PitchD – The PhD’s Pitch

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The initiative is named ‘PitchD – the PhD’s pitch‘ and is addressed to all PoliTo PhD candidates. It consists of series of seminars in which PhD students present their research work, mainly from a scientific point of view, but also giving a glimpse of their experience so far (conferences, abroad periods, publications and so on). The aim is to present to a broad audience the research carried out in our university, perhaps motivating students to undertake this path in their future career.

Are you a PhD student? What are you waiting for?! Apply now to be one of the speakers!

Call for contribution 2021 and detailed information: LINK

List of 2021 PitchDs

PhD Candidate Date Title Abstract Youtube
Giuseppe Ortolano 28 April 2021 Experimental Quantum Reading with Photon Counting Abstract Youtube
Francesca Pistilli 28 April 2021 Point cloud denoising with graph convolutional neural networks Abstract Youtube
Susana Fuentes Vélez 28 April 2021 Impedance-based microfluidic devices for personalized medicine applications Abstract Youtube
Francesco De Pace 14 April 2021 Assessing the Effectiveness of Enhanced Virtual Reality Systems for Accurate Robot Teleoperation Tasks Abstract Youtube
Mohammad Amir Mansoori 14 April 2021 Hardware Acceleration for Microwave Imaging Algorithms Abstract Youtube
Mohammad Ghazi Vakili 14 April 2021 Industry 4.0: Industrial IoT Enhancement and WSN Performance Analysis Abstract Youtube
David O. Rodriguez Duarte 3 March 2021 Microwave imaging for brain stroke monitoring Abstract Youtube
Edoardo Giusto 3 March 2021 Air pollution monitoring for the smart cities of tomorrow Abstract Youtube
Marta Lovino 3 March 2021 AI in cancer: from DNA and other molecules to relevant models Abstract Youtube
Alessio Sacco 27 January 2021 Internet Congestion Control with Partially Invisible Networks via Reinforcement Learning Abstract Youtube
Raffaele Aaron Giampaolo 27 January 2021 ARCADIA, a proposal for future fully depleted monolithic active sensors Abstract Youtube
Gemma Giliberti 27 January 2021 Multiterminal heterojunction-bipolar transistor solar cell Abstract Youtube

List of 2020 PitchDs

PhD Candidate Date Title Abstract Youtube
Elisa Fevola 25 November 2020 Data driven patient-specific Cardiovascular Modeling for Early Detection of Coronary Artery Disease Abstract Youtube
Davide Romanin 25 November 2020 Electronic properties of field-effect-doped phonon-mediated superconductors: an ab-initio study Abstract Youtube
Antonio Esposito 25 November 2020 Wearable brain-computer interfaces for daily-life applications Abstract Youtube
Andrea Coluccio 21 October 2020 Logic-in-Memory computing: an architectural solution to solve the von Neumann bottleneck Abstract Youtube
Roberto Rubino 21 October 2020 Relaxation DAC: a new-in-concept interface for the Internet-of-Things Abstract Youtube
Noemi Giordano 21 October 2020 Multi-source phonocardiography for the home prevention of heart failure Abstract Youtube
Gianluca Zoppo 23 September 2020 A short review on brain-inspired computing with memristive crossbars Abstract Youtube
Francesco Marrone 23 September 2020 Memristors: an overview on the technologies and their compact modeling techniques Abstract Youtube
Riccardo Peloso 23 September 2020 Mostly-digital High Fidelity audio reproduction systems Abstract Youtube
Francesco Giordano 10 June 2020 A Possible Solution to Stabilize the Future Power Grid and Enabling Electric Vehicle Integration Abstract Youtube
Luciano Prono 10 June 2020 An Optimized Compressed Sensing Decoder based on Deep Neural Support Prediction Abstract Youtube
German Sviridov 29 April 2020 Can AI improve your cloud gaming experience? Abstract Youtube
Farid Elsehrawy 29 April 2020 Light-trapping enhanced thin-film III-V quantum dot solar cells Abstract Youtube
Umberto Garlando 25 March 2020 CAD tools for Field Coupled Nanocomputing: from the development to a Live Demo Abstract Youtube
Fabio Mandrile 25 March 2020 Towards 100% Renewable Generation with Virtual Synchronous Generators Abstract Youtube
Alejandro Rojas David Martinez 29 January 2020 CMOS Electronics readout for radiation detector at cryogenic temperature Abstract
Osama Bin Tariq 29 January 2020 Tag-Less Indoor Localization with AI and Capacitive Sensors Abstract Youtube
Kristjane Koleci 29 January 2020 Low Density Parity Check Codes for Post-quantum Cryptography Abstract Youtube
Pedro Leite Correia 11 December 2019 Nanoscale Digital-Based Analog Processing: How to Design Analog Building Blocks Using Only Digital Gates Abstract Youtube
Andon Bano 11 December 2019 Use of Optical Fiber for Potable Water Monitoring Abstract Youtube

List of 2019 PitchDs

PhD Candidate Date Title Abstract Youtube
Lee Bar-On 12 June 2019 Plant Electronics for Biosensors and Communication Abstract
Ivan Ny Hanitra 12 June 2019 Electrochemical Sensing Platforms for Wearable Physiology Abstract Youtube
Annunziata Paviglianiti 5 June 2019 SoCNNet: An Optimized Sobel Filter based Convolutional Neural Network for SEM images Classification of Nanomaterials Abstract Youtube
Barbara Capello 5 June 2019 Metasurfaces for Mantle Cloaking Applications and Scattering Reduction Abstract Youtube
Gunnar Riemenschneider 8 May 2019 Gravitational Waves: understanding Black Holes, Neutron Stars, and exotic phenomena in the observable universe Abstract Youtube
Alessandra Neri 8 May 2019 Beyond the PhD: what’s your next step? Abstract Youtube
Alex Minetto 16 April 2019 Navigating a Ph.D. – Squeezing the last drop of information from the Global Navigation Satellite Systems Abstract Youtube
Marco Caruso 16 April 2019 Measuring human movement using magneto-inertial sensors: is it possible to assess our motor ability outside the clinic? Abstract Youtube
Daniele Torsello 26 March 2019 Combined microwave and Eliashberg analysis of the effects of disorder in Iron Based Superconductors Abstract Youtube
Stefano Bottigliero 26 March 2019 Embedded Radar Prototyping for Collision Avoidance and Real-Time Localization Abstract Youtube

Embedded Radar Prototyping for Collision Avoidance and Real-Time Localization (Stefano Bottigliero)

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The purpose of my presentation is to exhibit my present and future work as PhD student in the field of embedded radar system prototyping.
The focus is on the design and prototyping of a collision avoidance system for Automated Guided Vehicles (AGV) based on a Time Of Flight sensor and of a Real Time Localization System (RTLS) based on Ultra Wide Band (UWB) signals. It is shown how these two systems can be used in an integrated Simultaneous Locating and Mapping (SLAM) system.
The presentation then deals with the activity I carried on for automotive radar applications and other radar systems. An overview of the different radar systems developed by the radar group is also presented.

Combined microwave and Eliashberg analysis of the effects of disorder in Iron Based Superconductors (Daniele Torsello)

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In this seminar I will present a combined experimental and theoretical approach toward reaching new insights into the mechanisms of superconductivity through the analysis of the effects of disorder in Iron Based Superconductors (IBS). We investigate the critical temperature, penetration depth, quasiparticle conductivity and surface impedance of high-quality IBS single crystals by a planar waveguide resonator technique, in a cavity perturbation approach. The experimental method and data analysis are described, showing that this technique is reliable for the study of small crystals and, since the measurement technique is non-destructive and does not alter the crystals, the very same samples can be measured before and after irradiation, making the analysis of the effects of additional defects more reliable. The absolute values of the penetration depth are accessible by the experiment, showing a fairly good agreement with literature data.
The experimental data is compared to calculations based on the Eliashberg equations within the s± wave model, giving a remarkable agreement. This overall consistency validates the model itself, thus allowing us to estimate parameters that are missing in literature. The calculations are also able to explain in a consistent way the effects of disorder both on the critical temperature and on penetration depth, by suitably accounting for the impurity scattering due to the defects created by irradiation. This combined approach has allowed the identification of the disorder induced s± to s++ order parameter symmetry transition in Rh-doped crystals.

Measuring human movement using magneto-inertial sensors: is it possible to assess our motor ability outside the clinic? (Marco Caruso)

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During my presentation I will focus on the interesting theme of human movement analysis by means of wearable magneto-inertial sensors (IMU). The aim of my research activity is to design and validate advanced algorithms of signal processing (also known as sensor fusion algorithms, e.g. Kalman filter and complementary filter) to estimate the joint kinematics starting from the inertial and magnetic data collected by IMUs. At the end of my presentation, I will introduce the main projects in which our research group is involved. In particular, we are focusing on a tele-rehabilitation project and European research in the Horizon 2020 framework to assess the digital mobility of population with and without motor impairments in real-world settings. Master thesis proposals and open research themes will be also presented.

Beyond the PhD: what’s your next step? (Alessandra Neri)

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My experience in GM
Eng. Alessandra Neri received her doctoral degree in Metrology from PoliTo with a thesis entitled ‘Development of innovative low cost sensors based on optical technologies’. She has been working in General Motors for almost 6 years, first as Hardware Electrical Architecture Development Engineer and now as Technology System Engineer, dealing with connected vehicles and big data. She will present her professional experience, providing an interesting insight on opportunities and challenges facing PhD students after the degree.

Gravitational Waves: understanding Black Holes, Neutron Stars, and exotic phenomena in the observable universe (Gunnar Riemenschneider)

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With the first detection of Gravitational Waves on September 14th, 2015 a new era of Astronomy has begun. The LIGO detectors observed the merger of two Black Holes several 10-times heavier then the sun clashing into each other at more then half the speed of light over a billion light-years and over a billion years ago. Since then the efforts by the LIGO scientific collaboration have been pushed forward and large international effort has begun to build a global network of more and better Gravitational Wave detectors and to understand what we can learn from studying Gravitational Waves.
But why are Gravitational Waves so interesting to physicists? Or what are they to begin with? What kind of events produce gravitational waves that can be detected on earth? How can they be detected? What kind of detectors are currently operating? What kind of detectors are planned?
In my talk I will discuss these questions and give a brief overview of the field of Gravitational Wave Astronomy. I will discuss the basic ideas of what are Gravitational Waves, how can they be detected, how can they be generated by Black Holes, Neutron Stars and other more exotic phenomena in the observable universe, and what can be understood from studying them.

