The protection of processor-based systems to mitigate the harmful effect of transient faults (soft errors) is gaining importance as technology shrinks. At the same time, for large segments of embedded markets, parameters like cost and performance continue to be as important as reliability. This paper presents a compiler-based methodology for facilitating the design of fault-tolerant embedded systems. The methodology is supported by an infrastructure that permits to easily combine hardware/software soft errors mitigation techniques in order to best satisfy both usual design constraints and dependability requirements. It is based on a generic microprocessor architecture that facilitates the implementation of software-based techniques, providing a uniform isolated-from-target hardening core that allows the automatic generation of protected source code (hardened code). Two case studies are presented. In the first one, several software-based mitigation techniques are implemented and evaluated showing the flexibility of the infrastructure. In the second one, a customized fault tolerant embedded system is designed by combining selective protection on both hardware and software. Several trade-offs among performance, code size, reliability, and hardware costs have been explored. Results show the applicability of the approach. Among the developed software-based mitigation techniques, a novel selective version of the well known SWIFT-R is presented.

The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace.

Jupyter notebooks provide an interactive programming environment that allows writing code, text, equations, and multimedia resources. They are widely used as a teaching support tool in computer science and engineering courses. However, manual grading programming assignments in Jupyter notebooks is a challenging task, thus using an automatic grader becomes a must. This paper presents UNCode notebook auto-grader, that offers summative and formative feedback instantaneously. It provides instructors with an easy-to-use grader generator within the platform, without having to deploy a new server. Additionally, we report the experience of employing this tool in two artificial intelligence courses: Introduction to Intelligent Systems and Machine Learning. Several programming activities were carried out using the proposed tool. Analysis of students’ interactions with the tool and the students’ perceptions are presented. Results showed that the tool was widely used to evaluate their tasks, as a large number of submissions were performed. Students expressed positive opinions mostly, giving feedback about the auto-grader, highlighting the usefulness of the immediate feedback and the grading code, among other aspects that helped them to solve the activities. Results remarked on the importance of providing clear grading code and formative feedback to help the students to identify errors and correct them.

There is an increasing concern about the mitigation of radiation effects in embedded systems. This fact is demanding new flexible design methodologies and tools that allow dealing with design constraints and dependability requirements at the same time. This paper presents a novel proposal to design radiation-tolerant embedded systems combining hardware and software mitigation techniques. A hardening infrastructure, which facilitates the design space exploration and the trade-offs analyses, has been developed to support this fault tolerance co-design approach. The advantages of our proposal are illustrated by means of a case study.