirozumi Yamaguchi, Satoshi Hiromori, and Hamada Rizk under PCT.
- Wearable Monitoring Device.
Patent number JP 2022-100902, filed on January 26, 2022, by Osaka University, developed by Hirozumi Yamaguchi, Satoshi Hiromori, and Hamada Rizk in Japan.
PROJECTS
TALKS
SERVICES
EDUCATION
Ph.D., Computer Science and Engineering
2020
M.Sc., Computer Engineering
2016
B.Sc., Computer & Automatic Control Engineering
2010