Name: Moshe Buchris Name: Amit Brilant Name: Ido Yakir

Name: Moshe Buchris Degree: MS.c in Electrical Engineering Supervisor: Prof. Haim Permuter Research topic: "Convolutional Unfolding Weighted Minimum Mean Square Error for MU-MIMO Downlink Beamforming" Summary: CUNNet, convolutional deep unfolding framework for the WMMSE beamforming algorithm in multi-user MIMO downlink systems. By learning structured precoder updates that exploit the spatial properties of the channel. CUNNet improves performance under fixed computational budgets while preserving the interpretability and stability of classical WMMSE. Name: Amit Brilant Department: Electrical and Computer Engineering Advisors: Prof. Ofer Hadar, Dr. Oshrit Hopper (External-Afeka) Seminar title: Detection and Diagnosis of Ovarian Carcinoma from Complex Cysts Using Computer Vision and Ultrasound Imaging Seminar summary: This research develops an AI system to assist physicians in detecting ovarian cancer from ultrasound images. The system analyzes transvaginal ultrasound scans from postmenopausal women and classifies ovarian findings as healthy, benign cysts, or malignant cysts. By combining image analysis with clinical patient data, the system aims to improve diagnostic accuracy, reduce physician subjectivity, and enable earlier detection of ovarian malignancies. Student: Ido Yakir, M.Sc in electrical engineering, electromagnetics and waves Supervisor: Prof. Reuven Shavit Seminar title: Neural-Network-Assisted Design of Conformal Metasurface Airborne Radomes Seminar abstract: Protective radomes often degrade antenna radiation performance through insertion loss and boresight errors, particularly in conformal shapes required for aerodynamic aircrafts. Conventional design methods using commercial EM software are computationally expensive for these electrically large structures and struggle with performance variability based on antenna orientation. This study proposes a conformal radome design utilizing a hybrid structure of dielectric and metasurface layers. To enable rapid optimization, the system is modeled using an equivalent transmission line circuit with shunt admittance and cascaded ABCD matrices, significantly reducing computational overhead compared to full-wave solvers. Furthermore, a neural network is employed to address performance variability. Trained on a dataset of unit cell parameters and transmission coefficients, the network predicts optimal geometries for each point on the radome. This approach allows for local compensation of aperture field distortions and minimization of insertion loss across multiple antenna orientations.