Short-courses & Workshops
Short-courses and workshops will be held on Thursday, September 15th.
MIKON 2022
You are invited to propose a tutorial for MIKON-2022 attendees.
Please contact TPC Co-Chair – Adam Lamecki adam.lamecki@ieee.org with your proposal
– title, presenter name(s) and short bio(s), 1000 words abstract.
Short Course on EM modelling
Abstract:
The aim of the Short Course on electromagnetic modelling is to cover fundamental and practical issues of computer modelling with different numerical methods. The Short Course will present to its attendees an overview of modelling techniques. The focus will be given to two most popular discrete numerical methods: Finite Element Method and Finite Difference Time Domain method, addressing fundamentals, advantages, and basic causes of errors. The theoretical lectures will be supported with presentation of successful practical and industrial user cases on application of electromagnetic modelling, dedicated to variety of research areas, and hands-on sessions, delivering practical experience with two different electromagnetic solvers, QuickWave and InventSim, based on FDTD and FEM methods. Practical sessions will be focused on filters and antennas (including ultra-fast analysis of Body of Revolution problems, e.g. dual reflector antennas) design and analysis, with highlights concerned with other devices. The attendees will work with the solvers, preparing a predefined simulation scenarios, running the simulation, exploring basic and advanced solvers’ capabilities, and interpreting simulation results.
The Short Course attendees will be provided with a three-month trial licence of full-capability QuickWave software and one month evaluation licence of InventSim.
Workshop on Microwave and Millimetre-Wave Characterization of Dielectric Sheets
Abstract:
The aim of this Workshop is to present state-of-the-art and novel achievements in microwave and millimetre-wave characterization of dielectric sheets in the 1-110 GHz range with various resonant methods. At first, the efforts on benchmarking currently available material characterization methods, as recently undertaken by the international scientific and industrial community associated within the iNEMI 5G/mmWave Materials Assessment and Characterization project Consortium, will be addressed. The aim of the project was to identify advantages and limitations of the selected methods, determine possible gaps for extending these methods to 5G/mmWave frequencies, as well as to develop reliable reference standard materials for set-up and calibration. The measurement results obtained with such resonant methods as split-post dielectric resonators (SPDRs), Fabry-Perot open resonators (FPORs), split cavity resonators (SCR), and balanced-type circular-disk resonators (BCDR) will be presented and the project impact will be discussed.
In a subsequent part of the Workshop, the attention will be paid to measurement techniques based on SPDRs and FPORs addressing their features, advantages and limitations as well as practical measurement aspects. Point-wise measurements with SPDRs will be discussed together with surface imaging of the complex permittivity, run with a fully-automated 2D SPDR scanner. In case of the FPOR, two geometries will be presented, namely, double-concave and plano-concave, pointing out major differences between them in terms of the measurable types of the samples. Error and uncertainty budgets of both SPDRs and FPORs will also be discussed in details. In particular, it will be shown how the thickness and Q-factor uncertainties affect dielectric constant and loss tangent uncertainties, respectively.
Lectures will be supported with hands-on training, giving the opportunity of gaining practical insight into material characterization with SPDRs and FPORs as given directly by their vendors and developers.
IRS 2022
You are invited to propose a tutorial for IRS-2022 attendees.
Please contact TPC Chair – Jacek Misiurewicz jmisiure@elka.pw.edu.pl with your proposal
– title, presenter name(s) and short bio(s), 1000 words abstract.
Beyond 5G Integrated Sensing and Communications
Duration:
Half-day (two slots) with a 10-minute break in between
Where & When:
To be defined
Kumar Vijay Mishra Dr. Ing.
United States Army Research Laboratory
M. R. Bhavani Shankar Dr. Ing.
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg
Abstract:
Today’s cellular networks are at a crossroads while moving from the current 4G cellular networks used for content delivery to the upcoming 5G networks that will provide services with low latency, high security, and high throughput. At the same time, a crunch in spectrum usage implies that such high data networks must coexist with the radar sensing systems of the future. In this tutorial, we present fundamental challenges in enabling a crucial tradeoff between sensing/radar and communications functionalities in beyond 5G (B5G) systems. In particular, the automotive sector has recently witnessed concerted and intense efforts towards realizing the joint radar-communications (JRC) systems for efficient utilization of limited electromagnetic spectrum at millimeter-wave (mm-Wave). This band is characterized by severe penetration losses, short coherence times, and the availability of wide bandwidth. While wide bandwidth is useful in attaining high vehicular communications data rates and high-resolution automotive radar, the losses must be compensated by using a large number of antennas at the transmitter and receiver. In this context, there is also recent research focus on joint multiple-input multiple-output (MIMO)-Radar-MIMO-Communications (MRMC) systems, where the antenna positions of radar and communications are shared with each other.
