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IEEE Portugal Section's Outstanding MSc Thesis Award: Celebrating Academic Excellence

The IEEE Portugal Section proudly announces the winners of the fourth edition of the Outstanding MSc Thesis Award, honoring exceptional master’s theses within technical areas covered by the IEEE’s scope. Former MSc students enrolled in Portuguese Higher Education Institutions who successfully defended their theses in a public session within the last 12 months were eligible for this prestigious award.

A panel of experts, including members from the IEEE Portugal Section and industry professionals, meticulously assess submissions. Applicants were urged to submit the thesis, along with a compelling Motivation Letter and a declaration confirming thesis evaluation at a higher education institution.

Recipients of the Outstanding MSc Thesis Award:

  • João Carlos Ramos Gonçalves de Matos (MSc Thesis at FEUP): Research Frameworks towards Health Equity This thesis delves into healthcare AI’s potential biases, particularly in racial and ethnic disparities. It explores data science challenges in health equity and proposes three research frameworks validated with a medical database. These frameworks address treatment allocation likelihoods, treatment effectiveness across subpopulations, and correction methods for biased medical devices. The thesis emphasizes the imperative of prioritizing health equity in healthcare research.

  • João Pedro Machado Vitorino (MSc Thesis at ISEP): Realistic Adversarial Machine Learning to Improve Network Intrusion Detection Focusing on AI in cybersecurity, this thesis tackles adversarial vulnerabilities in Machine Learning (ML)-based Network Intrusion Detection (NID) systems. It presents a methodology for robustness analysis and an intelligent approach for generating realistic adversarial examples. The study demonstrates how adversarial attacks can impact cyber-attacks and highlights the importance of addressing ML vulnerabilities in cybersecurity.

  • José Pedro Moura Costa Pinto (MSc Thesis at UA): New Architecture for Rotational Self-Adaptive Electromagnetic Energy Harvester This research develops an electromagnetic rotational harvester optimized for low-frequency mechanical sources. It introduces an intelligent self-adaptive mechanism called Polarity Switching, dynamically optimizing coil polarity based on magnet angular displacement. Experimental results show significant power gains, indicating potential advancements in self-powering solutions at micro and macro scales.

  • Pedro Miguel Nicolau Escaleira (MSc Thesis at UA): Securing Real World 5G MEC Deployments Focusing on Edge Computing in 5G networks, this thesis proposes a Multi-access Edge Computing (MEC) architecture and a Moving Target Defense as a Service (MTDaaS) mechanism. The MTDaaS enhances application security by increasing heterogeneity through dynamic application versioning. Evaluations demonstrate increased difficulty for potential intruders in assaulting MEC applications.

Congratulations to the esteemed recipients for their exceptional achievements! The IEEE Portugal Section extends appreciation to all applicants for their invaluable contributions and commitment to academic excellence and innovation. Their contributions stand as exemplary endeavors in advancing their respective technical fields. To explore previous winners and their remarkable achievements, visit this link for more information.

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