Joint AP/MTT/EMC/GRS Societies | Distinguished Lecturer Workshop

A one-day workshop presenting talks by Distinguished Lecturers from IEEE AP-S, IEEE MTT-S, IEEE EMC-S and IEEE GRS-S along with opportunities for the audience to interact with the DLs in relaxed interactive networking sessions.

Programme:

• 9.00 – Coffee and Networking
• 9.30 – Opening and Welcome by Dr Masood Ur Rehman, VC IEEE UKRI AP-S/MTT-S Joint Chapter
• 9.40 – Opening talk: “From engineering electromagnetics to electromagnetic engineering: Training next generations” by Prof Levent Savgi, IEEE AP-S DLC Chair
• 10.30 – Keynote 1: “A next wave of wireless communication is here – How 6G will evolve and its enablers” by Prof. Qammer H. Abbasi, University of Glasgow, IEEE AP-S DL
• 11.20 – Keynote 2: “Additive manufacturing – Emerging opportunities for microwave components” by Prof Cristiano Tomassoni, IEEE MTT-S DL, University of Perugia

12.10 – 1.10 Lunch Break, Networking

• 1.10 – Keynote 3: “The antenna digital twin – When measurements and simulations unite” by Dr Benoit Derat, IEEE EMC-S DL, Rohde & Schwarz, Munich
• 2.00 – Keynote 4: “Artificial intelligence for earth observation” by Prof Mihai Datcu, IEEE GRS-S DL, POLITEHNICA Bucharest

2.50 – Coffee and Networking

• 3.05 – Keynote 5: “Towards mature AI-driven sensing – Aspects from a realisation perspective” by Prof Alfonso Farina, IEEE AES-S DL, Leonardo Radar & Sensors Academy
• 3.55 – Closing remarks by Dr. Hasan Abbas, Secretary IEEE UKRI AP-S/MTT-S Joint Chapter
• 4.00 – Lab tour
• 5.00 – End

Lectures:

A next wave of Wireless Communication is Here: How 6G will evolve and its enablers by Prof Qammer H. Abbasi

Future wireless networks are expected be more than allowing people, mobile devices, and objects to communicate with each other. The sixth generation (6G) of mobile networks are envisioned to include high data rate applications and ultra-massive, connected things. This also includes bio and nano-internet of things (IoT) tele-operated driving, unmanned mobility, haptic communications, unmanned aerial vehicles, and many more.

Given the size of nano-sensors, Terahertz (THz) frequency is proposed to do various sensing activities at this scale. However, it will be ideal to use the same radio frequency for communications as well. Furthermore, THz is also proposed as an enabler of extremely high data rate applications in 6G communications. The talk will be focused on enablers for 6G which includes i) Terahertz antenna design, ii) new technology, which is referred to as Reconfigurable Intelligent Surfaces (RISs), its design and application and iii) joint communication and sensing in 6G and state of the art in this area while focusing on healthcare application and iv) lastly role of quantum technologies in future 6G communication with experimental results of wireless communication inside the dilution fridge at 4K.

Additive Manufacturing: Emerging Opportunities for Microwave Components by Prof Cristiano Tomassoni

The Additive Manufacturing (AM) technology, also known as 3D-printing technology, offers several interesting and attractive features, including fast prototyping, geometry flexibility, easily customizable products, and low cost (in some cases). However, using such technologies for microwave devices is not straightforward as AM has not been specifically developed for microwave components, and in most cases, some adaptation and post-processing is necessary. Furthermore, there are many AM technologies available, and it is important to understand their characteristics before selecting one.

In the presentation, an overview of the different AM technologies available will be provided. Additionally, an analysis of some of the most common AM technologies used for the manufacturing of microwave components will be conducted in more detail, with the help of several examples. Several microwave components manufactured with some of the most popular AM technologies will be shown, along with a detailed description of the manufacturing process, post-processing, and all actions necessary to make the component perform well. Furthermore, it will be shown how the flexibility of this technology allows the development of new classes of components with non-conventional geometries that can be exploited to obtain high-performing components in terms of compactness, weight, losses, etc.

The Antenna Digital Twin – When Measurements and Simulations Unite by Dr Benoit Derat

Antenna or OTA measurements are blind. Knowing the detailed implementation of the device under test is not needed to realise them. They even include all production tolerances that might impact performance. However, measurements are limited to canonical test environments, eg in anechoic chambers.

Simulations, on the contrary, can give access to electromagnetic fields in practically any scenario. Yet, simulations are only as good as the knowledge of the very details of the radiation source. What if one could unite the two and benefit from the combined strengths of experimental and numerical techniques?

This talk shows how to enable this with the augmented OTA approach, involving the creation of an antenna digital twin, based on actual measured data. Practical applications are demonstrated, including field characterisation inside the car and EMF assessments with virtual human models.

Artificial Intelligence for Earth Observation by Prof Mihai Datcu, German Aerospace Centre DLR

The volume and variety of valuable Earth Observation (EO) images as well as non-EO related data is rapidly growing. The open free data access becomes widespread and has an enormous scientific and socio-economic relevance. EO images are acquired by sensors on satellite, suborbital or airborne platforms. They extend the observation beyond the visual information, gathering physical parameters of the observed scenes in a broad electromagnetic spectrum.

The sensed information depends largely on the imaging geometry, orbit, illumination and other specific parameters of the space instruments. Typical EO systems can be classified into optical or radar instruments.

In recent years, both types of sensors deliver widely different images, and both have made considerable progress in spatial and radiometric resolution, image acquisition strategies, and data rates.

Generally imaging sensors generate an isomorphic representation of the observed scene. This is not the case for EO, the observations are a doppelgänger of the scattered field, an indirect signature of the imaged object. This positions the load of EO image understanding, and the utmost challenge of Big EO Data Science, as new and particular challenge of Machine and Deep Learning and Artificial Intelligence (AI).

This presentation reviews and analyses the new approaches of EO imaging leveraging the recent advances in physical process based AI methods and signal processing, and leading to explainable paradigms where intelligence is the analytical component of the end-to-end sensor and Data Science chain design. A particular focus is on the semantic aspects as a key component in the explainable learning paradigms.

Towards Mature AI-driven Sensing – Aspects from a Realisation Perspective by Dr Alfonso Farina, Leonardo Radar & Sensors Academy

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.

AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.

AI is a key enabler of the Sustainable Development Goals (SDGs) set in 2015 by the international community as part of the UN 2030 Agenda for Sustainable Development through which countries of the world collectively pledged to eradicate poverty, find sustainable and inclusive development solutions, ensure everyone’s human rights, and generally make sure that no one is left behind by 2030.

This talk will delve deep into the definition of AI and learning from nature, the role of AI in sensing with an emphasis on Command & Control applications looking at dataset preparation and image processing and reflecting on AI points of criticism.