Tutorials

This tutorial is a gentle introduction to the fundamental concepts of quantum radar. Such radars have been garnering increasing interest since 2019, when a quantum radar prototype was demonstrated in the laboratory for the first time. Attendees will learn how basic quantum mechanical concepts, such as the Heisenberg uncertainty principle, intertwine with basic radar detection theory to give rise to the field of quantum radar. By the end of the tutorial, attendees will understand the concepts needed to follow the latest developments in quantum radar—and quantum sensing in general.

Length: 1.5 Hours
Type of Tutorial: Lecture

These tutorials will introduce the participants to the Stone Soup library and demonstrate how to setup a multi-target scenario and utilize the built-in multi-target tracking algorithms to produce state estimates of the targets in the presence of false alarms. The tutorial will consist of a brief presentation outlining the library and the scenario, followed by a hands-on section where participants can follow along and gain a deeper understanding of how the parameters of the simulation and tracking algorithm affect the overall track quality.

Length: 1.5 Hours
Type of Lecture: Lecture with Hands On
Attendees will need to bring their laptop to participate in the hands-on activity 

The recent rise of Machine Learning (ML) has caused an intertwined relationship between Sensing and ML: sensors are used to collect data, which are then used to train ML models, which in turn are used in sensing systems. The applications are vast: medical diagnosis, surveillance, fault detection, condition monitoring, digital twins, etc. Uncertainty, which is a fundamental component of measurement and sensing, must be quantified for risk management and better decision making. Unfortunately, many researchers and practitioners overlook uncertainty, making their sensing systems practically unadoptable because they perform less than satisfactory in a real-world setting. In this tutorial, we will give an overview of measurement fundamentals including uncertainty, and cover relevant standards such as VIM and GUM. We then show how ML is used for indirect measurement and sensing, and how to quantify, communicate and visualize the uncertainty of ML-assisted measurements and sensing, in order to design more reliable and trustworthy sensing systems. We will cover both ML regression and ML classification. We will also cover the terminology confusion between the ML and the Measurement communities, as well as common mistakes such as considering misclassification probability as uncertainty, or the oxymoron of high accuracy and low precision. Finally, we go over a few specific examples. 

Length: 3 Hours
Type of Tutorial: Lecture with Hands On
Attendees will need to bring their laptop to participate in the hands-on activity 

This introductory tutorial addresses problems of storing and/or multilinear processing of very large multidimensional data arrays (tensors) that could possibly result from multi-sensor measurements. After introducing basic tensor operations, we will cover both theoretical and computational aspects of two classic tensor decompositions: Canonical Polyadic Decomposition (CPD) and Tucker Decomposition (TD). We will also introduce the concept of Tensor Networks, with particular emphasis on the relatively recent Tensor Train Decomposition (TTD).  

Length: 3 Hours
Type of Lecture: Lecture Only