ISEL - Engenharia Electrónica, Telecomunicações e Computadores
Permanent URI for this community
Browse
Browsing ISEL - Engenharia Electrónica, Telecomunicações e Computadores by Field of Science and Technology (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
Now showing 1 - 10 of 11
Results Per Page
Sort Options
- Aplicações de modelos de linguagem de grande escala na cibersegurançaPublication . Conceição, Tiago Miguel Pestana; Cruz, Nuno Miguel MachadoA crescente complexidade e sofisticação das ameaças no ciberespaço têm impulsionado a procura por soluções inovadoras e eficientes no campo da cibersegurança. Neste contexto, conduziu-se uma investigação com o objetivo de avaliar a viabilidade de Large Language Models (LLMs) no que concerne à automatização da geração de código e configurações no âmbito da cibersegurança. A investigação centrou-se em mecanismos de ciberdefesa e aplicações de educação em cibersegurança, com particular ênfase em soluções de geração de honeypots, malware e exercícios de Capture The Flag (CTF). Foram avaliados sete modelos, incluindo o GPT-4, Gemini Pro e Claude Opus 3. A metodologia de avaliação assentou no desenvolvimento de dois mecanismos de avaliação, sendo o primeiro um novo benchmark Cybersecurity Language Understading (CSLU), baseado no Massive Multitask Language Understanding, constituído por questões de escolha múltipla sobre diversos domínios do conhecimento. As prompts foram concebidas com o intuito de avaliar o estado de conhecimento de cada modelo relativamente aos tópicos supracitados. O segundo mecanismo consistiu na avaliação da consistência, criatividade e adaptabilidade dos modelos referente à geração de artefactos. Os resultados evidenciaram uma notória proeminência referente ao tópico de malware, com quatro destes a alcançarem a pontuação máxima. Por outro lado, o desempenho na tarefa de CTF revelou uma maior variação de resultados. De um modo geral, os modelos GPT- 4, Gemini Pro e Claude 3 Opus demonstraram resultados consistentemente superiores entre os modelos estudados. Num segundo momento, pretendeu-se desenvolver uma ferramenta baseada na web, com o objetivo de fornecer uma prova de conceito dos estudos anterior realizados. A referida ferramenta, recorrendo aos melhores LLMs estudados, permite ao utilizador criar e lançar automaticamente serviços de segurança, como os mencionados honeypots ou exercícios de CTF. De uma perspetiva global, estas descobertas sugerem que a aplicação de LLMs em atividades de cibersegurança pode ser altamente vantajosa.
- An autonomic parallel strategy for exhaustive search tree algorithms on shared or heterogeneous systemsPublication . Gonçalves de Oliveira Passos, Fernanda; Rebello, Vinod E. F.Backtracking branch-and-prune (BP) algorithms and their variants are exhaustive search tree techniques widely employed to solve optimization problems in many scientific areas. However, they characteristically often demand significant amounts of computing power for problem sizes representative of real-world scenarios. Given that their search domains can often be partitioned, these algorithms are frequently designed to execute in parallel by harnessing distributed computing systems. However, to achieve efficient parallel execution times, an effective strategy is required to balance the nonuniform partition workloads across the available resources. Furthermore, with the increasing integration of servers with heterogeneous resources and the adoption of resource sharing, balancing workloads is becoming complex. This paper proposes a strategy to execute parallel BP algorithms more efficiently on even shared or heterogeneous distributed systems. The approach integrates a self-adjusting dynamic partitioning method in the BP algorithm with a dynamic scheduler, provided by an application middleware, which manages the parallel execution while addressing any issues of imbalance. Empirical results indicate better scalability with efficiencies above 90% for instances of an application case study for the discretizable molecular distance geometry problem (DMDGP). Improvements of up to 38% were obtained in execution speed-ups compared to a more traditional parallel BP implementation for DMDGP.
