(The talk will be given in English)

Speaker:     Prof. Tirza Routtenberg

                               Department of Electrical and Computer Engineering, Ben Gurion University 

Monday, November 11^th, 2024

12:00 - 13:00

Statistical Graph Signal Processing with Applications to Smart Grids

Abstract

Graphs are fundamental mathematical structures that are widely used in various fields for network data analysis to model complex relationships within and between data, signals, and processes. In particular, graph signals arise in many modern applications, leading to the emergence of the area of graph signal processing (GSP) in the last decade. GSP theory extends concepts and techniques from traditional digital signal processing (DSP) to data indexed by generic graphs, including the graph Fourier transform (GFT), graph filter design, and sampling and recovery of graph signals. However, most of the research effort in this field has been devoted to the purely deterministic setting. In this study, we consider the extension of statistical signal processing (SSP) theory by developing graph SSP (GSSP) methods and bounds. Special attention will be given to the development of GSP methods for monitoring the power systems, which has significant practical importance, in addition to its contribution to the enrichment of theoretical GSSP tools. In particular, we will discuss the following problems (as time permits): 1) Bayesian estimation of graph signals in non-linear models; 2) the identification of edge disconnections in networks based on graph filter representation; 3) the development of performance bounds, such as the well-known Cramér-Rao bound (CRB), on the performance in various estimation problems that are related to the graph structure; 4) the detection of false data injected (FDI) attacks on the power systems by GSP tools; 5) Laplacian learning with applications to admittance matrix estimation. The methods developed in these works use GSP concepts, such as graph spectrum, GSP, graph filters, and sampling over graphs.

Short Bio

Tirza Routtenberg is an Associate Professor at the School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel. She received her B.Sc. from the Technion in 2005, and her M.Sc. and Ph.D. in Electrical Engineering from Ben-Gurion University in 2007 and 2012, respectively. From 2012 to 2014, she was a Postdoctoral Fellow at Cornell University, and in 2022–2023, she served as the William R. Kenan, Jr. Visiting Professor for Distinguished Teaching at Princeton University. Her research interests include statistical signal processing, estimation and detection theory, signal processing on graphs, and applications in smart grids. She has received several awards, including the Toronto Prize for Excellence in Research in 2021 and four Best Student Paper Awards coauthor at international IEEE conferences.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בטופס הנוכחות שיועבר באולם במהלך הסמינר

Electrical Engineering Systems Seminar

Speaker: Elad Fisher

M.Sc. student under the supervision of Prof. Guy Even

Wednesday, 6^th November 2024, at 15:30

Room 011, Kitot Building, Faculty of Engineering

Dense 5G Backhaul Network Planning, Including: Routing, Frequency/Power Assignment, and Fault Tolerance

Abstract

My thesis deals with the problem of designing a wireless backhaul network for dense 5G networks. The task of designing a backhaul network involves routing and assignment of frequencies as well as assigning transmission powers to links. In addition, we consider the problem of designing a backhaul network that is resilient to single-link and single-base station failures. The objectives of the backhaul design problem are to minimize the number of frequency bands used in the frequency assignment as well as minimize the number of antenna pairs (each antenna pair can support two anti-parallel links).

As the baseline algorithm for backhaul design, we consider: (1) an algorithm that performs routing based rounding of a solution to a min-cost multi-commodity flow problem, and (2) an algorithm that assigns frequencies to links using a greedy first-fit algorithm.

In the thesis, we experiment with various options to modify the linear program that solves the multi-commodity flow problem by adding integer constraints, resulting in a mixed-integer linear programming problem.

The fractional solutions of the optimization problems are rounded to obtain a routing.

Based on the mixed-integer linear program, we present a routing algorithm that reduces the number of antenna pairs used. Additionally, we introduce two iterative algorithms that alternate between routing and frequency assignment. One of these algorithms reduces the number of frequency bands used by iterating between routing using the min-cost multi-commodity flow linear program and assigning frequencies using the greedy first-fit algorithm, while adjusting link costs between iterations. The second algorithm reduces the number of frequency bands and antenna pairs used by iterating between routing and assigning frequencies using the greedy first-fit algorithm, while adding integer constraints for sets of links with high interference among them.

We present two algorithms that provide tolerance to single-link failures. We experiment with the effect of using our routing/iterative algorithms on the objectives. Our experiments demonstrate that our algorithms compared to the baseline, on average, reduce the number of frequency bands by 15% or reduce the number of antenna pairs by 5%.

In our experiments, our algorithms design a backhaul network with 91-231 base stations and 9-25 gateways within an hour.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בדף הנוכחות שיועבר באולם במהלך הסמינר

Electrical Engineering Systems Seminar

Speaker: Dana Wizner

Ph.D. student under the supervision of Prof. Raja Giryes

Monday, 28^th October 2024, at 15:00

ZOOM Seminar

Low Rank Models: From Signal Processing to Deep Learning Theory

Abstract

Recently, our world has entered the age of “Big Data.” We are now facing the challenge, and opportunity, of processing massive amounts of data while being able to uncover the information in it, buried as low-dimensional structures. To this end, we would like to explore the world of low-rank and sparse models, with an emphasis on theoretic aspects of real-world applications.

In this seminar, we study a variety of problems where a low rank or sparse prior arises naturally, and helps us to better explain observed phenomena or to simplify the common models. First, we study joint blind deconvolution and demixing, cast as a separable low-rank retrieval problem. Next, we explain the vulnerability of neural networks to universal and transferable adversarial attacks through the framework of sparsity. In another line of research we bridge between two important concepts: the Neural Tangent Kernel (NTK), which captures the evolution of deep neural networks (DNNs) during training, and the Neural Collapse (NC) phenomenon, which refers to the emergence of symmetry in DNNs, and prove the emergence of NC in DNNs with block-structured NTK. Finally, we study diffusion models as "correlation machines", where we analyze linear diffusion, and show a connection to the spiked covariance model and the power iteration method.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום בצ'ט של שם מלא + מספר ת.ז.