Name: Maor Kakun Advisor: Professor Yitzhak Yitzhaky Name  : Fatema Abu Elheija  Supervisor :Gil Shalev. Name: Shani Budilovsky Supervisors: Dr. Yehuda Ben-Shimol and Prof. Itshak Lapidot

Abstract: Maor Kakun This research investigates emotion recognition using Remote Photoplethysmography (rPPG) signals extracted from facial videos. rPPG estimates the blood volume pulse by tracking subtle temporal variations in facial pixel intensities, enabling non-contact physiological measurement. Extracting high-quality rPPG signals under motion artefacts, video compression, and challenging illumination conditions remains difficult. Current state-of-the-art approaches rely on large neural network models that operate as black boxes, directly mapping video frames to rPPG signals with high computational cost and limited interpretability. This study proposes a hybrid framework that combines classical signal processing techniques, physically interpretable feature extraction, and a lightweight deep learning architecture. The proposed approach reduces computational complexity while improving model explainability and robustness across varying recording conditions. Using the resulting high-quality rPPG signals, reliable heart rate variability features are obtained, enabling effective emotion recognition. Abstract: Fatema Abu Elheija Rapid detection of toxic chemical species is important for environmental monitoring and chemical safety. In this work, we demonstrate label-free, real-time, and quantitative detection of sodium hypochlorite solution (SHC) using a Meta-Nano-Channel Chemical Field-Effect Transistor (MNC ChemFET) in PBS solution. The SiO₂ sensing surface is functionalized with an indole-based recognition layer that selectively reacts with hypochlorite molecules, producing changes in interfacial surface charge that modulate the transistor’s electrical response. The multi-channel MNC structure enhances electrostatic coupling and improves sensitivity and signal stability under electrolyte conditions. Calibration and control measurements confirm quantitative sensing performance, selectivity, and operational stability. Abstract: Shani Budilovsky Automatic Speaker Verification (ASV) is a biometric authentication technology that verifies a speaker’s identity based on their voice characteristics and is widely used in security-sensitive applications such as access control and identity verification. Despite their success, ASV systems are vulnerable to spoofing attacks, including replay, synthetic, and manipulated speech. To address this challenge, Spoofing-robust ASV (SASV) systems combine speaker verification models with dedicated spoofing countermeasures to jointly improve verification accuracy and attack resistance. Data augmentation has become a key technique for improving the generalization and robustness of deep learning-based ASV and countermeasure models by increasing training data diversity and simulating real-world acoustic variability. Augmentation methods can be applied at the waveform level or at the time-frequency representation level, such as spectrogram-based techniques. However, in the SASV setting, not all augmentation strategies are equally beneficial, and some may improve speaker discrimination while degrading spoof detection performance, or vice versa. This seminar examines the effect of different data augmentation strategies on both ASV and spoofing-robust ASV systems, and discusses the importance of carefully designing augmentation pipelines that balance speaker verification accuracy with spoofing robustness in practical SASV systems