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    Apresentação

    Centro de Telecomunicações e Multimédia

    A nossa visão é promover um mundo animado e sustentável onde a inteligência em rede permite uma interação ubíqua com o conteúdo sensorial. A missão é desenvolver sistemas e tecnologias avançadas para permitir comunicações de alta capacidade, eficientes e seguras, extração de conhecimento dos média e aplicações multimédia ubíquas imersivas.

    No CTM trabalhamos em quatro áreas principais de investigação: Tecnologias Óticas e Eletrónicas, Redes Sem Fios, Tecnologias Multimédia e de Comunicações, e Processamento de Informação e Reconhecimento de Padrões.

    Últimas Notícias

    INESC TEC com 5 projetos exploratórios FCT aprovados em 4 áreas de I&D

    Telecomunicações e multimédia, fotónica aplicada, software confiável e sistemas de computação avançada – são estas as quatro áreas que os investigadores do INESC TEC vão trabalhar no âmbito dos cinco projetos que foram aprovados através do Concurso de Projetos Exploratórios da Fundação para a Ciência e a Tecnologia (FCT).

    02 outubro 2024

    Inteligência Artificial

    Kick-off of the first European project led by INESC TEC in the health area

    It is called AI4Lungs; it aims to develop Artificial Intelligence (AI) tools and computational models to optimise the diagnosis and treatment of lung diseases. Through a holistic and multimodal approach, researchers will create a personalised healthcare solution for respiratory diseases. In late February, representatives of the 18 partner entities of the project (from 10 countries) met at INESC TEC to kick off the AI4Lungs project.

    01 abril 2024

    Comunicações

    Europa discute oportunidades de colaboração na área de comunicações sem fios em alta frequência

    Ambientes de propagação inteligentes, melhorias no processamento de sinal para a sexta geração de comunicações móveis e desenvolvimentos de rede e localização orientados para 6G foram alguns dos temas em debate num evento organizado pelos projetos europeus TERRAMETA, coordenado pelo INESC TEC, 6G-SHINE e TIMES, em colaboração com o RESTART-IN – um PRR Italiano.

    06 março 2024

    Inteligência Artificial

    Investigadores INESC TEC no primeiro protótipo desenvolvido em Portugal que aplica IA ao diagnóstico colorretal

    O trabalho que levou ao primeiro protótipo que aplica Inteligência Artificial (IA) ao diagnóstico colorretal totalmente desenvolvido por portugueses contou com investigadores do INESC TEC e do laboratório de Anatomia e Patologia Molecular (IMP Diagnostics). O trabalho foi publicado na reconhecida revista científica internacional npj Precision Oncology (https://www.nature.com/articles/s41698-024-00539-4).

    05 março 2024

    Investigadores do INESC TEC lideram discussão sobre comunicações sem fios e visão computacional na GLOBECOM

    Quase a completar o seu primeiro ano de duração, o projeto CONVERGE, coordenado pelo INESC TEC, já deu cartas numa das principais conferências da IEEE Communications Society, a GLOBECOM, na Malásia, com a organização de um painel. “Convergence of wireless communications and computer vision: a new paradigm created by the CONVERGE project” procurou debater as novas oportunidades e desafios potenciais que podem ser antecipados pela utilização de ferramentas que combinam rádio com visão computacional.

    23 janeiro 2024

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    001

    Projetos Selecionados

    PFAI4_5eD

    Programa de Formação Avançada Industria 4 - 5a edição

    2024-2024

    Equipa
    Vítor Grade Tavares

    Coordenador de Centro

    Filipe André Ribeiro

    Coordenador de Centro

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    002

    Laboratórios

    Laboratório de Computação Musical e Sonora

    Laboratório de Tecnologias Óticas e Eletrónicas

    Publicações

    CTM Publicações

    Ler todas as publicações

    2025

    Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables

    Autores
    Caetano, R; Oliveira, JM; Ramos, P;

    Publicação
    MATHEMATICS

    Abstract
    Accurate demand forecasting is essential for retail operations as it directly impacts supply chain efficiency, inventory management, and financial performance. However, forecasting retail time series presents significant challenges due to their irregular patterns, hierarchical structures, and strong dependence on external factors such as promotions, pricing strategies, and socio-economic conditions. This study evaluates the effectiveness of Transformer-based architectures, specifically Vanilla Transformer, Informer, Autoformer, ETSformer, NSTransformer, and Reformer, for probabilistic time series forecasting in retail. A key focus is the integration of explanatory variables, such as calendar-related indicators, selling prices, and socio-economic factors, which play a crucial role in capturing demand fluctuations. This study assesses how incorporating these variables enhances forecast accuracy, addressing a research gap in the comprehensive evaluation of explanatory variables within multiple Transformer-based models. Empirical results, based on the M5 dataset, show that incorporating explanatory variables generally improves forecasting performance. Models leveraging these variables achieve up to 12.4% reduction in Normalized Root Mean Squared Error (NRMSE) and 2.9% improvement in Mean Absolute Scaled Error (MASE) compared to models that rely solely on past sales. Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments.

    FecharLer Abstract

    2025

    Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions

    Autores
    Costa, V; Oliveira, JM; Ramos, P;

    Publicação

    Abstract
    This study investigates the integration of deep learning for single-modality and multimodal data within materials science. Traditional methods for materials discovery are often resource-intensive and slow, prompting the exploration of machine learning to streamline the prediction of material properties. While single-modality models have been effective, they often miss the complexities inherent in material data. The paper explores multimodal data integration—combining text, images, and tabular data—and demonstrates its potential to improve predictive accuracy. Utilizing the Alexandria dataset, the research introduces a custom methodology involving multimodal data creation, model tuning with AutoGluon framework, and evaluation through targeted fusion techniques. Results reveal that multimodal approaches enhance predictive accuracy and efficiency, particularly when text and image data are integrated. However, challenges remain in predicting complex features like band gaps. Future directions include incorporating new data types and refining specialized models to improve materials discovery and innovation.

    FecharLer Abstract

    2025

    A survey on cell nuclei instance segmentation and classification: Leveraging context and attention

    Autores
    Nunes, JD; Montezuma, D; Oliveira, D; Pereira, T; Cardoso, JS;

    Publicação
    MEDICAL IMAGE ANALYSIS

    Abstract
    Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&Estains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms in ANNs, the scientific understanding of these methods should first be addressed.

    FecharLer Abstract

    2025

    CNN explanation methods for ordinal regression tasks

    Autores
    Gómez, JB; Cruz, RPM; Cardoso, JS; Gutiérrez, PA; Martínez, CH;

    Publicação
    Neurocomputing

    Abstract

    2025

    CNN explanation methods for ordinal regression tasks

    Autores
    Barbero-Gómez, J; Cruz, RPM; Cardoso, JS; Gutiérrez, PA; Hervás-Martínez, C;

    Publicação
    NEUROCOMPUTING

    Abstract
    The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBDCAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.

    FecharLer Abstract

    Factos & Números

    2Capítulos de livros

    2020

    15Docentes do Ensino Superior

    2020

    28Investigadores Séniores

    2016

    Ver todos os Factos & Números
    Contactos
    Filipe André Ribeiro

    Coordenador

    +351222094299

    filipe.a.ribeiro@inesctec.pt