Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
AIDRC researchers co-authored an accepted IEEE Vehicular Technology Magazine article on time reversal for 6G wireless communications. The article presents experimental results on the spatiotemporal focusing capability of time reversal across carrier frequencies. It examines requirements for efficient time reversal operation and synergies with technologies like reconfigurable intelligent surfaces. Why it matters: The research explores advancements in 6G wireless communication, with potential implications for coverage extension, sensing, and localization capabilities in the region.
The paper introduces a novel method for short-term, high-resolution traffic prediction, modeling it as a matrix completion problem solved via block-coordinate descent. An ensemble learning approach is used to capture periodic patterns and reduce training error. The method is validated using both simulated and real-world traffic data from Abu Dhabi, demonstrating superior performance compared to other algorithms.
Communications Physics journal has a focus collection on space quantum communications. The collection covers supporting technologies, new quantum protocols, inter-satellite QKD, constellations of satellites, and quantum inspired technologies and protocols for space based communication. Contributions are welcome from October 20, 2020 to April 30, 2021, and accepted papers are published on a rolling basis. Why it matters: Space-based quantum communication is a critical area for developing secure, global quantum networks, and this collection could highlight relevant research for the GCC region as it invests in advanced technologies.
KAUST Associate Professor Taous-Meriem Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group, focusing on control theory, system modeling, and signal applications. Her group develops mathematical models and algorithms to control processes relying on real-time feedback, especially for systems where experimental data is limited. The EMAN group recently developed a real-time control algorithm for a solar membrane distillation system, increasing water production by over 50% in simulations. Why it matters: Laleg-Kirati's work advances both engineering and healthcare by combining model-based research with AI, offering opportunities for personalized medicine and efficient resource management in the region.
A KAUST team led by Hossein Fariborzi won second place in the MEMS Design Contest for their "MEMS Resonator for Oscillator, Tunable Filter and Re-Programmable Logic Applications." The device is runtime-reprogrammable, allowing the function of each device in the circuit to be changed during operation. The KAUST team demonstrated that two MEMS resonators could replace over 20 transistors in applications like digital adders, reducing digital circuit complexity. Why it matters: This innovation could significantly reduce power consumption, chip area, and manufacturing costs in microprocessors, advancing the development of energy-efficient microcomputers in the region.