High throughput screening, which has been used for many years in the pharmaceutical field, is emerging as an effective method for accelerating materials discovery and as a new tool for elucidating composition-structure-functional property relationships. It is based on the rapid combinatorial synthesis of a large number of samples of different compositions, combined with rapid and...

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Antenna radiation control in terms of shape and polarization is a key element for future communication systems. Directive compact antennas offer new opportunities for wireless applications in terms of spatial selectivity and filtering. This leads to a reduction in electromagnetic pollution by mitigating interferences with other communication systems and reducing battery consumption in compact smart...

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In light of the growing demand for transmission capacity in communication networks, it is essential to explore innovative techniques that enhance spectral efficiency while maintaining the reliability and security of transmission links. This project proposes a comprehensive theoretical modeling of Faster-Than-Nyquist (FTN) systems, accompanied by simulations and numerical analyses to evaluate their performance in various...

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Semantic communications is an emerging and transformative research area, where the focus shifts from transmitting raw data to conveying meaningful information. While initial models and design solutions have laid foundational principles, they often rest on strong assumptions regarding the extraction, representation, and interpretation of semantic content. The advent of 6G networks introduces new challenges, particularly...

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This PhD research project focuses on pioneering foundational theories in semantic communications, specifically by advancing the compositional learning and reasoning capabilities of AI agents within multi-agent environments. The goal is to enable AI-driven systems to selectively extract, interpret, and exchange semantically meaningful information in a goal-oriented and context-sensitive manner, moving beyond traditional data-oriented communications. Integrated...

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The rise of machine learning models for processing sensor data has led to the development of Edge-AI, which aims to perform these data processing tasks locally, directly at the sensor level. This approach reduces the amount of data transmitted and eases the load on centralized computing centers, providing a solution to decrease the overall energy...

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In the last decade, sensor systems using Rydberg atoms have been seen as a breakthrough solution to the problems of receiving radio frequency (RF) electromagnetic waves. The characteristics of this type of receiver are promising: extreme sensitivity, very wide bandwidth, miniature size independent of wavelength and no coupling to the immediate environment. All these advantages...

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Next-generation wireless networks envision an unprecedented densification and an efficient use of the frequency spectrum in the near-millimeter-wave range (17-31 GHz). This context poses an urgent need for disruptive solutions enabling the minimization of the number of transceivers and their individual power consumption. Pioneering works have recently shown that a periodic temporal and spatial modulation...

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