ERC FUNDED PROJECTS

The aim of this project is the realisation of efficient mid-infrared and THz optoelectronic emitters. This work is motivated by the fact that the spontaneous emission in this frequency range is characterized by an extremely long lifetime when compared to non-radiative processes, giving rise to devices with very low quantum efficiency. To this end we want to develop hybrid light-matter systems, already well known in quantum optics, within optoelectronics devices, that will be driven by electrical injection. With this project we want to extend the field of optoelectronics by introducing some of the concepts of quantum optic, particularly the light-matter strong coupling, into semiconductor devices. More precisely this project aims at the implementation of novel optoelectronic emitters operating in the strong coupling regime between an intersubband excitation of a two-dimensional electron gas and a microcavity photonic mode. The quasiparticles issued from this coupling are called intersubband polaritons. The major difficulties and challenges of this project, do not lay in the observation of these quantum effects, but in their exploitation for a specific function, in particular an efficient electrical to optical conversion. To obtain efficient quantum emitters in the THz frequency range we will follow two different approaches: - In the first case we will try to exploit the additional characteristic time of the system introduced by the light-matter interaction in the strong (or ultra-strong) coupling regime. - The second approach will exploit the fact that, under certain conditions, intersubband polaritons have a bosonic character; as a consequence they can undergo stimulated scattering, giving rise to polaritons lasers as it has been shown for excitonic polaritons.

1 761 000 €

Start date: 2010-05-01, End date: 2015-04-30

Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.

2 499 595 €

Start date: 2017-10-01, End date: 2022-09-30

Redox chemistry provides the fundamental basis for numerous energy-related electrochemical devices, among which Li-ion batteries (LIB) have become the premier energy storage technology for portable electronics and vehicle electrification. Throughout its history, LIB technology has relied on cationic redox reactions as the sole source of energy storage capacity. This is no longer true. In 2013 we demonstrated that Li-driven reversible formation of (O2)n peroxo-groups in new layered oxides led to extraordinary increases in energy storage capacity. This finding, which is receiving worldwide attention, represents a transformational approach for creating advanced energy materials for not only energy storage, but also water splitting applications as both involve peroxo species. However, as is often the case with new discoveries, the fundamental science at work needs to be rationalized and understood. Specifically, what are the mechanisms for ion and electron transport in these Li-driven anionic redox reactions? To address these seminal questions and to widen the spectrum of materials (transition metal and anion) showing anionic redox chemistry, we propose a comprehensive research program that combines experimental and computational methods. The experimental methods include structural and electrochemical analyses (both ex-situ and in-situ), and computational modeling will be based on first-principles DFT for identifying the fundamental processes that enable anionic redox activity. The knowledge gained from these studies, in combination with our expertise in inorganic synthesis, will enable us to design a new generation of Li-ion battery materials that exhibit substantial increases (20 -30%) in energy storage capacity, with additional impacts on the development of Na-ion batteries and the design of water splitting catalysts, with the feasibility to surpass current water splitting efficiencies via novel (O2)n-based electrocatalysts.

2 249 196 €

Start date: 2015-10-01, End date: 2021-03-31

"During their first year of life, infants become attuned to the phonemes, words and phonological rules of their language, with little or no adult supervision. After 30 years of accumulated experimental results, we are still lacking an account for the puzzling fact that these 3 interdependent components of language are acquired not sequentially, but in parallel. Drawing tools from Machine Learning and Automatic Speech Recognition, we construct a model of this early process, test it on 2 large spontaneous speech databases (Japanese, French and Dutch) and test its predictions in infants using behavioral, EEGs and fNIRS techniques. 1. Coding. We study different ways of defining coding features for speech, from fine-grained to coarse grained, in view of the automatic discovery of a hierarchy of linguistic units. We compare this with a systematic study of the units of speech coding as they unfold in 6, 9 and 12 month old infants.. 2. Lexicon. Infants recognize some words before they know the phonemes of their language; we modify existing word segmentation algorithms so they can work on raw speech. We test the unique prediction that infants start with a large lexicon that’s quite different from the adult one. 3. Rules. Phonemes are produced as overlapping, coarticulated gestures. To untangle these context effects, we use a predictive model of coarticulation in auditory space and invert it. We test when and how infants perform reverse coarticulation. 4. Integration. The above subprojects provide only an initial bootstrapping into approximate phonemes, words, and contextual rules. We show how to iteratively integrate these approximate representations to derive better ones. The outcome will be numerically assessed on an adult directed and infant directed speech database, and compared to those of to state-of-the-art supervized phoneme recognizers. The predictions will be tested in infants learning artificial languages and in a longitudinal study."

2 194 557 €

Start date: 2012-11-01, End date: 2017-10-31