Name - Guy Moshe Atias Name : Daniel Sheftel Name Fatema Abu Elheija Name : Dor Krief Name: Yuval Hadad

Student full name - Guy Moshe Atias Seminar Object - Asynchronous Swarm-Intelligence Navigation for GPS-Denied IMU-Only Drone Swarms. Research supervisor - Professor Igal Bilik Abstract - This work presents a low-cost, fully asynchronous GPS-denied navigation framework for drone swarms, leveraging IMU measurements, lightweight peer-to-peer communication, swarm intelligence, and error-correction to enable accurate cooperative positioning for rapid reconnaissance and disaster- area scanning Name : Daniel Sheftel Degree : M.Sc. in Computer and Electrical Engineering Supervisor : Prof. Mor Peretz Seminar topic : Design and control of high frequency capacitive power transfer systems: Capacitive Power Transfer (CPT) systems require defined design and control mechanisms to maintain operational stability and enable spatial freedom between the coupling interface. This seminar examines the double-sided LCLC compensation topology, focusing on the mathematical modeling required to quantify steady-state behavior and dynamic responses. The presentation details the derivation of small-signal transfer models, establishing the necessity for closed-loop regulation to mitigate the effects of variable coupling parameters and hardware detuning. Furthermore, a specific control methodology is introduced, demonstrating the use of a continuous-conduction buck converter to dynamically modulate the equivalent output resistance of the LCLC network. This approach provides an active mechanism to regulate power transfer capabilities and maintain system authority across varying load conditions and spatial misalignments. Fatema Abu Elheija Master’s Student, Department of Electrical and Computer Engineering Supervisor :Gil Shalev. Seminar name : Chemical Field-Effect Transistor for the Specific and Label-free Detection of Sodium Hypochlorite Solution Abstract 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. Name : Dor Krief Degree : M.Sc. in Computer and Electrical Engineering Supervisor : Prof. Mor Peretz Seminar topic : Generic hardware platform for loads energy management using Smart Algorithms This research presents a unique and robust hardware platform designed for centralized energy management in multi-voltage systems, such as modern "smart soldier" platforms. The study addresses the inefficiencies of legacy power distribution-characterized by redundant energy storage, excessive weight, and double-conversion losses-by implementing a novel centralized architecture , that allows both battery charging and load supply at the same time . The platform utilizes smart algorithms to enable real-time, continuous power supply, predictive maintenance, and efficient energy shuttling between numerous loads. By integrating a distributed conversion approach and advanced data logging, the research aims to significantly extend battery lifetime, reduce system volume, and optimize power density for mission-critical applications. Student: Maor Kakun Program: M.Sc. Electrical Engineering (Signal Processing Specialization) Advisor: Professor Yitzhak Yitzhaky Research Subject: Assessing Emotional States from Facial Videos Using Remote Photoplethysmography. Research Overview: 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 rec Yuval Hadad Electrical Engineer Department Igal Bilik Topic: Adversarial Attack on Automotive Radar Point Cloud Classifiers Summary: Automotive radar systems have become critical components in autonomous driving, providing robust object detection capabilities across diverse weather conditions and lighting scenarios. This thesis investigates the vulnerability of deep learning based radar point cloud classifiers to adversarial attacks, addressing a critical gap in automotive safety research. The research is presented in two main contributions. First, we introduce the Radar PointNet architecture, specifically designed for automotive radar point cloud classification. R PointNet extends the conventional PointNet architecture by incorporating radar specific features including radial velocity, radar cross section values, and range information, enabling more effective classification of vehicles, motorcycles, and pedestrians. Second, we propose a comprehensive adversarial attack framework targeting radar based perception systems. Our initial approach introduces the Radar Car lini and Wagner attack, demonstrating that even small perturbations to radar point clouds can significantly degrade classification performance. Building on this foundation, we develop a novel group wise adversarial attack methodology that generates physically plausible perturbations by applying structured affine transformations to spatially coherent point groups. The group wise attack framework reduces optimization complexity from 256 to 100 learnable parameters for typical point clouds while paradoxically improving both attack effectiveness and imperceptibility. We introduce two grouping strategies: frame based grouping when temporal metadata is available, and PCA based grouping that approximates temporal structure using only spatial coordinates. Both methods achieve approximately 19% attack success rates while maintaining superior imperceptibility compared to unstructured baselines. Experimental evaluation on the RadarScenes dataset demonstrates R-PointNet achieves 94% classification accuracy compared to 60% for conventional PointNet. The adversarial attacks reveal critical vulnerabilities, with motorcycle targets showing highest susceptibility (54.5% attack success rate) and even well i ii classified vehicle categories experiencing 14.7% vulnerability. These findings underscore the urgent need for robust defense mechanisms in automotive radar perception systems before widespread deployment in safety critical autonomous driving applications. The research contributes both to advancing radar based deep learning architectures and to understanding their security vulnerabilities, providing essential groundwork for developing more resilient automotive perception systems