Name: Yuval Meir Studies: Electrical and Computer Engineering with Thesis Under the supervision of : Prof. Shmuel Ben-Yaakov Guy Perets MSc. In Communication Systems Engineering Mentors: Prof. Itshak Lapidot (Afeka), Dr. Yehuda Ben-Shimol (BGU) Tuvia Hausdorff M.sc in Electrical and Computers Engineering Supervised by Dr. Dan Vilenchik Title: Robust Voice-Based Depression Detection in Real-World Settings using Voice Conversion GenAI

Name: Yuval Meir Studies: Electrical and Computer Engineering with Thesis Under the supervision of : Prof. Shmuel Ben-Yaakov Seminar Title: Second harmonic generation in nonlinear capacitors and application in electric field sensor design Abstract: This seminar presents a study on the design of a novel electric field sensor based on the Second Harmonic Generation (SHG) phenomenon in Non-Linear Capacitors (NLC). The research focuses on developing a comprehensive model to describe the generation of the second harmonic signal under large-signal excitation, explicitly characterizing the correlation between the capacitor's non-linearity profile and the resulting harmonic magnitude. Bridging theoretical analysis with high-fidelity SPICE simulations, the study validates these findings through laboratory experiments and culminates in the design and implementation of a practical proof-of-concept electric field tester, demonstrating the feasibility of this sensing approach. Guy Perets MSc. In Communication Systems Engineering Mentors: Prof. Itshak Lapidot (Afeka), Dr. Yehuda Ben-Shimol (BGU) Subject: Waveform-Based Spoof-Robust Automatic Speaker Verification Using Deep Learning Seminar Summary: Modern speaker verification systems are increasingly targeted by spoofing attacks such as replay and synthetic speech. A waveform-based anti-spoofing system distinguishes genuine speech from manipulated audio by learning authenticity cues directly from the raw signal. It models the characteristic patterns of natural speech production and real recording chains, and flags samples that deviate in ways consistent with spoofing while aiming to stay reliable across diverse conditions and attack styles. Tuvia Hausdorff M.sc in Electrical and Computers Engineering Supervised by Dr. Dan Vilenchik Title: Robust Voice-Based Depression Detection in Real-World Settings using Voice Conversion GenAI Summary: My research explores how speech-based machine learning systems can robustly detect depression in real-world clinical settings, where data is limited, noisy, and demographically biased. I focus on using self-supervised speech representations and generative audio models to reduce dependence on labeled data and improve generalization across speakers and recording conditions. In particular, I study label-free and weakly supervised adaptation techniques, including voice conversion–based data augmentation and test-time adaptation, while keeping the pipeline aligned with clinical constraints such as sensitivity, uncertainty, and failure modes. The goal is to move voice-based mental health models from controlled benchmarks toward reliable, deployable systems that engineers can reason about, evaluate, and integrate into real products.