Study of the performance of the prototype EIC dRICH readout based on SiPM with thermoelectric cooling

L'Electron-Ion Collider è un futuro acceleratore di particelle che approfondirà la nostra conoscenza riguardo l'interazione forte tramite la collisione di elettroni con nuclei e protoni. Uno dei progetti attualmente considerati per la costruzione del rivelatore, il dual-radiator RICH, prevede l'impiego di due radiatori Cherenkov, sui quali verranno montati dei fotorivelatori per rilevare l'emissione della luce Cherenkov e risalire alla massa delle particelle. L'opzione di base per questi rivelatori sono i sensori al silicio SiPM. Questo lavoro di tesi si basa sullo studio delle prestazioni di un prototipo per l'acquisizione dei dati rilevati dai SiPM che sfrutta l'effetto termoelettrico per raffreddare la zona in cui sono situati i sensori. L'analisi dei dati acquisiti ha portato alla conclusione che le prestazioni del prototipo sono confrontabili con quelle misurate all'interno di una camera climatica quando si trovano alla stessa temperatura.

Robotic grasping: State of the art and modular real-world implementation

Robotic Grasping is an important research topic in robotics since for robots to attain more general-purpose utility, grasping is a necessary skill, but very challenging to master. In general the robots may use their perception abilities like an image from a camera to identify grasps for a given object usually unknown. A grasp describes how a robotic end-effector need to be positioned to securely grab an object and successfully lift it without lost it, at the moment state of the arts solutions are still far behind humans. In the last 5–10 years, deep learning methods take the scene to overcome classical problem like the arduous and time-consuming approach to form a task-specific algorithm analytically. In this thesis are present the progress and the approaches in the robotic grasping field and the potential of the deep learning methods in robotic grasping. Based on that, an implementation of a Convolutional Neural Network (CNN) as a starting point for generation of a grasp pose from camera view has been implemented inside a ROS environment. The developed technologies have been integrated into a pick-and-place application for a Panda robot from Franka Emika. The application includes various features related to object detection and selection. Additionally, the features have been kept as generic as possible to allow for easy replacement or removal if needed, without losing time for improvement or new testing.