name: Ido Avrahami Degree: Electrical and Computer Engineering name: Yoni Barta Degree: M.Sc. (Research Track), Electrical & Computer Engineering, BGU

name: Ido Avrahami Advisor name: Prof. Adrian Stern Degree: Electrical and Computer Engineering :Seminar Summary Event-based dynamic vision sensors, which generate sparse spike-based outputs, are ideal for low-power appli- cations. Spiking Neural Networks are designed to process this data efficiently on asynchronous neuromorphic hardware. As event-based vision advances, understanding the vulnerability of Spiking Neural Networks to physical adversarial attacks becomes crucial. This work introduces a novel light-based adversarial attack on neuromorphic vision. We exploit undetectable optical events, specifically designed light pulses, to disrupt the temporal dynamics of event-based sensors. Our method demonstrates how these physical attacks can be tailored to the event-based data’s discrete and sparse nature while achieving high success rates. name: Yoni Barta Degree: M.Sc. (Research Track), Electrical & Computer Engineering, BGU Advisors: Prof. Nir Shlezinger and Prof. Tirza Rotenberg Seminar title: Multiuser Localization with Leaky-Wave Antennas (LWAs) for THz Communications Abstract: Next-generation wireless networks are expected to exploit the terahertz (THz) band, but high path loss and hardware complexity make conventional antenna arrays costly. Leaky-Wave Antennas (LWAs) offer a low-cost alternative that couples frequency to radiation angle, enabling beam scanning with a single passive element. This talk presents a physics-aware signal model for multiuser localization using a single LWA receiver over a wide THz band, and introduces two estimation methods: a maximum-likelihood (ML) approach and a low-complexity spectral correlation/beamforming method tailored to the LWA’s frequency–angle response. We also adapt a MUSIC-style subspace estimator by treating frequency subbands as virtual array elements, and derive Cramér–Rao bounds for AoA estimation under the LWA model. Simulations show accurate single- and multiuser AoA recovery within the effective aperture, with the proposed low-complexity method closely tracking ML performance and, in some cases, rivaling conventional ULA-based baselines—while using a single, cost-efficient THz antenna.