These synergistic approaches that exploit the interplay between state sensing and communication are both driving factors and opportunities for many current signal processing and information-theoretic techniques. For example, while there are still many open challenges at mm-Wave JRC, it is already a precursor to sub-mm-Wave or Terahertz (THz) JRC, where futuristic short-range THz communications would coexist with low-THF (.1-1THz) automotive and imaging radars. At present, THz band is witnessing developments such as ultra-massive-MIMO systems which employ thousands of antennas in a few cms of aperture. Imaging with low-THz automotive radar is currently being investigated. Joint sensing-communications is also a growing area for unmanned aerial vehicles (UAVs) such as drones. Building on the existing approaches, the tutorial focuses on highlighting emerging scenarios in collaborative and joint sensing and communications systems, particularly at mm-Wave and THz frequencies, highly dynamic vehicular environments, distributed radar-communications networks, and aerial channels, that would benefit from information exchange between the two systems. It presents the architectures, possible methodologies for mutually beneficial co-existence as separate entities or as a joint module and presents some recent results. The avenues discussed in the tutorial offer rich research potential while also enabling innovative plug-and-play methodologies for co-existence and co-design.
Deep Learning for Synthetic Aperture Radar Target Recognition and Image Interpretation
Duration:
Quarter-Day (2 hours)
Where & When:
To be defined
Prof. Feng Xu
Fudan University
Abstract:
In the big data era of earth observation, deep learning and other data mining technologies become critical to successful end applications. Deep learning technology has revolutionized the computer vision areas, and is gradually being applied in radar remote sensing. Over the past several years, there has been exponentially increasing interests related to deep learning techniques applied to synthetic aperture radar (SAR) imagery. However, there are issues that are specific to SAR image interpretation such as limited training samples, sensitivity to observation configuration, or weak generalization ability. There are some techniques that can be used to mitigate these issues such as fusing electromagnetic physics laws with deep neural networks, using prior constraints of physical laws to realize few-shot learning capability, etc. This tutorial reports the recent progresses of the author and collaborators in this area.
The first part of the tutorial briefly introduces the theory of deep learning, including the principles of deep neural networks, the backpropagation algorithms, programming toolboxes, etc. The second part of the tutorial introduces many cases of application including SAR automatic target recognition, polarimetric classification, image segmentation, few-shot/zero-shot learning, target reconstruction, etc. In each case study, it will also introduce the used novel advanced deep learning method which are specially designed to tackle the challenges that are specific to SAR data, e.g. EM-simulation-aided zero-shot learning, adversarial auto-encoder networks for SAR image generation, differentiable SAR renderer for target reconstruction, physics-inspired neural networks for electromagnetic problems. Finally, it also discusses the future development of SAR intelligent interpretation and microwave vision technology.
Participants are expected to understand the basic theory for deep neural networks including convolutional neural network, backpropagation algorithm, etc., and learn the relevant skills for SAR image interpretation with deep learning. In the meantime, participants will get to know some recent progresses in the this area and understand how deep learning techniques can be adapted for the specific domain-relevant problems.
Surveillance of drones and birds with staring radar
Duration:
Quarter-Day (2hrs)
Where & When:
To be defined
Mohammed Jahangir
Microwave Integrated Systems Laboratory (MISL), University of Birmingham
Michail Antoniu
University of Birmingham
Abstract:
In recent years the number of drones operating at low altitudes has increased immensely that has brought about the growth in the demand for radar that can provide round the clock surveillance for small airborne targets. Staring radar utilizes extended dwell on target in order to detect small drones but such systems are equally effective at detecting birds which can result in confuser targets. A deeper understanding at the signature level is central to the ability to develop effective discriminators that can distinguish drones from birds. At University of Birmingham (UoB) a dedicated facility of two networked staring radars has been set up to provide data to support machine learning techniques for classification of targets and mapping and characterising low to medium airspace in an urban setting.
This workshop will briefly outline the motivation for small target detection using a surveillance radar, introduce the basic radar principles of the staring radar and illustrate the working of the radar equation to derive a generic signal-to-noise ratio for a small target. This will be followed by a description of the staring radar testbed at UoB and the process set up for conducting control trials with drones and birds along with techniques developed for obtaining labelled data for opportune birds. Numerous examples will be provided of real radar measurements of drones and birds, along with a discussion of a number of machine learning techniques. The latter will also review modelling approaches for target signatures and future direction for classification. Finally, latest techniques developed for longer term monitoring of bird movement in low altitude airspace will be discussed to highlight the potential benefit to other applications such as aeroecology.
This workshop will enable the participants to gain an appreciation of the potential and limitation for detecting and classifying small airborne targets with a staring radar. They will benefit from gaining some insight into both the future direction for the emerging processing techniques and innovation in radar architecture for networked systems.
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CONTACT US / Conference office
Rafal LECH – MRW-2022 ORG. COMM. CO-CHAIR
e-mail: dr.rafal.lech@ieee.org
Microwave and Radiolocation Foundation
Nowowiejska 15/19; 00-665 Warsaw; Poland
phone: +48 22 234 7622;
e-mail: fmikon@ire.pw.edu.pl
website: http://www.ire.pw.edu.pl/fmikon/