The Development of Sustainable Engineering with PjBL during the COVID-19 Pandemic.

Sustainable Engineering education must provide cyber-physical and distributed systems competencies, such as the Internet of Things (IoT), to contribute to the Sustainable Development Goals (SDG). The COVID-19 pandemic caused profound impacts arising from a traditional on-site teaching model rupture and demanded distance learning for engineering students. In this context, we considered the following Research Questions (RQ): How can Project Based Learning (PjBL) be applied in hardware and software courses from the Engineering curriculum to foster practical activities during the COVID-19 pandemic? Is the student performance in the fully remote offering comparable to the face-to-face offering? (RQ1); Which Sustainable Development Goals are related to the Engineering students' project themes? (RQ2). Regarding RQ1, we present how PjBL was applied in first-, third- and fifth-year Computer Engineering Courses to support 31 projects of 81 future engineers during the COVID-19 pandemic. Student grades in a Software Engineering course indicate no relevant differences between student performance in remote and face-to-face offerings. Regarding RQ2, most Computer Engineering students from the Polytechnic School of the University of São Paulo in 2020 and 2021 decided to create projects related to SDG 3-Good Health and Well-being, SDG 8-Decent Work and Economic Growth and SDG 11-Sustainable Cities and Communities. Most projects were related to health and well-being, which was an expected behavior according to how health issues were brought into highlight during the pandemic.

A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning.

Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors' knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.

Hands-Free Authentication for Virtual Assistants with Trusted IoT Device and Machine Learning.

Virtual assistants, deployed on smartphone and smart speaker devices, enable hands-free financial transactions by voice commands. Even though these voice transactions are frictionless for end users, they are susceptible to typical attacks to authentication protocols (e.g., replay). Using traditional knowledge-based or possession-based authentication with additional invasive interactions raises users concerns regarding security and usefulness. State-of-the-art schemes for trusted devices with physical unclonable functions (PUF) have complex enrollment processes. We propose a scheme based on a challenge response protocol with a trusted Internet of Things (IoT) autonomous device for hands-free scenarios (i.e., with no additional user interaction), integrated with smart home behavior for continuous authentication. The protocol was validated with automatic formal security analysis. A proof of concept with websockets presented an average response time of 383 ms for mutual authentication using a 6-message protocol with a simple enrollment process. We performed hands-free activity recognition of a specific user, based on smart home testbed data from a 2-month period, obtaining an accuracy of 97% and a recall of 81%. Given the data minimization privacy principle, we could reduce the total number of smart home events time series from 7 to 5. When compared with existing invasive solutions, our non-invasive mechanism contributes to the efforts to enhance the usability of financial institutions' virtual assistants, while maintaining security and privacy.

Rede de alta velocidade para a transmissão, armazenamento e visualização de imagens médicas / High Speed Networks for Transmission, Storage and Viewing of Medical Images

A distribuição dos serviços de saúde e o compartilhamento de informações clínicas, utilizando sistemas de telecomunicação, envolvem um conjunto diverso e complexo de tecnologias. Além disso, se a informação a ser compartilhada contém imagens, um problema especial em cardiologia é como distribuir imagens dinâmicas obtidas em estudos de ecocardiografia e de angiografia com qualidade de serviço. As imagens individuais são facilmente transmitidas, mas o volume total, considerando todas as imagens em uma seqüência, é enorme podendo chegar facilmente na ordem de Giga Bytes por paciente. Os avanços em tecnologias de comunicações e em informática facilitam a médicos e especialistas localizados em um centro de referência a troca de informações com médicos e profissionais de saúde em local distante, em outro centro, ou mesmo com o próprio paciente. Embora em especialidades como cardiologia, radiologia e patologia, as informações relevantes sobre o paciente sejam inerentemente digitais, a necessidade de alta qualidade associada ao volume de dados impõem limitações na prática de ações de Medicina à distância através dos meios de comunicação convencionais