Non-linear modelling of microwave devices embedding nano-ferroelectric materials for real-time reconfigurable transmitarray

In this thesis work a nonlinear model for Interdigitated Capacitors (IDCs) based on ferroelectric materials, is proposed. Through the properties of materials such as Hafnium-Zirconium Oxide (HfZrO2), it is possible to realize tunable radiofrequency (RF) circuits. In particular, the model proposed in this thesis describes the use of an IDC, realized on a High-Resistivity silicon substrate, as a phase shifter for beam-steering applications. The model is obtained starting from already present experimental measurements, through which it is possible to identify a circuit model. The model is tested for both low power values and other power values using Harmonic Balance simulations, which show an excellent convergence of the model up to 40 dBm of input power. Furthermore, an array composed by two patches operating both at 2.55 GHz, which exploits the tunable properties of the HfZrO-based IDC is proposed. At 0dBm the model shows a 47° phase shift with polarization -1 V and 1 V which leads to a 11° steering of the main lobe of the array.

Embedding Starobinsky inflation in string compactifications

A period of accelerated expansion of the primordial universe, known as inflation, represents the standard paradigm for the early universe cosmology. While inflation agrees with observational constraints, a complete understanding of its physical origin is not available yet. This suggests the necessity of an embedding into a more fundamental theory. String theory is arguably the best-developed candidate for an ultra-violet (UV) complete theory of gravity and string compactifications could provide a natural framework for addressing this issue. The aim of this thesis work is to investigate the potential embedding of Starobinsky inflation in effective field theories arising in string compactifications. In particular, we focus on two main objectives. The first one is the evaluation of Yukawa-like couplings in f (R)-theories of gravity with fermions, more specifically in the context of Starobinsky inflation. The second goal is understanding if any of the moduli which naturally arise in string compactifications has the right form of this coupling and displays the correct scalar potential, as needed for a possible identification with the scalar field driving Starobinsky inflation.

Embedding Video Object Detection Capabilities on Low-Power Nano-Drones

Our objective for this thesis work was the deployment of a Neural Network based approach for video object detection on board a nano-drone. Furthermore, we have studied some possible extensions to exploit the temporal nature of videos to improve the detection capabilities of our algorithm. For our project, we have utilized the Mobilenetv2/v3SSDLite due to their limited computational and memory requirements. We have trained our networks on the IMAGENET VID 2015 dataset and to deploy it onto the nano-drone we have used the NNtool and Autotiler tools by GreenWaves. To exploit the temporal nature of video data we have tried different approaches: the introduction of an LSTM based convolutional layer in our architecture, the introduction of a Kalman filter based tracker as a postprocessing step to augment the results of our base architecture. We have obtain a total improvement in our performances of about 2.5 mAP with the Kalman filter based method(BYTE). Our detector run on a microcontroller class processor on board the nano-drone at 1.63 fps.