Validation of the ''full White Rabbit'' setup in the context of the KM3NeT experiment

The newborn Neutrino Astronomy is one of the most promising branches of astroparticle physics. Its main goal is to measure astrophysical neutrino fluxes and to identify their sources, thus allowing us to achieve a better understanding of the sources and the acceleration mechanisms of Cosmic Rays. Due to their unique physical properties, neutrinos constitute an optimal probe to observe high-energy astrophysical phenomena, as they propagate throughout the Universe for very large distances without being absorbed or deflected by magnetic fields along their travel. For this reason neutrino astronomy is expected to become decisive for settling questions about Cosmic Ray sources and acceleration mechanisms which cannot be solved by other means. The detection of astrophysical neutrinos is achieved by the deployment of neutrino telescopes deep under ice or water. The development of my thesis has been carried out within the KM3NeT experiment, which consists in two underwater detectors that are going to be installed in the depths of the Mediterranean Sea, reaching a volume greater than 1 km3 of sea water instrumented with thousands of optical sensors. Due to the inaccessibility of the underwater sites, the technology used in the detectors must be carefully evaluated and validated with great accuracy before the deployment. The work of this thesis is aimed at the integration and validation at the Bologna Common Infrastructure (BCI) laboratory of a test bench based on the forthcoming network configuration of the detectors, which relies on a full standard White Rabbit network. The setup recreates a full Detection Unit with all electronic boards and relative Data AcQuisition system resources. The validation of the setup represents a focal point for the transition of the experiment towards its next phase. In particular, in this thesis the validation of the time synchronization and of the integrity and stability of optical and monitoring channel data streams will be illustrated

Advanced Radio Frequency Identification Design and Applications

Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID

Design of a distributed data acquisition system for the ITER’s neutral beam

The International Thermonuclear Experimental Reactor (ITER) is a groundbreaking interna- tional collaboration aimed at developing fusion energy as a clean, safe, and virtually limitless source of power that brings together scientists, engineers, and experts from 35 countries to con- struct and operate the world’s largest experimental fusion reactor. Through the fusion of hy- drogen isotopes, ITER seeks to replicate the process that powers the sun and stars, harnessing the immense energy released to generate electricity. With its ambitious goals and cutting-edge technology, ITER represents a significant milestone in the pursuit of sustainable and abundant energy for the future. As part of the ITER project, the development of several systems of plasma heating is needed to achieve fusion conditions in order to reach plasma ignition. One of such heating systems is the Heating Neutral Beam (HNB), which is designed to inject a energetic beam of neutral atoms into the plasma and heat the fusion plasma by coulomb collisions of such with the plasma. This system requires of several components such as power supplies, cryopumps and cooling components working together in order to achieve a controlled and safe operation of the HNB. It also needs to work coordinated with the experimental control with high availability. The neutral beam control system is, therefore, responsible for the correct and safe operation of the two HNB units installed at ITER. The project presents an overview of the instrumentation and control system currently being developed for the Neutral Beam units and presents the development and design of a remote distributed data acquisition system prototype for the Neutral Beam instrumentation and control system. The performance of the prototype will be measured and evaluated to determine if such solution is fit for ITER requirements and can therefore be implemented into the Neutral Beam control system and other control systems within the reactor components. This project was developed under the Traineeship program by the European Joint Undertaking for ITER and the Development of Fusion Energy, Fusion For Energy (F4E). This report presents the work the author performed during such contract and under the guidance of the program’s supervisor

Using High-Level Processing of Low-Level Signals to Actively Assist Surgeons with Intelligent Surgical Robots