Metasurfaces for Mantle Cloaking Applications and Scattering Reduction (Barbara Capello)

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My research activity is focused on the study of electromagnetic metamaterials and especially on their application to electromagnetic invisibility. Metamaterials are artificial periodic structures able to control and bend the propagation path of the electromagnetic waves in unprecedented ways. Among the different applications of metamaterials, cloaking is one of the most fascinating. In particular, mantle cloaking, differently from other techniques, has the advantage of suppressing the scattered field from the target object by using a 2D thin patterned metasurface, therefore reducing the cloak weight and thickness. In this seminar I will present an overview of electromagnetic metamaterials and cloaking problems, showing the design and analysis of a particular metasurface based on a width modulated microstrip line.

SoCNNet: An Optimized Sobel Filter based Convolutional Neural Network for SEM images Classification of Nanomaterials (Annunziata Paviglianiti)

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This presentation aims at providing an illustration of my research about an optimized deep Convolutional Neural Network (CNN) for the automatic classification of Scanning Electron Microscope (SEM) images of homogeneous nanofibers (HNF) and nonhomogeneous nanofibers (NHNF) produced by electrospinnig process. Specifically, SEM images are used as input of a Deep Learning (DL) framework consisting of a Sobel filter based pre-processing stage followed by a CNN classifier (SoCNNet). Experimental results of this study demonstrate the potential effectiveness of proposed SoCNNet in the industrial chain of nanofibers production.

Electrochemical Sensing Platforms (Ivan Ny Hanitra)

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Wearable electrochemical sensors play a significant role in physiology and health status monitoring since they are capable of continuously collecting biological data from human body in the field. In sports applications, it is crucial to assess muscle fatigue, mineral loss, dehydration in order to foresee muscle cramping or other physiological dysfunction. The biological compounds enabling such physiological status monitoring belong to different families of biomarkers, hence, necessitating a general purpose and multi-sensing platform. Moreover, within a multi-sensing context, efficient data processing tools are needed to predict reliably the concentration of each biomarker and to cope with interference. In this talk, electrochemical sensing platforms dedicated to wearable physiology applications are presented. The electronic front-end interfaces developed are described and characterized for endogenous metabolites, electrolytes and exogenous compounds monitoring. The programmability and versatility of the sensing front-end enables its use for broader biomedical applications. In addition, some insights on processing tools for multi-ion sensing are also presented.

Plant Electronics for Biosensors and Communication (Lee Bar-On)

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The concepts of a complete plant “Internet of Things” with direct data collection from the plant, is a novel approach. Extensive plant research is available. However, study of plants in terms of electronics and electrical conduction mechanisms are not well defined. Here would like to establish an improved understanding of the electronic conduction within the plant and deploy it for sensing and communication applications. Using this new approach, we seek to establish whether a measurable electrical change, will allow detection of biological and physiological changes within the plant.

Use of Optical Fiber for Potable Water Monitoring (Andon Bano)

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Nowadays, optical fibers are quite well known for their great telecommunication capabilities, but recently they have gained a lot of attention for their sensing capabilities. Small size, high sensitivity, electromagnetic compatibility and the low cost are some of their most important key features and the main reason why so much research is going on in the field. My research focuses on plasmonic based optical fiber sensors aimed to monitor the quality of potable water. The plasmonic phenomenon allows an analysis based on stable high-resolution refractive index sensing. According to UN, more than 40% of the world’s population is affected by water scarcity problems, which in a lot of cases does not mean total lack of water, but rather means having not sanitized water supply. The necessity for monitoring the quality of such water before using (either for washing or drinking), is an emergency, and in most of the cases it needs to be done actively on the spot, in order to prevent the consumption. Plasmonic optical fiber sensors seem to be a very promising solution for such an emergent problem.

Nanoscale Digital-Based Analog Processing: How to Design Analog Building Blocks Using Only Digital Gates (Pedro Leite Correia)

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While digital Integrated Circuits (IC) have been taking the advantage of CMOS technology scaling in terms of speed, power consumption, and cost, the techniques running behind the analog signal processing are still lagging behind. In other words, it is clear that overtime there have been significant improvements (and it is expected to) in digital circuits performance when compared with analog building blocks. To mitigate this, there has been an increasing trend in finding alternative IC design strategies to implement analog functions exploiting digital-in-concept design methodologies. In addition to the advantage of using the well-established fully-automated EDA digital tools, the idea of re-thinking analog functions in digital terms would make the Analog IC blocks also suitable to avail energy efficiency and the feature-size shrinking of new technologies. In this context, this presentation will cover the current status of the state-of-art digital-based analog building blocks results including circuits such as OTA, comparators and converters, as well as their impact over the emerging applications and technologies. Put differently, are you a digital guy ? Would you like to know how to design OpAmp (analog stuff) only using logical gates ? This talk is for you.

