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This thesis presents a digital twin framework for modeling and optimizing intrabody RF wireless communications across frequencies including millimeter-wave (mmWave) and terahertz (THz) bands. By integrating detailed anatomical and physiological data, the framework accurately replicates the complex electromagnetic environment within the human body. This enables predictive simulations of signal propagation, addressing key challenges such as attenuation, scattering, and absorption. The digital twin provides a non-invasive platform for designing and optimizing intrabody wireless systems, facilitating the advancement of high-performance networks for biomedical applications like implantable devices and real-time health monitoring.

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Intrabody communication (IBC) is a promising technique for secure and energy-efficient data transmission within the human body, with applications in biomedical devices, wearable sensors, and implantable systems. The exploration of different transmission methods, including terahertz (THz) waves, galvanic coupling, and ultrasounds, presents an opportunity to optimize performance for diverse medical and health monitoring applications. This research seeks to analyze the feasibility and efficiency of these communication techniques through a combination of simulations and experimental validation.

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This thesis focuses on developing algorithms to detect anomalies in heartbeat signals captured by radar, with an emphasis on decoupling cardiac signals from respiration to enhance detection accuracy. The research incorporates Integrated Sensing and Communication (ISAC), enabling radar systems to seamlessly combine physiological monitoring with communication functionality. The student will design and implement methods for detecting or preventing heart rate anomalies, such as arrhythmias, and validate their performance through measurement campaigns using real radar data. The ultimate goal is to deliver a low-complexity, real-time solution for healthcare applications and preventative monitoring.

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This thesis explores a novel approach to restoring nerve function through semantic signal reconstruction, designed to act as a relay system for damaged nerves. By focusing on the "semantic meaning" of neural signals—the critical information necessary for physiological responses—this method bypasses the need to replicate the entire signal, enabling efficient and targeted signal propagation across injured sections. The work emphasizes low-complexity algorithms and real-time processing, making the approach suitable for implantable devices like cuff electrodes. These devices capture, interpret, and regenerate nerve signals, ensuring seamless communication despite nerve damage.

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The thesis aims at studying the potential of the cellular infrastructure for the retrieval and monitoring of rain. The goal of the work is to investigate the potential of the cellular network to be use for weather forecasting, towards greener environments. The student will learn how to model and process the cellular signals to estimate the rain precipitation rate, and validate the simulations on experimental data acquired with a 5G cellular base station.

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La tesi si concentra sulla progettazione e la manifattura di una cuff electrode, un’interfaccia neurale da posizionare attorno a un nervo periferico per il campionamento del segnale nervoso. Lo studente sarà coinvolto in attività pratiche di laboratorio, con focus su biomateriali avanzati, tecniche di produzione, parziale progettazione circuitale e aspetti normativi. L’obiettivo è sviluppare un protocollo e un metodo di produzione che garantiscano un campionamento stabile ed efficace del segnale nel tempo.

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La tesi si concentra sulla progettazione e sviluppo hardware di un chip impiantabile, con focus sull’acquisizione e trasferimento dei segnali biomedicali. Lo studente sarà coinvolto nella scelta, configurazione e integrazione delle componenti elettroniche per la realizzazione del dispositivo. L’ottimizzazione si orienterà verso applicazioni mediche future sull’uomo, puntando a miniaturizzazione del segnale medicale, efficienza energetica e affidabilità a lungo termine.

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Eye-tracking capabilities have applications across a wide range of fields, including entertainment, medicine, and security. This thesis proposes the development of novel algorithms and devices that exploit mmWave radars for visual gaze analysis. The candidate will focus on both the practical aspects and the more abstract algorithmic components of the problem. The work includes an experimental phase involving the design of PCBs using commercial sensors to validate the developed algorithms. Additional extensions of this thesis will explore integrated sensing and communication using the same device.

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This thesis aims to develop a predictive smart irrigation system by integrating electrophysiological sensors, electronic noses, and environmental sensors to monitor and analyze plant health. The model will optimize irrigation practices through neural networks to enhance water efficiency and promote sustainable agriculture in controlled and natural environments.

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Multimodal large language models (MLLMs) were proposed over the last two years to generate text output from a prompt combining different input types, for instance, text and images. If applied to medicine, they are expected to generate new applications to support clinicians, e.g. in the decision-making when a medical doctor needs advice from a more experienced colleague. Some MLLMs specific to the medical domain were developed, like LLaVA-Med, PeFoMed, and Med-Flamingo. However, those MLLMs can only be used for visual question answering but not other tasks, like image segmentation, which is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease progress monitoring. Segment Anything Model (SAM) was developed as the first foundational model for the segmentation of natural images. SAM automatically generates segmentation masks by selecting a point, drawing a bounding box around objects to segment, or even using a text prompt. SAM was not specifically developed for medical images, e.g., computed tomography scans, which are traditionally more challenging. Initial studies have shown that its performance was lower than that of deep learning models in the segmentation task of medical images. However, the recent release of MedSAM, a fine-tuned version of SAM on an unprecedented quantity and variety of datasets for medical images, outperforming state-of-the-art deep learning models, disrupted the field. Medical SAM 2, based on SAM 2, outperformed deep learning models, SAM-based ones, and even MedSAM in medical image segmentation. This thesis work aims to combine MLLMs and foundation models like MedSAM/ Medical SAM 2 to build a tool enabling users to type a prompt like “Segment the pancreas” and visualize the segmentation mask. This thesis will build upon a recent research work at the Politecnico di Milano on a fine-tuned MLLM outputting a bounding box for the localization of the pancreas, given a question like “Where is the pancreas”. The automatically generated bounding box could serve as a prompt for MedSAM/Medical SAM 2 to segment the abdominal organs. Since current MLLMs need improvements in the localization of medical organs, the student(s) will need to optimize the MLLM architecture, for instance by replacing the vision encoder with a novel component, like MedNeXt .

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