Electrical Engineering Systems Seminar

Speaker: Shoval Mishal

M.Sc. student under the supervision of Prof. Shai Avidan

Wednesday, 13^th November 2024, at 15:00

Room 011, Kitot Building, Faculty of Engineering

Is There a Needle in the Haystack?

Abstract

We are interested in detecting the existence of novel object classes in aerial images, without specifying these novel classes ahead of time.

In our setting, we are equipped with a detector capable of detecting a closed set of objects (e.g., vehicles, planes) but wish to determine if other, unspecified, object classes, that are of interest (say, ships), appear in the images as well. This open vocabulary problem poses two challenges. The first is scale, as there are tens of millions of patches to evaluate. The second is vagueness. How do you determine whether a given patch contains a semantically meaningful object of interest or just an interesting background pattern?

To address these challenges, we propose a funnel approach that gradually reduces the number of patches of interest from tens of millions to a short list of few tens of thousands. The patches in the short list are ranked automatically and shown to a human operator. We therefore measure performance by ``Time-To-1st(TT-1), i.e. the time it takes a human to find the first instance of interesting new classes in aerial images, and show we are capable of producing such a sample within the first few patches. Our code will be made publicly available.

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

  -סמינר זה יחשב כסמינר שמיעה לתלמידי תואר שני ושלישי-  This Seminar Is Considered A Hearing Seminar For Msc/Phd Students-

(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