Low Density Parity Check Codes for Post-quantum Cryptography (Kristjane Koleci)

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The current technological progress in the field of quantum computing poses serious problems for the security of widely adopted asymmetric cryptosystems as RSA and ECC. The asymmetric encryption requires to rely on different algorithms, such as Code-based cryptography. The topic of my research is the McEliece cryptosystem based on Error Correcting Codes, in particular a version that adopts QC-LDPC codes. The codes are structured and sparse, thus they have key of small size even if the dimensions of the inner variables are huge. I am mainly working on the hardware implementation of the decryption system in order to make the computation of the quantities faster and include the possibility to have a scalable architecture that is able to match the needs of a particular application or the limitations of a device.

Tag-Less Indoor Localization with AI and Capacitive Sensors (Osama Bin Tariq)

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Low cost, ubiquitous, tag-less, and privacy-aware indoor monitoring is essential to many applications, such as home automation and assisted living of elderly persons. Capacitive sensors can be inconspicuous, cost and power-effective solution, but afflicted by noise especially at ranges much longer than their plate diameter. Here, we explore how well different Artificial Intelligence (AI) techniques can extract location and movement information from noisy experimental data (with both high-pitch and slow drift noise) obtained from capacitive sensors operating in load mode at long ranges. For a 3 m x 3 m space equipped with four 16 cm x 16 cm capacitive sensors, we investigate first how well Machine Learning (ML) can localize a person in 16 predefined positions, then how well continuous human walking trajectories can be reconstructed by various types of Neural Network architectures. We discuss how the experimental data was obtained, preprocessed, and the outcome of the various ML and AI techniques. The key result is the ability to locate and track a person in the above setting with an average accuracy of 93% for location classification and a mean error of 28 cm for trajectory reconstruction using sensors with an estimated SNR of about 3-4 dB.

CMOS Electronics readout for radiation detector at cryogenic temperature (Alejandro Rojas David Martinez)

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Cryogenic CMOS electronic could provide an important impact in the current conventional electronic applications, since this can offer important device performance improvements, in term of RMS noise, power consumption and speed. In the nuclear physics area, the underground astroparticle physics experiments require the usage of the cold electronics due its structure (LAr and LXe Time Projection Chamber). In this presentation, the behavior of 110 nm CMOS transistor parameters (gm, Vth, µo, rSD, q) between 300 K and 77 K and the integrated front-end electronic design for readout a sensor area of 24 cm2 are presented. The experimental result of the cryogenic electronic front-end for DarkSide20K, using a SiPM as sensor and Liquid Nitrogen, are described. This research is on behalf of DarkSide collaboration.

Towards 100% Renewable Generation with Virtual Synchronous Generators (Fabio Mandrile)

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Power electronics-based energy conversion is making the exploitation of Renewable Energy Sources, such as sun and wind, more and more attractive and the amount of renewable energy generation is expected to rise exponentially all over the world in the next 20 years. However, this comes at a price: the power system was designed to operate with a prevalence of synchronous generators. Therefore, changes in the design of power electronics and control will be mandatory to reach a 100% renewable-based power system. In particular, electronic converters will have to guarantee the so called “ancillary services” in order to maintain the stability and the correct operation of the power grid. A promising solution is the concept of Virtual Synchronous Generator, that makes the converter behave as a synchronous machine. This talk will introduce the issues related to power generation from renewables and describe how Virtual Synchronous Generators can solve them.

CAD tools for Field Coupled Nanocomputing: from the development to a Live Demo (Umberto Garlando)

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The Moore’s law states the number of transistors inside a single chip doubles every eighteen months thanks to dimensions scaling. However, this trend is reaching an end due to technological limits and power density. For these reasons new technologies are being studied in order to find possible alternatives. In particular, magnetic QCA (quantum-dot cellular automata) technologies are the most promising. No current flows inside the technological elements, and information propagation is achieved through magnetic fields: they are part of the Field Coupled Nanocomputing (FCN) technologies. Technologists focus on the design of the single device and its characteristics. A circuit level exploration needs CAD (Computer Aided Design) tools in order to speed up the process. The ToPoliNano (Torino Politecnico Nanotechnologies) framework enables the design and simulation of digital circuits based on FCN technologies. The framework and the supported technologies will be described and a Live Demo of the tools will be performed.

Light-trapping enhanced thin-film III-V quantum dot solar cells (Farid Elsehrawy)

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The demand for a source of renewable energy has been the driving force to achieve significant advancements in the field of photovoltaic energy generation during the past decades. III-V quantum dot (QD) solar cells hold promising potential for the realization of multiple novel photovoltaic concepts. However, one of their essential limitations arises from the weak absorption coefficient of the QD layers as well as the difficulty to grow active regions with high in-plane QD density and to stack many QD layers. Light trapping allows overcoming limitations faced by QD solar cells and Intermediate Band (IB) solar cells that generally suffer from weak QD interband and intraband photo-generation. It can be used to increase the effective optical path length of the QD layers, thereby resulting in higher photogeneration and overall efficiency. Moreover, light trapping concepts allow the use of a thin active region while maintaining constant short-circuit current, thereby minimizing volume recombination losses and enabling a higher open circuit voltage. Thin-film solar cells are associated with a high power-to-weight ratio, an essential requirement in space applications.