Robotic surgical systems are increasingly used for minimally-invasive surgeries. As such, there is opportunity for these systems to fundamentally change the way surgeries are performed by becoming intelligent assistants rather than simply acting as the extension of surgeons' arms. As a step towards intelligent assistance, this thesis looks at ways to represent different aspects of robot-assisted surgery (RAS). We identify three main components: the robot, the surgeon actions, and the patient scene dynamics. Traditional learning algorithms in these domains are predominantly supervised methods. This has several drawbacks. First many of these domains are non-categorical, like how soft-tissue deforms. This makes labeling difficult. Second, surgeries vary greatly. Estimation of the robot state may be affected by how the robot is docked and cable tensions in the instruments. Estimation of the patient anatomy and its dynamics are often inaccurate, and in any case, may change throughout a surgery. To obtain the most accurate information, these aspects must be learned during the procedure. This limits the amount of labeling that could be done. On the surgeon side, different surgeons may perform the same procedure differently and the algorithm should provide personalized estimations for surgeons. All of these considerations motivated the use of self-supervised learning throughout this thesis. We first build a representation of the robot system. In particular, we looked at learning the dynamics model of the robot. We evaluate the model by using it to estimate forces. Once we can estimate forces in free space, we extend the algorithm to take into account patient-specific interactions, namely with the trocar and the cannula seal. Accounting for surgery-specific interactions is possible because our method does not require additional sensors and can be trained in less than five minutes, including time for data collection. Next, we use cross-modal training to understand surgeon actions by looking at the bottleneck layer when mapping video to kinematics. This should contain information about the latent space of surgeon-actions, while discarding some medium-specific information about either the video or the kinematics. Lastly, to understand the patient scene, we start with modeling interactions between a robot instrument and a soft-tissue phantom. Models are often inaccurate due to imprecise material parameters and boundary conditions, particularly in clinical scenarios. Therefore, we add a depth camera to observe deformations to correct the results of simulations. We also introduce a network that learns to simulate soft-tissue deformation from physics simulators in order to speed up the estimation. We demonstrate that self-supervised learning can be used for understanding each part of RAS. The representations it learns contain information about signals that are not directly measurable. The self-supervised nature of the methods presented in this thesis lends itself well to learning throughout a surgery. With such frameworks, we can overcome some of the main barriers to adopting learning methods in the operating room: the variety in surgery and the difficulty in labeling enough training data for each case

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Trajectory Prediction with Event-Based Cameras for Robotics Applications

This thesis presents the study, analysis, and implementation of a framework to perform trajectory prediction using an event-based camera for robotics applications. Event-based perception represents a novel computation paradigm based on unconventional sensing technology that holds promise for data acquisition, transmission, and processing at very low latency and power consumption, crucial in the future of robotics. An event-based camera, in particular, is a sensor that responds to light changes in the scene, producing an asynchronous and sparse output over a wide illumination dynamic range. They only capture relevant spatio-temporal information - mostly driven by motion - at high rate, avoiding the inherent redundancy in static areas of the field of view. For such reasons, this device represents a potential key tool for robots that must function in highly dynamic and/or rapidly changing scenarios, or where the optimisation of the resources is fundamental, like robots with on-board systems. Prediction skills are something humans rely on daily - even unconsciously - for instance when driving, playing sports, or collaborating with other people. In the same way, predicting the trajectory or the end-point of a moving target allows a robot to plan for appropriate actions and their timing in advance, interacting with it in many different manners. Moreover, prediction is also helpful for compensating robot internal delays in the perception-action chain, due for instance to limited sensors and/or actuators. The question I addressed in this work is whether event-based cameras are advantageous or not in trajectory prediction for robotics. In particular, if classical deep learning architecture used for this task can accommodate for event-based data, working asynchronously, and which benefit they can bring with respect to standard cameras. The a priori hypothesis is that being the sampling of the scene driven by motion, such a device would allow for more meaningful information acquisition, improving the prediction accuracy and processing data only when needed - without any information loss or redundant acquisition. To test the hypothesis, experiments are mostly carried out using the neuromorphic iCub, a custom version of the iCub humanoid platform that mounts two event-based cameras in the eyeballs, along with standard RGB cameras. To further motivate the work on iCub, a preliminary step is the evaluation of the robot's internal delays, a value that should be compensated by the prediction to interact in real-time with the object perceived. The first part of this thesis sees the implementation of the event-based framework for prediction, to answer the question if Long Short-Term Memory neural networks, the architecture used in this work, can be combined with event-based cameras. The task considered is the handover Human-Robot Interaction, during which the trajectory of the object in the human's hand must be inferred. Results show that the proposed pipeline can predict both spatial and temporal coordinates of the incoming trajectory with higher accuracy than model-based regression methods. Moreover, fast recovery from failure cases and adaptive prediction horizon behavior are exhibited. Successively, I questioned how much the event-based sampling approach can be convenient with respect to the classical fixed-rate approach. The test case used is the trajectory prediction of a bouncing ball, implemented with the pipeline previously introduced. A comparison between the two sampling methods is analysed in terms of error for different working rates, showing how the spatial sampling of the event-based approach allows to achieve lower error and also to adapt the computational load dynamically, depending on the motion in the scene. Results from both works prove that the merging of event-based data and Long Short-Term Memory networks looks promising for spatio-temporal features prediction in highly dynamic tasks, and paves the way to further studies about the temporal aspect and to a wide range of applications, not only robotics-related. Ongoing work is now focusing on the robot control side, finding the best way to exploit the spatio-temporal information provided by the predictor and defining the optimal robot behavior. Future work will see the shift of the full pipeline - prediction and robot control - to a spiking implementation. First steps in this direction have been already made thanks to a collaboration with a group from the University of Zurich, with which I propose a closed-loop motor controller implemented on a mixed-signal analog/digital neuromorphic processor, emulating a classical PID controller by means of spiking neural networks

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems