A new broadband electromagnetic induction (EMI) array sensor, GEM-5, for detecting and characterizing Unexploded Ordnance (UXO) has been developed in order to provide high production rates for EMI surveys. The sensor consists of a single rectangular loop transmitter around a linear array of seven pairs of coaxial receiver coils, with each coil in a pair located at the same vertical distance abo...

Using Uncrewed Aerial vehicles (UAVs) to rapidly scan areas for potential unexploded ordnance (UXO) can provide an efficiency increase while minimizing detonation risks. We present a complete overview of how such mappings be performed using scalar magnetometers, including initial sensor testing, time stamping validation, data positioning, noise removal, and source model inversion. A test survey...

The goal of the work presented in a two-companion paper is to pave way for reliably assessing risks damage buildings on shore, induced by detonation unexploded historical ordnance (UXO) large weights variable shallow water environments with depth less than 50 m. risk assessment quantified through seismic magnitude Richter scale, charges different (between 80- and 680-kg TNT-equivalent). This me...

For unexploded O=ordnance (UXO) detection, individual technology cannot achieve the best detection performance. The new mode of joint magnetic and electromagnetic method has attracted more attention. In this paper, a newly developed system is introduced, multi-rotor UAV-based (UAVMAG) cart-based time-domain (TDEM-Cart) are combined, cooperative processing field data proposed. result inversion f...

OBJECTIVE To estimate post-traumatic stress disorder (PTSD) symptoms in those injured and not injured by landmines or unexploded ordnance (UXO) in rural Lao People's Democratic Republic and to determine whether the perception of social support was associated with PTSD symptom severity. METHODS A community survey was conducted among 190 people injured by landmines or UXO and 380 age-, sex- and...

This study analyzed the accuracy of solar cell modeling parameters extracted from noisy data using Genetic Algorithms (GAs). Three crossover operators (XOs) were examined, namely Uniform (UXO), Arithmetic (AXO), and Blend (BXO) operators. The used an experimental benchmark a simulated curve where noise levels (p) 0 to 10% added. For each XO, analysis was carried out by running GAs 100 times var...