Can AI improve your cloud gaming experience? (German Sviridov)

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Quality of Experience (QoE) assessment for video games is known for being a long and expensive process, typically requiring the active involvement of several human players under different network conditions while ultimately bringing limited transferability across different games. Noteworthy, QoE is correlated with the achieved in-game score, as player frustration will arise whenever their performance is far from what is expected.
Taking inspiration from this observation, we question ourselves if it is possible to simplify the QoE assessment task by substituting human players with artificial agents trained to play the same game under varying network conditions.
To answer this question we let our artificial agents play a set of retro games with different types of interaction, showing that the observed score degradation curves can be exploited in networking devices (e.g., by prioritizing scheduling decisions), reinforcing fairness across games, and thus enhancing the overall quality of gaming experience.

An Optimized Compressed Sensing Decoder based on Deep Neural Support Prediction (Luciano Prono)

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Compressed Sensing (CS) is a widely researched technique employed to lower energy consumption of signal acquisition. It is especially suited for naturally sparse signals such as human body biosignals. These type of signals can be linearly mapped on a lower dimensional space through a simple vector-matrix multiplication. From this, the recovery of these signals can be partitioned in two steps: support estimation phase (i.e., the retrieval of the positions of the coefficients in the sparse vector) and coefficient estimation phase. Support estimation can be performed with an oracle based approach on a deep neural network. The output of the oracle can be then used to estimate the coefficient by solving the Least Mean Square (LMS) problem. This approach allows the definition of an encoder-decoder pair with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG.

A Possible Solution to Stabilize the Future Power Grid and Enabling Electric Vehicle Integration (Francesco Giordano)

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The power grid is assisting a huge transformation thanks to the adoption of renewable resources. which have enabled bidirectional power flows, no longer only from high and medium voltages to low voltage but also vice versa. This phenomenon, together with the fact that wind and solar energies are not easily foreseeable and programmable, leads to the need to rethink a more flexible electricity network able to store a greater electricity capacity. On the other hand, another revolution is increasingly making its way. The transport sector is destined to converge rapidly towards electricity. The combination of these two revolutions can represent a risk for the stability of the electricity grid but also a crucial opportunity not to be missed.

Mostly-digital High Fidelity audio reproduction systems (Riccardo Peloso)

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Recent years have observed a significant rise in popularity of music streaming services. Instead of using a physical format, music is now distributed in a completely digital way. Nevertheless, on the playback phase little has changed as it remained inherently analog. It follows that to correctly reproduce a digital audio signal, it must be converted back to its analog representation. This operation has to be performed by a Digital to Analog Converter (DAC). During the conversion phase digital signals are subject to inevitable analog errors which can deteriorate the quality of the resulting output signal. In High Fidelity audio systems, which aim to be as faithful as possible to the original source, these kind of errors represent a major obstacle to overcome. Although older analog-centric DAC chips are able to partially mitigate this issue, newer digital-centric designs struggle to reach the same level of performance, as they need to cope with error related to modern transistor production processes.This talk will focus on novel smart digital algorithms capable of concealing and mitigating issues related to digital-centric DACs, while keeping cost and power consumption low.

Memristors: an overview on the technologies and their compact modeling techniques (Francesco Marrone)

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In the last decade, the accelerating progress in the development of more and more complex neural networks has called for performant accelerators and novel computing paradigms. Memristors, theorized to be the fourth fundamental circuit elements, have attracted the interest of the neuromorphic computing research community as promising technologies to overcome the limitations of current digital systems. Originally developed as information storage devices, memristive technologies have unvealed their true potential as basic computing elements by exploiting the Ohm’s Law when operated at very low electric field. Nonetheless, their intriguing complex physical working principles, responsible for their state-dependent highly nonlinear characteristics, open the door to very novel computing paradigms such as spiking and oscillatory neural networks. Those complex physical phenomena, which qualify memristors as perfect candidates for mimicking neuron level activities, need either very powerful computers to be simulated or application specific compact modelling techniques.

A short review on brain-inspired computing with memristive crossbars (Gianluca Zoppo)

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Most conventional artificial neural networks architectures are implemented on digital computers yielding order of magnitude higher energy consumption than biological brains. Neuromorphic computing aims to emulate the neural structure and operation of the brain in order to create low power computing platforms that are able to tackle complex tasks in artificial intelligence. Nonvolatile memory devices, such as memristors, stand out as promising candidates to represent synaptic weights of artificial neural networks by enabling efficient parallel computations such as matrix-vector multiplications. Despite the great success in deep learning applications, there is an increasing interest in the direct use of the intrinsic physics dynamics of these devices in approximating the complex behavior of the brain. Various short/long-term synaptic plasticities have been recently reported and the analysis of the analog neural networks dynamics has drawn particular interest among researchers. This talk will briefly introduce different applications of memristive crossbars both as accelerators for deep learning and as building blocks for analog neural networks.

