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The work is in cooperation with Hitachi Energy.

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La proposta di tesi prevede lo sviluppo di un sistema di alimentazione per un dispositivo attivo impiantato per applicazioni mediche basato su trasferimento wireless di potenza. Il dispositivo realizza un bypass neuronale per ristabilire una funzione corporea persa di un paziente. La parte iniziale dell’attività riguarda l’analisi degli approcci proposti in letteratura. La parte successiva verterà sulla definizione di una configurazione di riferimento da simulare in ambiente COMSOL. Poiché l’ambito applicativo è quello umano, un aspetto importante da considerare riguarda la normativa CE/FDA. Al fine di rendere più efficiente il sistema di alimentazione, si prevede la definizione di un sistema di “beamforming” (alimentazione wireless localizzata di un dispositivo). La durata prevista per il lavoro di tesi è di circa 9-12 mesi. Nell’ambito dell’attività è prevista una collaborazione con Imperial College di Londra, Università di Toronto e Politecnico di Torino. La tesi potrà essere sviluppata come progetto di coppia.

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La misura di segnali nervosi genera una quantità di dati elevata che è difficile da gestire a livello di elaborazione, memorizzazione e trasmissione. L’acquisizione di tali segnali richiede dunque l’applicazione di tecniche di compressione. La proposta di tesi riguarda l’applicazione di tecniche di compressione a segnali biomedicali elettro neurografici (ENG) e la successiva valutazione dell’impatto sulla loro classificazione mediante reti neurali di nuova generazione. L’obiettivo è quello di valutare la relazione esistente tra la distorsione introdotta dalla compressione e l’accuratezza ottenuta da un dato classificatore. I segnali ENG da analizzare sono disponibili da misure fatte presso il nostro laboratorio e università partner. La durata prevista per il lavoro di tesi è di circa 9-12 mesi. La tesi potrà essere sviluppata come progetto di coppia.

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La seguente proposta di tesi riguarda lo studio e l’analisi di segnali biologici presenti nelle piante per la definizione di un sistema in grado di garantire il loro benessere. L’attività si concentrerà sull’analisi del segnale nervoso che si propaga all’interno della pianta e la sua classificazione per riconoscere l’effetto di differenti condizioni dell’ambiente. Il lavoro permetterà anche di ottimizzare l’utilizzo di risorse preziose come, ad esempio, l’acqua e l’impiego di fertilizzanti e pesticidi, creando così le premesse per le coltivazioni intelligenti. È prevista una collaborazione con un’azienda. La durata prevista per il lavoro di tesi è di circa 9-12 mesi. La tesi potrà essere sviluppata come progetto di coppia.

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I dispositivi elettronici impiantabili sottocutanei rappresentano una tra le tecnologie più promettenti per diverse applicazioni in ambito medico. Molte sono le sfide da affrontare prima di arrivare ad una loro commercializzazione. La presente proposta di tesi affronta due aspetti fondamentali che permetterebbero di accelerare l’impiego di tali dispositivi in applicazioni neurologiche del sistema periferico. Nello specifico, i principali temi affrontati saranno la classificazione dei segnali mediante approcci di elaborazione numerica avanzati e la loro codifica per la memorizzazione e il trasferimento wireless verso l’esterno del corpo umano. Altri aspetti importanti del lavoro riguarderanno la scrittura di un codice firmware per la loro implementazione e la normativa CE/FDA. Lo studente avrà a disposizione registrazioni nervose provenienti da nervo sciatico, pudendo e vago. L’obiettivo è quello di studiare e definire nuovi metodi di compressione per la classificazione dei segnali nervosi con tecniche di Deep Learning da utilizzare in future applicazioni per la stimolazione in tempo reale su paziente. La durata prevista per il lavoro di tesi è di 9-12 mesi. Nell’ambito dell’attività è prevista una collaborazione con Imperial College di Londra, Università di Toronto e Politecnico di Torino.

