Branden Ghena

Tock began 10 years ago as a research operating system developed by academics to help other academics build urban sensing applications. By leveraging a new language (Rust) and new hardware protection mechanisms, Tock enabled Multiprogramming a 64 kB Computer Safely and Efficiently. Today, it is an open source project with a vibrant community of users and contributors. It is deployed on root of trust hardware in data center servers and on millions of laptops; it is used to develop automotive and space products, wearable electronics, and hardware security tokens--all while remaining a platform for operating systems research. This paper focuses on the impact of Tock's technical design on its adoption, the challenges and unexpected benefits of using a type safe language (Rust)--particularly in security sensitive settings--and the experience of supporting a production open4source operating system from academia.

Today's computational devices are overwhelmingly wireless. To realize wireless communication, today's devices use a grab bag of protocols (Bluetooth, WiFi, 4G/5G, LoRa, NFC, etc.) and no one universal standard has emerged. This diversity presents a ripe pedagogical opportunity to introduce students to the fundamental tradeoffs and design decisions inherent to wireless communication and networking. Furthermore, many wireless protocols are accessible to study in a classroom (in fact, many we all use daily), which lends to a very hands-on course.

This paper explores the practical implementation and challenges associated with AI and ML in the field of text recognition.It presents a variety of innovative solutions aimed at improving the overall accuracy of text recognition models.These solutions encompass effectively managing data quality and diversity, optimizing large-scale training and inference procedures, providing robust support for multiple languages and fonts, tackling variations in text layout and arrangement, accurately recognizing handwritten text, and enhancing model interpretability and explainability.By addressing these key areas, the proposed solutions aim to significantly enhance the performance and reliability of text recognition systems.As we delve deeper into this investigation, our focus sharpens on the implementation of artificial intelligence and machine learning in the field of text recognition.This paper presents innovative solutions that not only aim to enhance accuracy but also address data quality management, optimize large-scale training, support multilingualism and different fonts, handle layout variations, recognize handwritten text, and improve model interpretability.By addressing these crucial aspects, our proposed solutions have the potential to enhance the overall performance and reliability of text recognition systems, pushing the boundaries of AI and ML applications in this field.