Multi-source phonocardiography for the home prevention of heart failure (Noemi Giordano)

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Auscultation is a traditional routine screening tool in the clinical practice for cardiovascular diseases, the first world cause of death according to the World Health Organization. Phonocardiography is its digital counterpart and is growing in importance because it allows, for example, for the assessment of the electromechanical coupling of the heart. A delay in the closure of heart valves, which originates heart sounds, with respect to the ventricular depolarization in the simultaneous electrocardiogram was found to correlate with heart failure. This talk will explore how the design of a noninvasive device for the recording of phonocardiographic (PCG) and electrocardiographic (ECG) signals can help in the prevention of acute episodes of heart failure. The issues regarding the positioning of the digital stethoscope, limiting its applicability in a real-life homecare context, will be investigated along with the potentiality of multi-source phonocardiography as a possible solution.

Relaxation DAC: a new-in-concept interface for the Internet-of-Things (Roberto Rubino)

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Technology scaling has been leading the market of integrated circuits (IC) production of the last decades, to get aster and more efficient digital circuits. Meanwhile, transistor variability has increased the challenges in the design of analog blocks, especially where low area and low power embedded systems are required. This is the case for the Internet of Things (IoT) sensor nodes, where analog interfaces are needed as acting, sensing and communicating blocks in systems which need little to no maintenance, possibly harvesting energy from the environment. The strategy to re-think analog interfaces to be intrinsically mostly-digital, matching-insensitive and able to work at very low supply voltages is proving to be a promising solution. The new-in-concept Relaxation Digital-to-Analog converter (ReDAC) which is going to be presented in the talk, is targeting all these challenges as an ultra low power, bitstream based, easily scalable converter.

Logic-in-Memory computing: an architectural solution to solve the von Neumann bottleneck (Andrea Coluccio)

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Nowadays, computer architectures are widely studied in the literature. The von Neumann scheme, which is essentially composed of CPU and memory, is the most adopted structure. Both elements have made significant improvements in terms of performance: CPUs are becoming faster and more efficient over the years, achieving high clock frequencies and multitasking capabilities. However, memory cannot follow the trend of the CPU in terms of computational speed, so the communication between these two elements represents a bottleneck: a lot of power, energy, and execution time are wasted only to wait for the data from memory. This phenomenon is called the von Neumann bottleneck. What could we do to reduce this drawback? Logic-in-Memory (LiM) could be a solution: it merges storage and computation, so instead of moving data back and forth in the memory hierarchy, calculations are performed directly inside the memory array, reducing the data traffic and improving the performance.

Wearable brain-computer interfaces for daily-life applications (Antonio Esposito)

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Brain-computer interfaces (BCI) are communication channels alternative to natural peripheral nerves and muscles. Indeed, these were born for people with motor impairments, but they opened up new possibilities also in fields like gaming, entertainment, education, and industry. As in many other domains, what yesterday seemed just “magic”, today is becoming a technological reality. However, BCI applications outside the laboratory are still limited, and many research groups are trying to reach higher performances and user-friendliness. The present talk focuses on the development of non-invasive and wearable BCIs to be easily used in daily life. Two well-known paradigms are considered, namely “steady-state visually evoked potentials” and “motor imagery”, and the whole BCI loop will be briefly described, i.e. signal acquisition, signal processing, and possible applications. The overall aim of the R&D described in this talk is to contribute in spreading the BCI technology over the world.

Electronic properties of field-effect-doped phonon-mediated superconductors: an ab-initio study (Davide Romanin)

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The possibility of controlling transport properties of semiconductors by means of an external electric field goes back to the invention of the field-effect transistor (FET) in 1960 and in particular to its modern implementation, the metal-oxide-semiconductor field-effect transistor (MOSFET). However, field-effect doping can also be employed for modifying the conductivity of thin films of noble metals or tuning strongly correlated phases of matter (such as superconductivity, ferromagnetism, insulator-to-metal transitions, charge-density waves, etc.). In order to describe these situations and try to compare the experimental outcomes to theoretical predictions, it is important to understand how FET geometry affects the electronic properties of materials, e.g. using density functional theory (DFT). In this presentation I will show you how field-effect doping is treated in the framework of plane-waves pseudopotential DFT codes and what we can learn from the electronic band-structure using two case studies: niobium nitride (NbN) and hydrogenated diamond thin films.

Data driven patient-specific Cardiovascular Modeling for Early Detection of Coronary Artery Disease (Elisa Fevola)

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Coronary artery disease – the narrowing or blockage of coronary arteries – is one of the leading causes of mortality worldwide. The most common surgical treatment in this case is bypass grafting, where some arteries are used to bypass the blockage. Unfortunately, up to 60% of grafts fail some years after surgery, and the reasons behind such failures are still largely unknown. A huge help in this sense comes from engineers and mathematicians, which can use advanced computational models to simulate how blood flows in the affected arteries, sometimes borrowing modeling techniques from circuit applications. Such models provide information on a patient’s unique physiology, and may help clinicians in performing a better diagnosis and treatment planning. This talk will provide an overview of the use of computational models for cardiovascular applications and the ongoing work to overcome one of its main challenges, the correct choice of boundary conditions.

