The past decade has witnessed tremendous progress in microwave radar sensors that use wireless signals measure various human life activities. Compatible with modern semiconductor process, this non-contact technology is projected to take important roles of in-patient and out-patient healthcare, assisted living, and human-machine interface. This talk reviews recent advancement on radar sensing technologies including RF/microwave front-end development, system integration, and signal processing. Based on the state-of-the-art engineering technologies, exciting achievements made by researchers worldwide in both engineering lab and pre-clinical/clinical environments will be discussed, including patient monitoring, human behavior recognition, pedestrian tracking, non-contact blood pressure measurement, speech recognition, and sleep medicine. Contributions from both academic and industrial R&D groups will be included. After discussing the current challenges facing scientists and practitioners, future research directions will be laid out for ubiquitous deployment of smart radar sensors at the human-microwave frontier.

This keynote talk will introduce the concept of Power over Fiber (PoF) and potential applications envisioned of that technology in support of beyond 5G networks with optical fronthauling using different types of optical fibers from single mode fibers to multicore fibers with spatial division multiplexing capabilities. It will cover dedicated and shared scenarios showing different tests on Analog Radio over Fiber transmission for different modulation formats compliant with 5G New Radio (NR) standard. All as part of works in 6G_Xtreme, H2020 BlueSPACE (https://bluespace-5gppp.squarespace.com/news), and OF-SGOALS. It will be an open forum to discuss about the potential of PoF fostering the pending energy efficiency strategy in 5G networks and beyond.

This talk will discuss emerging trends for open networking including motivations and challenges. It will specifically address methodologies for solutions’ co-creation across technological disciplines and end-users, as well as the opportunity to drive continuous evolution and renewal. Last, it will debate the need to innovate responsibly and develop system-level approaches to address climate change and other key societal challenges as part of the telecommunications sector innovation thinking.

The increasing high requirements of wireless communications and sensors are making research and commercialization of millimeter-wave integrated circuits and antennas experience tremendous growth. The advancement of modern CMOS technology facilitates it to become the prevailing technology to achieve low-cost and highly-integrated millimeter-wave integrated circuits. Meanwhile, the compound semiconductor is still a must for low noise and high power millimeter-wave system. As the operating frequency enters the millimeter-wave regime, the circuit component's size becomes comparable to the electromagnetic wave wavelength. Therefore, a mixed design methodology using both the lumped and distributed elements in the millimeter-wave integrated circuit design is of great interest, not only compound semiconductor but also CMOS integrated circuits. On one hand, to achieve high-integration and high-performance, the heterogeneous packaging architecture to combined the merits of both CMOS and compound semiconductor millimeter-wave integrated circuits is becoming an attractive technology. On the other hand, considering the efficiency, cost, and integration of advanced wireless systems, discrete antenna is no longer suitable for millimeter-wave wireless systems. Therefore, antenna-in-package (AiP) has become the mainstream for millimeter-wave system, which implements an antenna or antennas on (or in) package of chips leading to a high efficiency and highly-integrated radio. In the talk, innovative design approaches and methodologies on millimeter-wave integrated circuits, subsystems and corresponding antenna-in-package will be introduced.

Plasmonic materials have a wide range of applications, from energy storage and harvesting to bio-sensing and memory storage devices. However, the archetypal plasmonic materials gold and silver are limited in their applicability, displaying poor thermal stability, limited spectral tunability, and incompatibility with standard CMOS fabrication processes.
Consequently, refractory plasmonic materials are capable of withstanding high operating temperatures and can include refractory metal elements (e.g. W, Mo, Ti) in addition to transition metal oxides and nitrides (e.g SrMoO3, SrNbO3, SrRuO3, TiN, NbN). Transition metal oxides (TMOs) and transition metal nitrides (TMNs) are of particular interest as they are capable of delivering tailorable optical properties via deposition-controlled variations in film stoichiometry, morphology and strain. Of the TMNs investigated, titanium nitride (TiN) has been the subject of recent research as its optical constants are comparable to gold and it also displays high-temperature stability and a tuneable plasma frequency. However, other binary and ternary TMNs including NbN, TaN, ZrN and TiZrN also hold promise for use within plasmonic applications at varying wavelengths and operating conditions.
In this paper, the mechanism of formation of transition metal nitride and oxide thin films and their optical properties with tunable epsilon-near-zero (ENZ) behaviour will be discussed. We will present the technological conditions for the deposition of thin films with unusual double ENZ frequencies and will show that they can be modified by changing the film deposition conditions. Thus allowing one to fabricate, control and engineer tunable plasmonic and metamaterial devices and surfaces, using CMOS-compatible technology.

In this talk, we will review some of our recent work on the benefits of machine learning techniques to optical communication and photonic systems in general. It will be demonstrated that machine learning techniques based on auto-encoders are effective in learning the optimum symbol mapping for highly complex communication channels resulting in an improved performance compared to the state-of-the-art. Moreover, the framework of machine learning based inverse system design will be presented and experimentally demonstrated for the design of ultra-wide band Raman amplifier. Finally, it will be shown that Bayesian inference in combination with Monte Carlo Markov Chain (MCMC) and Expectation Maximization (EM) algorithm results in a record sensitive optical phase tracking which can be a game changer for quantum metrology and optical frequency comb noise characterization.