Wireless Power Link Design for Both High-Power Inductive Coupling and Smart Metasurfaces Exploitation

As future technologies are going to be autonomous under the umbrella of the Internet of things (IoT) we can expect WPT to be the solution for intelligent devices. WPT has many industrial and medical applications both in the near-field and far-field domains. Considering the impact of WPT, this thesis is an attempt to design and realize both near-field and far-field WPT solutions for different application scenarios. A 27 MHz high frequency inductive wireless power link has been designed together with the Class-E switching inverter to compensate for the efficiency loss because of the varying weak coupling between transmitter and receiver because of their mutual misalignment. Then a system of three coils was introduced for SWIPT. The outer coil for WPT and the inner two coils were designed to fulfil the purpose of communication and testing, operating at frequencies different from the WPT coil. In addition to that, a trapping filter technique has also been adopted to ensure the EM isolation of the coils. Moreover, a split ring resonator-based dual polarization converter has been designed with good efficiency over a wide frequency range. The gap or cuts have been introduced in the adjacent sides of the square ring to make it a dual-polarization converter. The converter is also stable over a wide range of incident angles. Furthermore, a meta-element based intelligent surface has been designed to work in the reflection mode at 5 GHz. In this research activity, interdigital capacitors (IDCs) instead of ICs are introduced and a thin layer of the HfZrO between substrate and meta elements is placed whose response can be tuned and controlled with the applied voltage to achieve IRS.

Model selection and the vectorial misspecification-resistant information criterion in multivariate time series

The thesis deals with the problem of Model Selection (MS) motivated by information and prediction theory, focusing on parametric time series (TS) models. The main contribution of the thesis is the extension to the multivariate case of the Misspecification-Resistant Information Criterion (MRIC), a criterion introduced recently that solves Akaike’s original research problem posed 50 years ago, which led to the definition of the AIC. The importance of MS is witnessed by the huge amount of literature devoted to it and published in scientific journals of many different disciplines. Despite such a widespread treatment, the contributions that adopt a mathematically rigorous approach are not so numerous and one of the aims of this project is to review and assess them. Chapter 2 discusses methodological aspects of MS from information theory. Information criteria (IC) for the i.i.d. setting are surveyed along with their asymptotic properties; and the cases of small samples, misspecification, further estimators. Chapter 3 surveys criteria for TS. IC and prediction criteria are considered for: univariate models (AR, ARMA) in the time and frequency domain, parametric multivariate (VARMA, VAR); nonparametric nonlinear (NAR); and high-dimensional models. The MRIC answers Akaike’s original question on efficient criteria, for possibly-misspecified (PM) univariate TS models in multi-step prediction with high-dimensional data and nonlinear models. Chapter 4 extends the MRIC to PM multivariate TS models for multi-step prediction introducing the Vectorial MRIC (VMRIC). We show that the VMRIC is asymptotically efficient by proving the decomposition of the MSPE matrix and the consistency of its Method-of-Moments Estimator (MoME), for Least Squares multi-step prediction with univariate regressor. Chapter 5 extends the VMRIC to the general multiple regressor case, by showing that the MSPE matrix decomposition holds, obtaining consistency for its MoME, and proving its efficiency. The chapter concludes with a digression on the conditions for PM VARX models.

Specificità, efficacia e risposte adattative mediate da mutazioni dell'oncosoppressore TP53 degli inibitori del Complesso I mitocondriale nella terapia oncologica personalizzata: dai meccanismi molecolari allo sviluppo di modelli preclinici avanzati

L'inibizione del complesso respiratorio I (CI) è una strategia antitumorale emergente, sebbene la specificità e l’efficacia di nuovi farmaci restino poco investigate. La generazione di modelli cellulari tumorali nulli per il CI rivela la specificità di EVP 4593 e BAY 872243 nell’indurre gli effetti antiproliferativi non associati all’apoptosi, selettivamente via CI, riducendo eventuali effetti collaterali. Studi preliminari in vivo evidenziano un rallentamento della crescita tumorale negli animali trattati con EVP 4593, il quale emerge come l’inibitore più potente. Per il suo ruolo nella riprogrammazione metabolica, e la sua elevata frequenza di mutazioni nelle neoplasie umane, sono stati investigati i potenziali meccanismi di adattamento alla terapia anti-CI sulla base dello stato mutazionale di TP53. L’auxotrofia da aspartato, un hallmark metabolico delle cellule tumorali con un danno al CI, causa un blocco della sintesi proteica mTORC1-dipendente nelle linee cellulari con una p53 mutata o nulla, inducendo un collasso metabolico. Viceversa, l'attivazione del sensore energetico AMPK promuove un recupero parziale della sintesi di aspartato in linee cellulari con la forma wild type di P53, che è in grado di sostenere una migliore anaplerosi attraverso SCO2, fattore di assemblaggio del complesso respiratorio IV. Al fine di traslare questi risultati in un modello preclinico, si è ottimizzato l’ottenimento di colture di tumori umani espiantati tramite il bioreattore U-CUP. Il modello scelto è stato quello di carcinoma sieroso ad alto grado dell’ovaio (HGSOC), a partire da tessuto congelato, per l’elevata frequenza di mutazioni driver in TP53. I tessuti congelati preservano l'eterogeneità delle componenti cellulari del tessuto di origine e sono caratterizzati da cellule in attiva proliferazione senza attivazione di apoptosi. Dati preliminari mostrano un trend di riduzione dell’area tumorale nei tessuti trattati con EVP 4593 e supportano l’utilizzo del modello preclinico nello studio di nuovi inibitori del CI sfruttando materiale primario di pazienti oncologici.

High power density electrical machines with hairpin windings

Nowadays, electrical machines are seeing an ever-increasing development and extensive research is currently being dedicated to the improvement of their efficiency and torque/power density. Compared to conventional random windings, hairpin winding inherently features lower DC resistance, higher fill factor, better thermal performance, improved reliability, and an automated manufacturing process. However, several challenges need to be addressed, including electromagnetic, thermal, and manufacturing aspects. Of these, the high ohmic losses at high-frequency operations due to skin and proximity effects are the most severe, resulting in low efficiency or high-temperature values. In this work, the hairpin winding challenges were highlighted at high-frequency operations and at showing the limits of applicability of these standard approaches. Afterward, a multi-objective design optimization is proposed aiming to enhance the exploitation of the hairpin technology in electrical machines. Efficiency and volume power density are considered as main design objectives. Subsequently, a changing paradigm is made for the design of electric motors equipped with hairpin windings, where it is proven that a temperature-oriented approach would be beneficial when designing this type of pre-formed winding. Furthermore, the effect of the rotor topology on AC losses is also considered. After providing design recommendations and FE electromagnetic and thermal evaluations, experimental tests are also performed for validation purposes on a motorette wound with pre-formed conductors. The results show that operating the machine at higher temperatures could be beneficial to efficiency, particularly in high-frequency operations where AC losses are higher at low operating temperatures. The last part of the thesis focuses on comparing the main electromagnetic performance metrics for a conventional hairpin winding, wound onto a benchmark stator with a semi-closed slot opening design, and a continuous hairpin winding, in which the slot opening is open. Lastly, the adoption of semi-magnetic slot wedges is investigated to improve the overall performance of the motor.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.