Multiterminal heterojunction-bipolar transistor solar cell (Gemma Giliberti)

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The photovoltaic (PV) technology, blending cheapness and sustainability, represents a viable alternative to the fossil-fuel system responsible of climate change and global warming. Nowadays, the terrestrial PV market is dominated by c-Si single-junction solar cell achieving an efficiency close to its theoretical limit. However, in recent years, a good solution to overcome this limit is found in Perovskite-Silicon tandem solar cells thanks to the low manufacturing costs as well as the strong solar absorption of the perovskite (PVK) material. Aiming at increasing the PVK/Si solar efficiency and overcoming several constraints of the series connected double junction cell, such as the current matching and the need of tunnel junctions, it is studied an alternative architecture: the three-terminal bipolar transistor (3T-HBT) solar cell. To evaluate the performance potential and design optimization of this novel PVK/Si 3T-HBTsc, the classical Hovel model is extended to deal with the 3T-HBT structure demonstrating efficiencies up to 28.6%.

ARCADIA, a proposal for future fully depleted monolithic active sensors (Raffaele Aron Giampaolo)

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Fully depleted monolithic active pixel sensors (FD-MAPS) are the future of particle and radiation detectors. Full depletion improves charge collection timing and signal to noise ratio versus power dissipated. Furthermore, a smaller material budget and less interconnections push down production costs and time. The approach envisioned by the ARCADIA (Advanced Readout CMOS Architectures with Depleted Integrated sensor Arrays) collaboration consists in implementing the front-end electronics in a deep p-well embedded in an n-doped epitaxial layer, decreasing the sensor capacitance and effectively decoupling the substrate from the electronics. A patterned backside, with appropriate guard rings, allows the necessary high voltage to uniformly deplete the full wafer thickness. Despite the backside implants, the sensor fabrication is fully compatible with a standard CMOS production line. The talk will present the current R&D progress and results of tests on small-scale matrices and test structures.

Internet Congestion Control with Partially Invisible Networks via Reinforcement Learning (Alessio Sacco)

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Alternative internet congestion control strategies have been proposed since the TCP’s birth, mainly because of the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. However, years of research on transport protocols have not solved the tussle between in-network and end-to-end congestion control. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. This talk will introduce a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. A solution like the one presented has been proved to converge to a fair resource allocation after the learning overhead. Finally, it can provide higher throughput and lower delays compared to benchmark solutions based on end-to-end or in-network congestion control.

AI in cancer: from DNA and other molecules to relevant models (Marta Lovino)

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In recent years, the reduction of cost of Next Generation Sequencing (NGS) technologies has enabled the widespread of omics data: genomics (DNA), transcriptomics (RNA), proteomics (proteins), and many others. However, omics data are characterized by complexity and high dimensionality, which makes the biological interpretation a tricky process. Nowadays, AI contributed to the major technological advances in many fields, including biology. My work focuses on two areas in cancer analysis: gene fusion and multi-omics data integration. Gene fusions consist of a biological event in which two genes break and their portions are juxtaposed together, potentially causing cancer. Therefore, deep learning models have been exploited to predict the probability for a gene fusion to be involved in cancer genesis and progression. Since cancer is a complex phenomenon, one omic alone provides only partial information on the biological processes. Therefore, multi-omics data integration is crucial in the cancer domain and AI techniques allow a more efficient prediction.

Air pollution monitoring for the smart cities of tomorrow (Edoardo Giusto)

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Air quality, especially particulate matter, has attracted growing attention from governments, industry, and academia in recent years, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage.The talk would describe the development of a low-cost particulate matter monitoring system, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the influence of all model variables and the quality of different calibration strategies. Tests of statically immovable stations include an analysis of accuracy and sensors’ reliability made by comparing our results with more accurate and expensive standard β-radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events.

Microwave imaging for brain stroke monitoring (David O. Rodriguez-Duarte)

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A brain stroke is a widespread disorder that affects around one in six people in their life, top-ranking worldwide as causes of death, disability, and dementia in otherwise healthy adults. It is a medical emergency caused by the interruption of the regular supply of oxygen-rich blood to the brain, leading to the loss of millions of brain cells per minute. Thus, requiring prompt treatment. Stroke care widely relies on brain imaging technologies, identifying the specific pathophysiologic conditions for tailored treatment and enhancing effectiveness. The most well-established solutions are computerized X-ray tomography (CT) and magnetic resonance imaging (MRI). Moreover, in recent years microwave imaging (MWI) has emerged as a complementary technology that allows early diagnosis and bed-side follow-up. MWI relies on the electric contrast between the healthy brain tissues and the pathologies to retrieve essential diagnostic information according to their typology and their physiopathological status.

