A proposal for a virtual museum of computer science

A proposal for a virtual museum of computer science

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

A ROS-based Workspace Control and Trajectory Planner for a seven degrees of freedom Robotic arm

In this Bachelor Thesis I want to provide readers with tools and scripts for the control of a 7DOF manipulator, backed up by some theory of Robotics and Computer Science, in order to better contextualize the work done. In practice, we will see most common software, and developing environments, used to cope with our task: these include ROS, along with visual simulation by VREP and RVIZ, and an almost "stand-alone" ROS extension called MoveIt!, a very complete programming interface for trajectory planning and obstacle avoidance. As we will better appreciate and understand in the introduction chapter, the capability of detecting collision objects through a camera sensor, and re-plan to the desired end-effector pose, are not enough. In fact, this work is implemented in a more complex system, where recognition of particular objects is needed. Through a package of ROS and customized scripts, a detailed procedure will be provided on how to distinguish a particular object, retrieve its reference frame with respect to a known one, and then allow navigation to that target. Together with technical details, the aim is also to report working scripts and a specific appendix (A) you can refer to, if desiring to put things together.

The “FRA wheel” as a teaching tool to elaborate on the identity aspects of physics and mathematics as disciplines in interdisciplinary contexts

This thesis project is framed in the research field of Physics Education and aims to contribute to the reflection on the importance of disciplinary identities in addressing interdisciplinarity through the lens of the Nature of Science (NOS). In particular, the study focuses on the module on the parabola and parabolic motion, which was designed within the EU project IDENTITIES. The project aims to design modules to innovate pre-service teacher education according to contemporary challenges, focusing on interdisciplinarity in curricular and STEM topics (especially between physics, mathematics and computer science). The modules are designed according to a model of disciplines and interdisciplinarity that the project IDENTITIES has been elaborating on two main theoretical frameworks: the Family Resemblance Approach (FRA), reconceptualized for the Nature of science (Erduran & Dagher, 2014), and the boundary crossing and boundary objects framework by Akkerman and Bakker (2011). The main aim of the thesis is to explore the impact of this interdisciplinary model in the specific case of the implementation of the parabola and parabolic motion module in a context of preservice teacher education. To reach this purpose, we have analyzed some data collected during the implementation in order to investigate, in particular, the role of the FRA as a learning tool to: a) elaborate on the concept of “discipline”, within the broader problem to define interdisciplinarity; b) compare the epistemic core of physics and mathematics; c) develop epistemic skills and interdisciplinary competences in student-teachers. The analysis of the data led us to recognize three different roles played by the FRA: FRA as epistemological activator, FRA as scaffolding for reasoning and navigating (inhabiting) the complexity, and FRA as lens to investigate the relationship between physics and mathematics in the historical case.