- Design of a Cardiopulmonary antenna for vital signs monitoring robust to different subjectsPublication . Cardoso, João; Pinho, Pedro; Gouveia, Carolina; Albuquerque, DanielWith the advancement of wireless diagnosis and treatment technologies, antennas deployed close to the human body are now widely used. The use of on-body antennas, along with other technologies, presents itself as an innovative method for detecting and monitoring vital signs. These antennas can be attached directly on the body or on clothes, making it comfortable to use and less invasive when compared to conventional methods, allowing at-home monitoring of elderly patients or high risk workers with a single antenna. In this paper, a robust high bandwidth patch antenna was developed to operate in the dedicated Industrial, Scientific and Medical frequency band, namely at 2.45 GHz, capable of monitoring vital signs in any subject. This work presents the design and results of a robust cardiopulmonary antenna, to be further used to monitor the respiratory rate of five different subjects, each one with different physiognomy.
- Development and evaluation of a mobile application with augmented reality for guiding visitors on hiking trailsPublication . Silva, Rute; Jesus, Rui; Jorge, PedroTourism on the island of Santa Maria, Azores, has been increasing due to its characteristics in terms of biodiversity and geodiversity. This island has several hiking trails; the available information can be consulted in pamphlets and physical placards, whose maintenance and updating is difficult and expensive. Thus, the need to improve the visitors’ experience arises, in this case, by using the technological means currently available to everyone: a smartphone. This paper describes the development and evaluation of the user experience of a mobile application for guiding visitors on said hiking trails, as well as the design principles and main issues observed during this process. The application is based on an augmented reality interaction model providing visitors with an interactive and recreational experience through Augmented Reality in outdoor environments (without additional marks in the physical space and using georeferenced information), helping in navigation during the route and providing updated information with easy maintenance. For the design and evaluation of the application, two studies were carried out with users on-site (Santa Maria, Azores). The first had 77 participants, to analyze users and define the application’s characteristics, and the second had 10 participants to evaluate the user experience. The feedback from participants was obtained through questionnaires. In these questionnaires, an average SUS (System Usability Scale) score of 83 (excellent) and positive results in the UEQ (User Experience Questionnaire) were obtained.
- Drug recommendation system based on symptoms and user sentiment analysis (DRecSys-SUSA)Publication . Pinto, Ana Sofia Simões; Pato, Matilde Pós-de-Mina; Datia, Nuno Miguel SoaresAbstract The rapid growth of user-generated content on multiple online platforms has opened opportunities for improving decision-making across various domains, including healthcare. This dissertation focuses on the development of our Drug Recommendation System based on usergenerated content (DRecSys-SUSA), designed to assist healthcare professionals and patients by providing personalized drug recommendations and supporting informed decision-making. Our research leverages the UCI ML Drug Review dataset as the foundation for developing an advanced recommendation system. Our solution utilizes a combination of modern AI techniques, including Exploratory Data Analysis (EDA), data pre-processing, sentiment analysis (SA), and text generation using a fine-tuned Large Language Model (LLM). We design and propose a recommendation system framework, within which we implement multiple variants of DRecSys-SUSA using different combinations of AI techniques. Each variant generates medically relevant suggestions to user-specific inputs such as age, symptoms, and current medications. Through an iterative process of implementation and evaluation using an LLM-as-judge methodology with AI-generated real-world scenarios, we identify which AI techniques are most beneficial for providing clinically appropriate and user-friendly drug recommendations. The resulting insights contribute to the advancement of AI-driven healthcare tools by establishing effective approaches for leveraging user-generated content in medical recommendation systems.
- Intelligent sports weightsPublication . Duarte, Olga dos Santos; Jacinto, Gustavo; Véstias, Mário; Véstias, Mário; Duarte, Rui Policarpo; Duarte, RuiWeightlifting is a common fitness activity and can be practiced individually without supervision. However, performing regular weightlifting exercises without any form of feedback can lead to serious injuries. To counter this, this work proposes a different approach to automatic weightlifting supervision off-the-person. The proposed embedded system is coupled to the weights and evaluates if they follow the correct trajectory in real time. The system is based on a low-power embedded System-on-a-Chip to perform the classification of the correctness of physical exercises using a Convolutional Neural Network with data from the embedded IMU. It is a low-cost solution and can be adapted to the characteristics of specific exercises to fine-tune the performance of the athlete. Experimental results show real-time monitoring capability with an average accuracy close to 95%. To favor its use, the prototypes have been enclosed on a custom 3D case and validated in an operational environment. All research outputs, developments, and engineering models are publicly available.