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La proposta di tesi riguarda l’applicazione di tecniche basate sull'impiego di reti neurali di nuova generazione per l’analisi di segnali biomedicali elettro neurografici (ENG) acquisiti da nervo sciatico, vago e pudendo. L’obiettivo del lavoro è quello di classificare con la maggior accuratezza possibile differenti tipi di segnali sensoriali (propriocettivo, nocicezione, tocco, ecc. …). I segnali ENG da analizzare sono disponibili grazie a misure effettuate presso il nostro laboratorio e quello di università partner. La durata prevista per il lavoro di tesi è di circa 9-12 mesi. La tesi potrà essere sviluppata come progetto di coppia.

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The emission of GreenHouse Gases (GHG) and other particulate in the atmosphere is an ever growing concern of all government bodies around the world. Mapping with more detail sources of GHG emission and doing surveys to collect experimental data is key to understanding the problem in more depth. The key objective of this thesis is developing a low weight GHG measurement platform based on Raspberry Pi or Arduino, to be mounted on a small UAV for data collection campaigns. The work will span from sensor selection by surveying datasheets of commercially available solutions, to hardware implementation and writing the software for performing the experimental data acquisitions. Several challenges will have to be tackled from constraints due to weight limitations to synchronization of the captured data with UAV flight path.

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Context There has been an increased interest in the utilization of unmanned aerial vehicles (UAVs) for different civilian, military, and commercial applications. In particular, the communications sector has been testing the usage of UAVs to either augment existing infrastructure, or to replace them in particular scenarios. This is achieved by mounting base stations on the UAVs, which are then flown to the desired location to provide coverage to users. With their advantages such as ease of deployment, lower costs than base station towers, and flexible location capabilities, these so-called aerial base stations (ABSs) are a promising technology for emergency response, e.g., in cases when the terrestrial communication infrastructure is inoperable due to extreme phenomena like typhoons or earthquakes. However, with these advantages to ABSs, there lies an open problem with regards to their optimal placement as to maximize their coverage in terms of the users count and quality of service (QoS). There have been several proposed approaches to deal with the UAV placement problem. These approaches can be based on clustering, coverage circles optimization, or the use of virtual forces on the UAV agents. However, most of these solutions assume known ground client (GC) locations, which is not realistic especially in emergency situations when ground towers are unpowered and we do not have information on user locations to save bandwidth, and due to privacy issues. However, we can have information on the sensed QoS of the connected users–whether individual QoS or the aggregate–which can be associated with the user coverage of the ABS. Due to the a priori unknown user locations, there is an intrinsic trade-off between exploitation of existing data from previously-sensed locations, and exploration to different locations around the target region to acquire more information on user QoS, and in turn, coverage fitness. In this master thesis, you will be investigating a black-box optimization approach to the ABS coverage problem, in the context of an ABS formation. Using the respective ABS locations and their sensed user QoS, the formation automatically decides on the best placement combinations and trajectories of the member ABSs, according to the trade-off between exploitation and exploration. Tasks The master student will perform the following - Literature review on UAV usage for wireless network coverage applications - Study of a black-box optimization technique to be used for the problem - Formulation of a scheme for ABS formation placement for coverage, using real-time optimization - Development of a simulation testbed - Testing with time-invariant (but a priori unknown) client locations - Documentation of performed work Expected profile Currently-enrolled master student in Computer Engineering or Automation Engineering Highly skilled (or willing to learn) in programming in any language, but preferably Python and/or MATLAB/Simulink Analytical thinking and applied mathematical skills Willing to be trained on new concepts and skills Initial references Viet, P. Q., & Romero, D. (2022). Aerial Base Station Placement: A Tutorial Introduction. IEEE Communications Magazine, 60(5), 44–49. https://doi.org/10.1109/MCOM.001.2100861 Cao, X., Yang, P., Alzenad, M., Xi, X., Wu, D., & Yanikomeroglu, H. (2018). Airborne communication networks: A survey. IEEE Journal on Selected Areas in Communications, 36(9), 1907–1926. https://doi.org/10.1109/JSAC.2018.2864423 Mozaffari, M., Saad, W., Bennis, M., Nam, Y. H., & Debbah, M. (2019). A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems. IEEE Communications Surveys and Tutorials, 21(3), 2334–2360. https://doi.org/10.1109/COMST.2019.2902862 Alzenad, M., El-Keyi, A., & Yanikomeroglu, H. (2018). 3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users with Different QoS Requirements. IEEE Wireless Communications Letters, 7(1), 38–41. https://doi.org/10.1109/LWC.2017.2752161 Hayajneh, K. F., Bani-Hani, K., Shakhatreh, H., Anan, M., & Sawalmeh, A. (2021). 3D deployment of Unmanned aerial vehicle-base station assisting ground-base station. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/2937224 Contact Prospective students are to send their CV, master coursework grades, and code portfolio (if present) to: prof. Fredy Ruiz, Ph.D. Associate Professor fredy.ruiz@polimi.it prof. Lorenzo Fagiano, Ph.D. Associate Professor lorenzo.fagiano@polimi.it

