Waves and Machine Intelligence
SUB-SURFACE IMAGING_0.png (1.75 KB)   Sub-surface imaging Division

Relying on the international scientific research platform provided by Directed Energy Research Center (DERC) at TII, the WMI team is committed to the study of geophysics using various geophysical signals (including seismic waves, GPR, gravitational, and electromagnetic field data) to image the internal structure and properties of the earth, whether deep or shallow. Currently, the team is focusing on seismic wavefield simulation, full waveform inversion, gravity, and magnetic field multi‑physical attribute inversion, etc., to provide cutting‑edge imaging capabilities, algorithms, and solutions for oil and gas resource exploration, mineral resource exploration, deep earth structure exploration, and geological disaster prevention and environmental protection. The WMI team synthesizes contemporary machine learning algorithms with traditional geophysical data processing techniques to improve imaging resolution and efficiency.


MACHINE LEARNING FUNDAMENTALS_0.png (3.42   Machine Learning Fundamentals Division

The WMI team focuses on the accurate real-time detection, classification, and identification of Unmanned Aerial Vehicles (UAVs). The democratization of UAVs is creating an unprecedented need to monitor any misuse of this new technology. This division is essential to guarantee the safe use of this technology and to protect critical infrastructures. Our aim is to leverage CNN and passive Spectrum monitoring systems to detect, identify and classify UAVs at a long range. The team is also interested in the detection of landmines from GPR data using Artificial Intelligence tools.


 SYSTEMS DYNAMICS_0.png (2.52 KB)  System Dynamics and Resilience Division

The WMI team is currently investigating complex networks and critical infrastructures. Specifically, the team is currently establishing innovative network centralities based on network dynamics to determine the critical nodes of networks. Additionally, we offer a new perspective on the resilience of critical infrastructures using the novel Idle Network developed at TII. The goal of this research is two‑ fold: (i) to create an algorithm that accurately identifies the critical nodes in critical infrastructures, and (ii) to provide solutions to increase the resilience of critical infrastructures. 

Beyond that, the WMI team is also responsible for generating and accelerating algorithms for data‑processing in order to enable real‑time‑processing or reduce computational time. This is done with the ultimate goal of facilitating research, providing unique internal libraries, and doing incremental research based on computational costs and algorithm optimization.