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    Telecommunications and Multimedia

    At CTM, our vision is to promote a lively and sustainable world where networked intelligence enables ubiquitous interaction with sensory-rich content. Our mission is to develop advanced systems and technologies to enable high capacity, efficient, and secure communications, media knowledge extraction, and immersive ubiquitous multimedia applications.

    We work in 4 main areas of research: Optical and Electronic Technologies, Wireless Networks, Multimedia and Communications Technologies, and VCMI (Visual Computing and Machine Intelligence).

    Latest News

    INESC TEC with five FCT exploratory projects approved in four R&D areas

    Telecommunications and Multimedia, Applied Photonics, High-assurance Software and Advanced Computing Systems – these are the four domains that INESC TEC researchers will explore within the scope of the five projects that were approved through the Call for Exploratory Projects promoted by the Foundation for Science and Technology (FCT).

    02nd October 2024

    Artificial Intelligence

    Já arrancou o primeiro projeto europeu liderado pelo INESC TEC na área da saúde

    Chama-se AI4Lungs e tem como objetivo desenvolver ferramentas e modelos computacionais baseados em Inteligência Artificial para otimizar o diagnóstico e o tratamento de doenças pulmonares. Através de uma abordagem holística e multimodal, os investigadores vão criar uma solução de cuidados de saúde personalizados para doenças respiratórias. No final de fevereiro, representantes das 18 entidades parceiras do projeto, provenientes de 10 países, reuniram-se no INESC TEC para assinalar o arranque do AI4Lungs.

    01st April 2024

    Communications

    Europe discusses collaboration opportunities in high-frequency wireless communications

    Smart propagation environments, improvements in signal processing for the sixth generation of mobile communications, and 6G-centred network and location developments were some of the topics discussed at an event organised by the European projects TERRAMETA (coordinated by INESC TEC), 6G-SHINE and TIMES, in collaboration with RESTART-IN – an Italian PRR.

    06th March 2024

    Artificial Intelligence

    INESC TEC researchers work on the first prototype that applies AI to colorectal diagnosis developed in Portugal

    The work behind the first prototype that uses Artificial Intelligence (AI) for colorectal diagnosis was fully developed by Portuguese researchers INESC TEC, and the IMP Diagnostics Molecular & Anatomic Pathology laboratory; the work featured in the renowned international scientific journal npj Precision Oncology (https://www.nature.com/articles/s41698-024-00539-4 ).

    05th March 2024

    INESC TEC researchers led discussion on wireless communications and computer vision at GLOBECOM

    After almost one year, the CONVERGE project (coordinated by INESC TEC) has already showed relevant outcomes at one of the main conferences of the IEEE Communications Society, the GLOBECOM (Malaysia) – namely, through the organisation of a panel. “Convergence of wireless communications and computer vision: a new paradigm created by the CONVERGE project” sought to discuss the new opportunities and potential challenges associated with the use of tools that combine radio with computer vision.

    23rd January 2024

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    Featured Projects

    INESCTEC.OCEAN

    Centre of Excellence in Ocean Research and Engineering

    2025-2030

    Team
    Vítor Grade Tavares

    Centre Coordinator

    Filipe André Ribeiro

    Centre Coordinator

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    002

    Laboratories

    Laboratory of Sound and Music Computing

    Optical and Electronic Technologies Research Laboratory

    Publications

    CTM Publications

    View all Publications

    2025

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

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

    Publication
    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.

    CloseRead Abstract

    2025

    Model compression techniques in biometrics applications: A survey

    Authors
    Caldeira, E; Neto, PC; Huber, M; Damer, N; Sequeira, AF;

    Publication
    INFORMATION FUSION

    Abstract
    The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. These compressed models are especially essential when implementing multi-model fusion solutions where multiple models are required to operate simultaneously. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.

    CloseRead Abstract

    2024

    Incremental Redundancy HARQ Communication Schemes applied to Energy Efficient IoT Systems

    Authors
    Silva S.M.; Almeida N.T.;

    Publication
    2024 IEEE 22nd Mediterranean Electrotechnical Conference, MELECON 2024

    Abstract
    The rapid proliferation of Internet of Things (IoT) systems, encompassing a wide range of devices and sensors with limited battery life, has highlighted the critical need for energy-efficient solutions to extend the operational lifespan of these battery-powered devices.One effective strategy for reducing energy consumption is minimizing the number and size of retransmitted packets in case of communication errors. Among the potential solutions, Incremental Redundancy Hybrid Automatic Repeat reQuest (IR-HARQ) communication schemes have emerged as particularly compelling options by adopting the best aspects of error control, namely, automatic repetition and variable redundancy.This work addresses the challenge by developing a simulator capable of executing and analysing several (H)ARQ schemes using different channel models, such as the Additive White Gaussian Noise (AWGN) and Gilbert-Elliott (GE) models. The primary objective is to compare their performance across multiple metrics, enabling a thorough evaluation of their capabilities.The results indicate that IR-HARQ outperforms alternative methods, especially in the presence of burst errors. Furthermore, its potential for further adaptation and enhancement opens up new ways for optimizing energy consumption and extending the lifespan of battery-powered IoT devices.

    CloseRead Abstract

    2024

    Memory Optimization for FPGA Implementation of Correlation-Based Beamforming

    Authors
    Avelar, H; Ferreira, JC;

    Publication
    2024 IEEE 22nd Mediterranean Electrotechnical Conference, MELECON 2024

    Abstract
    This paper proposes a method to avoid using a CORDIC or external memory to process the steering vectors to calculate the pseudospectrum of correlation-based beamforming algorithms. We show that if we decompose the steering vector equation, the size of the matrix to be saved in memory becomes independent of the antenna array size. Besides, the amount of data needed is small enough to be saved in the internal block RAMs of the FPGA SoC. Besides, this method greatly reduces the number of memory accesses, by offloading some processing to hardware, while keeping the frequency at 300MHz with a precision of 0.25°. Finally, we show that this approach is scalable since the complexity grows logarithmically for bigger arrays, and the symmetry in the matrices obtained allows even more compact data. © 2024 IEEE.

    CloseRead Abstract

    2024

    Vision-Radio Experimental Infrastructure Architecture Towards 6G

    Authors
    Teixeira, FB; Ricardo, M; Coelho, A; Oliveira, HP; Viana, P; Paulino, N; Fontes, H; Marques, P; Campos, R; Pessoa, LM;

    Publication
    CoRR

    Abstract

    Facts & Figures

    82Researchers

    2016

    15Academic Staff

    2020

    2R&D Employees

    2020

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    Contacts
    Filipe André Ribeiro

    Coordinator

    +351222094299

    filipe.a.ribeiro@inesctec.pt