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The thesis is In collaboration with ST-Microelectronics, and provides for reimbursement of expenses. The thesis will start practicing on some specific technologies (MEMS mirrors, lasers, SPADs and Silicon-photonics), but also optical and embedded systems, then he/she will join the MEMS LiDAR or Silicon-Photonics program Role may vary upon specific project needs. Task examples that can be assigned to the intern are: Design and develop a control system for a LIDAR projection system realized in Silicon Photonics; build a test environment; characterize the device in real conditions. Design and develop a control system for a gyroscope realized in Silicon Photonics; build a test environment; characterize the device in real conditions. Design and develop a new CFD (Constant Fraction Discriminator); build a test environment; characterize the device in real conditions. Technical skills: Willing to learn and autonomy HW-SW skills (mandatory): Digital and analog electronics, schematic diagrams, C, C++, Matlab Other skills (appreciated): VHDL, 3D modeling (Solidworks), optics ST contact: daniele.caltabiano@st.com

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A huge amount of information is generated every day by a countless number of sensors. These sensor data are used to take important decisions and improve the welfare of users and citizens. However, the most of these data is never used and its storage and maintenance have a relevant cost in terms of finance and energy. This thesis aims at explore a different approach for sensor data management and will investigate methods and tools to adapt the data collection from sensor in terms of data generation frequency and level of detail according to several factor: actual usage of the data, data location, and energy efficiency of data storage and movement. This thesis will provide a methodology to solve issues related to where, when, and how to store sensor data in the cloud continuum. KEYWORDS: Green IT, IoT, adaptive monitoring system, cloud continuum, fog computing

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Modern applications are composed of several components (microservices, functions, monoliths) that cooperate together for reaching the overall application goal. Most of the existing infrastructures don’t provide information about the energy consumption at the application component level. Thus, some models to derive this information are needed if we want to envision a real sustainable application future. The aim of this thesis is to explore the current state of the art and the existing tools for deriving energy consumption of single-component applications and their emissions and to propose a method to extend these tools for measuring the impact of multi-component applications. KEYWORDS: Green IT, CO2 estimation, energy consumption, cloud native applications

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The adoption of AI is sensitively increasing the number of computing instances in the cloud. The training phase uses a huge amount of data coming from different data sources. The training data volume is one of the most relevant contributors to the power consumption of model training in ML, while having a limited impact on the model accuracy. At the same time, distributed ML approaches are becoming more and more popular (e.g., federated learning). In this context, the thesis should provide an approach to decrease the energy consumption of AI without affecting the accuracy of the model by taking decisions on where and how to store the training data and to execute the model training. KEYWORDS: data preparation, Green AI, Data quality, distributed learning

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Sustainability and Energy Efficiency are a driver for the improvement of our society and a necessity for a better future. While Data Centers are getting greener, this is not the case for other computing locations like fog and edge computing, composed of small and inefficient data centers. Considering applications as composed of several microservices interconnected in a workflow and exchanging data, the research question is how it is possible to adaptively allocate each component by considering the trade-off between QoS and Energy Efficiency. KEYWORDS: micro services, fog computing, cloud continuum, green IT, scheduler

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Images, audio tracks and videos can be manipulated in many different ways (e.g., object insertion and removal, local retouching, laundering operations, copy-move, deepfakes, etc.). We are interested in developing methods to detect and localize possible editing operations on multimedia objects exploiting model-based and data-driven solutions.

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Cooperation with Prysmian S.p.A.

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Copperation with Terna S.p.A.