- A new methodology for evaluating the neighbor discovery time in schedule-based asynchronous duty-cycling wireless sensor networksPublication . Passos, Diego; Trabbold, Beatriz; Carrano, Ricardo C.; Sousa, Cledson deDuty cycling is a fundamental mechanism for battery-operated wireless networks, such as wireless sensor networks. Due to its importance, it is an integral part of several Medium Access Protocols and related wireless technologies. In Schedule-based Asynchronous Duty Cycle, nodes activate and deactivate their radio interfaces according to a pre-designed schedule of slots, which guarantees overlapping uptime between two neighbors, independent of the offset between their internal clocks, making communication between them possible. This paper presents a new methodology for evaluating the Neighbor Discovery Time (NDT) of Schedule-based Asynchronous Duty Cycle. Differently from previous methodologies, it accounts for the possibility of the slots in the schedules of the two neighbors not being perfectly border-aligned - an unrealistic assumption in practice. By means of simulation, we show that not taking this under consideration can lead to an overestimate of the NDT by a factor of 2 depending on the particular scenario, thus justifying the importance of our work. center dot We propose a new subslot-based methodology for computing the NDT of a wakeup schedule used for asynchronous duty cycling. center dot It replaces the traditional slot-based methodology, by dividing slots into subslots, allowing for the analysis of non-integer clock offsets between nodes, and further allowing mathematical models to consider the more realistic continuous-time case. center dot Our validation data shows that the slot-based methodology may overestimate NDT by a factor of up to 2, making the proposed subslot-based methodology much more precise.
- Novel incremental conductance feedback method with integral compensator for maximum power point tracking: A comparison using hardware in the loopPublication . André, Sérgio; Silva, Fernando; Pinto, Sónia; Miguens Matutino, PedroResearch on renewable energy sources and power electronic converters has been increasing due to environmental concerns. Many countries have established targets to decrease CO2 emissions and boost the proportion of renewable energy, with solar power being a prominent area of investigation in the recent literature. Techniques are being developed to optimize the energy recovered from PV cells and increase system efficiency, including modeling PV cells, the use of converter topologies to connect PV systems to high-power inverters, and the use of MPPT methods. Certain MPPT algorithms are intricate and demand high processing power. The literature describes several MPPT methods; however, the number of hardware resources required by MPPT algorithms is typically not disclosed. This work proposes a novel MPPT technique based on integral feedback conductance and incremental conductance error, considering the current dynamics of the boost converter. This MPPT algorithm is compared to the most widely used techniques in the literature and evaluates each method’s efficiency, performance, and computational needs using an HIL system. Comparisons are made with well-known MPPT algorithms, such as perturb and observe, incremental conductance, and newer techniques based on fuzzy logic and neural networks (NNs). As the NN that is most widely used in the literature depends on irradiation and temperature, an additional NN that is trained using the proposed method is also investigated.
- phyloDB: A framework for large-scale phylogenetic analysis of sequence based typing dataPublication . Lourenço, Bruno; Vaz, Cátia; Coimbra, Miguel E.; Francisco, Alexandre P.phyloDB is a modular and extensible framework for large-scale phylogenetic analyses of sequence based typing data, which are essential for understanding epidemics evolution. It relies on the Neo4j graph database for data storage and processing, providing a schema and an API for representing and querying phylogenetic data. Custom algorithms are also supported, allowing users to perform heavy computations directly over the data, and to store results in the database. Multiple computation results are stored as multilayer networks, promoting and facilitating comparative analyses, as well as avoiding unnecessary ab initio computations. The experimental evaluation results showcase that phyloDB is efficient and scalable with respect to both API operations and algorithms execution.
- Real-time GPS indoor for USV tracking using lookup tablePublication . Vieira, Fábio; Teodoro, Pedro; Jorge, Pedro MendesThis study introduces an approach that utilizes Lookup Tables (LUT) to enable real-time tracking of an Unmanned Surface Vehicle (USV) in an indoor setting, using a fish-eye camera. The proposed method streamlines image processing and achieves O(1) complexity, significantly reducing application run time. The paper also outlines the process of calibrating the fish-eye camera to correct image distortion, computing the homography matrix for re-projection, and obtaining a virtual top view of the camera’s field of view. The paper provides a detailed explanation of the replacement of the undistortion and re-projection steps with the new LUT method. Experimental results demonstrate a significant enhancement in the process’s run time, making it feasible for real-time tracking, regardless of the image size.