סמינר בית הספר להנדסת חשמל ומחשבים 23/06/2025
סמינר מחלקתי
Join Zoom Meeting: https://us02web.zoom.us/j/88100615987?pwd=saGw8Z8xdzpuo4N6vAoVHqOpNN8tj7.1
Meeting ID: 881 0061 5987
Passcode: 127896
13:10 Jawhar Desi – Ph.D student. Supervisor: Prof. Joseph Rosen
Title: Advanced methods of optical imaging with dynamic phase masks
Abstract: Incoherent digital imaging has gained popularity due to its vast potential for realistic applications. The invention of electro-optical devices and advanced computational techniques, the capabilities of incoherent digital imaging have significantly improved. Electro-optical devices facilitate the use of dynamic phase masks in imaging systems, a feat that was unattainable with conventional methods of fabricating phase masks. These innovative phase masks are essential for overcoming challenges and breaking the barriers of incoherent optical imaging. These phase masks are created digitally and incorporated into the imaging system using a spatial light modulator. The images captured by the camera undergo post-processing to achieve the final results
In this talk, we will explore incoherent imaging techniques developed for resolution enhancement, specifically ASAI and MIND. Additionally, we will see a technique 3DVIS that allows imaging of single or multiple planes within a volume, these techniques have opened new doors in this field of incoherent imaging
Supervisor: Prof. Joseph Rosen
Biography: Jawahar Desai is a Ph.D. student of Electro – optical engineering in the School of Electrical and Computer engineering at Ben Gurion University. He started his Ph.D. in October 2021. His research focuses on incoherent imaging techniques for resolution enhancement and 3D imaging
13:55 Shahar Villeval. Ph.D Student. Supervisor – Prof. Joseph Tabrikian & Dr. Igal Bilik TBD
14:40 Bar Hen – Ms.C Student Under the supervision of Prof. Gil Shalev
Title: Detection of a single Escherichia coli bacteria with an electronic biochip
Abstract: The early detection of bacterial contamination in a rapid, specific, and with minimal sample processing is a critical unmet need for global food safety and infectious disease prevention. Escherichia coli (E. coli), a widely used indicator of faecal contamination, poses significant health risks in its pathogenic forms and continues to challenge conventional detection methods, which are often slow, labour-intensive, and incompatible with complex samples. The study reports the Meta-Nano-Channel biological Field-Effect Transistor (MNC bioFET) for real-time, specific, label-free, quantitative and real-time sensing of E. coli in 0.5 µL of undiluted Lysogenic Broth (LB). The MNC bioFET is a fabricated in a complementary metal-oxide-semiconductor (CMOS) silicon-on-insulator (SOI) technology and biofunctionalized with monoclonal anti-E. coli O111:B4 LPS antibodies. I demonstrate E. coli sensing with a limit-of-detection (LOD) of single bacteria and with excellent sensitivity and linearity. Extensive control experiments and physical surface characterizations are performed. This work presents a breakthrough in E. coli biosensing and provides a solid foundation for future research on real-time microbial diagnostics
Keywords: E. coli sensing, BioFET, Real-time bacterial detection, Single-cell analysis, Label-free biosensor, Biological activity monitoring
15:05 Dor Patel Ms.C Student Supervisors: Prof. Joseph Tabrikian and Dr. Igal Bilik
Subject: Cognitive Waveform Design and Beamforming for MIMO-OFDM ISAC
Abstract: This work addresses the problem of cognitive integrated sensing and communications (ISAC) waveform design, which presents a challenging task due to the need to balance different optimization criteria for communications and sensing performances. In the proposed cognitive approach, transmit beamforming in each of the subcarriers is dynamically adapted based on past measurements in order to minimize the Bayesian Cramér-Rao bound (BCRB) on target parameters while satisfying the minimum required communication channel capacity. Simulation results demonstrate that the proposed adaptive waveform design achieves high localization accuracy with only a minor compromise in communication performance, effectively addressing the inherent trade-offs in ISAC systems
15:30 Naor Fadida Msc. Student. Supervisor: Dr Igal Bilik
Subject: Maneuvering Targets Tracking using Conformal Prediction
Abstract: Interacting multiple model (IMM) algorithms are commonly used for maneuvering target tracking. Conventionally, IMM relies on model probabilities computed from likelihood functions, which assume specific measurement noise distributions. However, these likelihoods can be unreliable in the case of model mismatch, non-Gaussian noise, or complex dynamics
This work introduces a novel data-driven approach to model scoring in IMM tracking using conformal prediction (CP). A separate CP-based neural network (CP-Net) is trained for each motion model, such as constant velocity, constant turn, constant acceleration, and others. This approach enables them to generate calibrated confidence intervals for future measurements based on past observations. During operation, each model’s robustness is evaluated by how well the actual measurement aligns with its predicted interval, considering both the interval width and the deviation from the predicted center. This yields adaptive, distribution-free scores that replace conventional likelihoods in the IMM mixing process, enhancing robustness to modeling uncertainties
Short bio: Mr. Naor Fadida is an M.Sc. researcher at Ben-Gurion University of the Negev under the supervision of Dr. Igal Bilik. He holds a B.Sc. in Electrical Engineering from the Technion – Israel Institute of Technology, and is currently pursuing an M.Sc. in Electrical Engineering. His research focuses on maneuvering target tracking