Industry 4.0: Industrial IoT Enhancement and WSN Performance Analysis (Mohammad Ghazi Vakili)

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The term “Industry 4.0” is the current automation and data exchange trend in the manufacturing environment. It includes cyber-physical systems, the Internet of things, and cloud computing, which depict factories as intelligent environments. Their composing elements (e.g., machines, storage systems, edge and system devices, etc.) are seen as smart components, able to communicate with each other and take decisions autonomously. These benefits come when reliable communication is established between the shop floor and the factory’s top floor. Depending on the application, Wireless Sensor Network (WSN) and Internet of Things (IoT) help communicate devices and people with each other, both within the factory and outside it, reaching customers, suppliers, and broader governing bodies. Nowadays, Wireless communication plays a significant role in Industry 4.0. WSNs help factories to gather data and send control commands to the factory shop floor. WSN and IoT relevant challenges guarantee reliable communication in the factory where constrained communication is often required. The talk discusses and explains methods and algorithms to increase performance indicators in Industry 4.0, to have reliable communication between the shop floor and business applications in the factory.

Hardware Acceleration for Microwave Imaging Algorithms (Mohammad Amir Mansoori)

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Microwave Imaging (MI) in medical applications is a technique to observe the internal structure of an object by using electromagnetic fields at microwave frequencies. One of the challenges in MI is the high execution time of the image reconstruction algorithms making it difficult to be used in real-time systems. Our aim is to increase the speed of the recurrent algorithms used in MI by means of hardware acceleration techniques. These algorithms include compute-intensive parts which we call “kernels”. In this presentation, I will introduce you some of the most commonly-used kernels in MI and the methodology to design specific hardware accelerators in FPGAs for these kernels by means of High Level Synthesis (HLS) tool. These kernels include a forward electromagnetic solver called FDTD and some Machine Learning classifiers. The performance is compared with CPU and GPU-based designs in terms of processing time, power consumption and resource usage.

Assessing the Effectiveness of Enhanced Virtual Reality Systems for Accurate Robot Teleoperation Tasks
(Francesco De Pace)

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Over the last decade, many studies have focused on Virtual Reality (VR) frameworks for remotely controlling robotic systems. Although VR systems have been used to teleoperate robots in simple scenarios, their effectiveness in terms of accuracy, speed, and usability has not been rigorously evaluated for complex tasks that require accurate trajectories. In this talk, an Enhanced Virtual Reality (EVR) framework for robotic teleoperation is evaluated to assess if it can be efficiently used in complex tasks that require accurate control of the robotic end-effector. The environment and the employed robot are captured using RGB-D cameras, while the remote user controls the motion of the robot with VR controllers. The captured data are transmitted and reconstructed in 3D so as to allow the remote user to monitor the task execution progress in real time, using a VR headset. The EVR system is compared with two other interface alternatives: i) teleoperation in pure VR (the model of the robot is rendered with respect to its real joint states), and ii) teleoperation in EVRR (the model of the robot is superimposed on the real robot).

Experimental Quantum Reading with Photon Counting (Giuseppe Ortolano)

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The goal of quantum sensing is to reach an advantage over classical benchmarks using quantum resources. In the protocol of quantum reading this advantage is obtained for the task of information recovery from a classical digital memory. In the work presented we showed, both theoretically and experimentally, how this advantage can be achieved using an entangled two-mode squeezed vacuum source paired with a photon counting measurement and a maximum likelihood decision. This quantum strategy is able to outperform any classical strategy for the same number of input photons. Our experimental results prove how quantum entanglement and simple optics are able to enhance the redout of digital data, paving the way to real application of quantum reading as well as any other model based on the binary discrimination of bosonic loss.

Point cloud denoising with graph convolutional neural networks (Francesca Pistilli)

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Point clouds are becoming increasingly popular due to the availability of instruments such as LiDARs and the interest in exploiting geometric representation in many challenging applications such as autonomous driving, medical imaging or virtual reality. However, the acquisition methods are imperfect and insert a non-negligible noise. In general, a point cloud is a set of points in the 3D space to which a specific location is associated, the insertion of the noise changes their position and the collected shape is different from the original one. Therefore, in case of safety-critical applications, it is important to perform denoising of point clouds, to recover the original shape from corrupted data. This talk will be focused on the challenges of dealing with point clouds, the limitations of the existing methods in the context of point cloud denoising and the presentation of a neural network based on graph convolutions as a possible solution.

Impedance-based microfluidic devices for personalized medicine applications (Susana Fuentes Vélez)

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There are numerous challenges derived from the personalized health paradigm and novel diagnostic and therapeutic approaches are required. Interest in impedance-based assays is rising due to their remarkable advantages, including label-free, low cost, non-invasive, non-destructive, quantitative and real-time. In this application-oriented talk, first, a custom-made impedance measuring system based on electric cell-substrate impedance sensing (ECIS) will be introduced. Emphasis will be given to its potential in cancer treatment decision and early detection of chemoresistance. Then, jumping from cell analysis to more complex systems such as 3D biological constructs, an overview of a microfluidic device under construction, for impedance-based measurements of biopsies, will be presented.