TII's Directed Energy Research Center will present groundbreaking studies on diverse topics, from advanced high-power electromagnetic systems and pulsed power generators to innovative applications of artificial intelligence and metalens technology at the GlobalEM 2024 conference. Learn more below:
Title: Topology Optimization of an X-Band Cavity-Based Slow Wave Structure for Enhanced Bandwidth, Gain, and Efficiency
Authors: Moza Mohamed, Jane M.Lehr*, Ernesto Neira, A. Elfrgani*, Fernando Albarracín, Felix Vega, Chaouki Kasmi - *University of New Mexico
Abstract: Traditional design of Slow wave structures (SWS) used in X-band backward wave oscillators (BWOs) and traveling wave tubes (TWTs), systems that are used in the communications and radar industry, often rely on heuristic and incremental modifications which often fall short with the current demands for broader bandwidth and higher efficiency. The complex interaction of electromagnetic phenomena within the SWS poses the main challenge in the improvement of the overall system. This study aims to identify an effective topology optimization algorithm to improve the SWS performance metrics that will enable enhanced BWO and TWT systems.
Title: A Compact Spiral Generator for High-Power Electromagnetic Systems
Authors: Aaesha AlAli, Gideon Nimo Appiah, Umar Hashmi, Hamad Deiban, Fernando Albarracin, Felix Vega, and Chaouki Kasmi
Abstract: This work presents a practical implementation of a compact spiral generator for high-power electromagnetic systems (HPEM) applications. An analytical design is presented and validated via measurements. A prototype of a spiral generator with a 40 kV peak output amplitude and a 0.28 kV/ns rate-of-rise voltage is manufactured and tested in the laboratory
Title: Experimental Tests of a 300-kV PFN-Marx Generator for Low-Impedance Loads
Authors: Umar Hashmi, Aaesha AlAli, Gideon Appiah, Hamad Deiban, Fernando Albarracin, Felix Vega, Chaouki Kasmi
Abstract: This work presents the design and preliminary experimental tests of a PFN-Marx generator for low-impedance loads. A pulse-forming network PFN approach is implemented at each stage of the Marx generator to widen the pulse length to 150 ns. The design and simulated results are presented along with experimental results on a 33.6 Ω load.
Title: A 700W Amplifier System For L-Band Applications
Authors: Bharathidasan Sugumaran, Oliver Silva, Wajid Khattak, Abdul Baba, Mae Almansoori, Felix Vega, Chaouki Kasmi
Abstract: In this paper, an amplifier system operating in the 1.5 to 1.6 GHz frequency range is presented. The saturated output power reaches 700 Watts, and the Power Added Efficiency (PAE) is 49%. To our knowledge, the energy efficiency and compactness ratio is higher than any commercially available solution in the market.
Title: Characterization of the Wave Propagation Generated by an Indoor EMP Simulator
Authors: Ali Yaqoob, David Martinez, Hamad Alyahyaee, Islem Yahi, Felix Vega, Chaouki Kasmi
Abstract: This research investigates EM wave propagation within a removable EMP simulator housed in a Semi-Anechoic Chamber (SAC). By integrating findings from experimental setups and computer simulations, the study aims to understand the EM wave propagation within the modular system with its distinct architecture and dimensions. This contribution is pivotal for advancing electronic resilience against EMP threats and paves the way for innovative testing solutions in EMC evaluations.
Title: Towards Low-Cost Single-Port Imaging of Reflective Targets with a Resonant Metalens
Authors: Elias Le Boudec, Farhad Rachidi, Felix Vega, Hamidreza Karami, and Marcos Rubinstein
Abstract: We use a resonant metalens to locate sub-wavelength reflectors on a plane at frequencies and bandwidths lower than traditional synthetic aperture radars, enabling low-cost or high-penetration imaging.
Title: Enhanced landmine discrimination from GPR data using AI-based algorithms
Authors: A. Rangel, F. Ruiz, C. Pedraza. F. Vega, L. Prado, M. Al Mansoori, S. Ghazal, A. Al Mesmari, O. Lapuz, C. Kasmi
Abstract: This paper focuses on improving the discrimination ratio between target and clutter in Ground Penetration Radar (GPR) acquired data. The approach proposed involves using Artificial Neural Networks (ANNs) and Machine Learning (ML) techniques applied to GPR data to detect and classify the characteristic hyperbolic signature from specific buried objects, with a particular emphasis on Improvised Explosive Devices (IEDs), specifically improvised antipersonnel landmines.