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Load forecasting plays a crucial role in the day-to-day operations of electric utilities, especially in modern power systems, where a significant share of power generation is attributable to renewable sources. Over the years, several algorithms have been developed to tackle this problem, on time scales ranging from a few hours to several months. Most recent solutions have employed machine learning techniques such as deep learning to increase the granularity of the prediction, down to the single-building level. The main goal of this thesis is the implementation of several machine learning algorithms for the prediction of load consumption of a single building over a 24-hour time horizon. The data set that will be used for training is very rich and consists of over a year of recordings covering load consumption, load generation (due to photovoltaic panels installed on the building), and weather data. The applicant will implement and test the performance of both conventional machine learning algorithms such as Autoregressive Integrated Moving Average (ARIMA) and deep-learning-based approaches, for instance using Long Short-Term Memory (LSTM) networks.

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The displacement of conventional generators by renewable generation technologies implies a reduction in rotating inertia, which may jeopardize system stability. Monitoring inertia in real-time is thus becoming a major concern for Transmission System Operators (TSOs). The main goal of this thesis is the development of a framework for the online estimation of inertia in an electric power system by using state-of-the-art artificial intelligence (AI) techniques. Unlike existing algorithms for inertia estimation, which rely on disturbances (such as load or generator trips or line faults), the aim of this thesis is to provide a continuous estimation of inertia during the normal, unperturbed operation of a power system. To achieve this goal, the applicants will develop an approach based on deep convolutional neural networks for the continuous estimation of system inertia and apply it to synthetic data obtained by simulating models of several power networks. The obtained results will be compared with existing methods of inertia estimation and with other conventional (i.e., not based on deep-learning) machine learning algorithms.

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Several black-box models of circuit breakers have been proposed since the pioneering work by Cassie in 1943. Nevertheless, the nowadays need for a ``further'' black-box model is due to two main reasons: (i) accurate tools to simulate the complex physics that describes the interruption phenomena in circuit breakers are very CPU intensive; (ii) available black-box models condense in a few simple equations the complex physics of circuit breakers, but they are inaccurate, and do not cope with the full set of working conditions. The focus of this research thesis is on medium voltage vacuum breakers. The first goal is to formulate a proper electric arc model grounding on a modeling approach based on the prey-predator paradigm originally used to describe dust-forming plasmas that are found in flames, the interstellar medium, and comet tails, and then adopted to model SF6 circuit breakers and air miniature circuit breakers. Then, the arc model will be linked to the geometric, kinematic, and magnetic field properties of the vacuum breaker structure to obtain an aggregate model at the two electrical terminals.

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Constant ON-Time (COT) buck converters are popular in wide input-voltage range applications for many reasons, e.g., the ease of control design over varying input voltage conditions, the absence of jittery ON-time behavior, which affects constant-frequency control methods when incurring in minimum controllable ON-time limitations, and, provided that the ON-time is adapted to the input voltage, the ability to maintain a reasonably constant switching frequency over the input voltage range. Many wide-input-range adaptive COT regulators, targeted to automotive and other wide-input-range applications, are available from various IC vendors. COT buck converters are switching circuits and may exhibit complex dynamics induced by several types of bifurcations: period-doubling, grazing, contact, and border-collision. This thesis aims to study the stability of this circuit by resorting to proper mathematical tools, such as one-dimensional discrete-time maps, to identify suitable regions in the circuit parameter space in which it could be profitably used.

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Electric vehicle (EV) aggregators are looking to increase their profits by operating the EV energy consumption based on energy prices and, in some cases, ignoring the electrical grid impact. Therefore, this thesis aims to assess different EV aggregator strategies already drawn and evaluate them on an electrical grid to evaluate the effects of reverse power flow phase unbalance, among others. This research will analyze the benefits and drawbacks of the strategies by considering typical EV charging behaviors.

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Electric vehicles (EV) are used as flexible loads capable of providing energy services to the electrical grid. However, this flexibility capacity is difficult to predict in a time horizon due to the EV user’s uncertain behavior and a possible previous flexibility provision. Therefore, the thesis aims to create different scenarios for providing balancing services by considering typical EV charging connection times. An aggregator should manage the chargers power profiles.

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