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    Telecommunications and Multimedia

    The Centre for Telecommunications and Multimedia (CTM) welcomes close to 200 members, including at least 100 integrated researchers who carry out scientific work in the fields of communications, Artificial Intelligence, and computer science and engineering.


    The Centre’s activities cover several Research and Development (R&D) domains:

    • Communications and Electronics
      • Radio Frequency Technologies
      • Optoelectronics
      • Microelectronics
      • Wireless Communication Networks
    • Computer Perception
      • Computer Vision applied to Medical Imaging
      • Computer Vision applied to Digital Media
      • Computer Audio applied to Music

    With multidisciplinary teams that include dozens of PhDs, CTM is strongly committed to both European and national research projects, as well as consultancy projects with industry.

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    Publications

    CTM Publications

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    2026

    The 15-Minute City in Porto, Portugal: Accessibility for the elderly

    Authors
    Guerreiro, MS; Dinis, MAP; Sucena, S; Silva, I; Pereira, M; Ferreira, D; Moreira, RS;

    Publication
    CITIES

    Abstract
    The concept of the 15-Minute City aims to enhance urban accessibility by ensuring that essential services are within a short walking distance. This study evaluates the accessibility of Porto, Portugal, particularly for the elderly, by assessing urban density, permeability, and walkability, with a specific focus on crossings and ramps. A five-step methodology was employed, including spatial analysis using QGIS and Place Syntax Tool, proximity assessments, and an in-situ survey of crossings and ramps in the CHP. The results indicate that while the city of Porto offers a dense and walkable urban environment, significant accessibility challenges remain due to inadequate ramp distribution. The data collection identified 80 crossings, of which only 60 were listed in OpenStreetMap, highlighting data inconsistencies. Additionally, 18 crossings lacked curb ramps, posing mobility barriers for elderly residents. These findings highlight the need of infrastructure improvements to support inclusive urban mobility. The study also proposes an automated method to enhance ramp data collection for broader applications. Addressing these gaps is crucial for achieving the equity and sustainability goals of the 15-Minute City model, ensuring that aging populations can navigate urban spaces safely and efficiently.

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    2026

    Deciphering the Silent Signals: Unveiling Frequency Importance for Wi-Fi-Based Human Pose Estimation with Explainability

    Authors
    Capozzi, L; Ferreira, L; Gonçalves, T; Rebelo, A; Cardoso, JS; Sequeira, AF;

    Publication
    PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2025, PT II

    Abstract
    The rapid advancement of wireless technologies, particularly Wi-Fi, has spurred significant research into indoor human activity detection across various domains (e.g., healthcare, security, and industry). This work explores the non-invasive and cost-effective Wi-Fi paradigm and the application of deep learning for human activity recognition using Wi-Fi signals. Focusing on the challenges in machine interpretability, motivated by the increase in data availability and computational power, this paper uses explainable artificial intelligence to understand the inner workings of transformer-based deep neural networks designed to estimate human pose (i.e., human skeleton key points) from Wi-Fi channel state information. Using different strategies to assess the most relevant sub-carriers (i.e., rollout attention and masking attention) for the model predictions, we evaluate the performance of the model when it uses a given number of sub-carriers as input, selected randomly or by ascending (high-attention) or descending (low-attention) order. We concluded that the models trained with fewer (but relevant) sub-carriers are competitive with the baseline (trained with all sub-carriers) but better in terms of computational efficiency (i.e., processing more data per second).

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    2026

    A Distributed Electric Vehicles Charging System Powered by Photovoltaic Solar Energy with Enhanced Voltage and Frequency Control in Isolated Microgrids

    Authors
    Baltazar, P; Barros, JD; Gomes, L;

    Publication
    Electronics

    Abstract
    This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation.

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    2026

    Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models

    Authors
    Patrício, C; Barbano, CA; Fiandrotti, A; Renzulli, R; Grangetto, M; Teixeira, LF; Neves, JC;

    Publication
    PATTERN RECOGNITION LETTERS

    Abstract
    Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). In the context of brain MRIs, existing CA approaches rely on supervised contrastive learning or variational autoencoders (VAEs) using both healthy and unhealthy data, but such reliance on target samples is challenging in clinical settings. Unsupervised Anomaly Detection (UAD) learns a reference representation of healthy anatomy, eliminating the need for target samples. Deviations from this reference distribution can indicate potential anomalies. In this context, diffusion models have been increasingly adopted in UAD due to their superior performance in image generation compared to VAEs. Nonetheless, precisely reconstructing the anatomy of the brain remains a challenge. In this work, we bridge CA and UAD by reformulating contrastive analysis principles for the unsupervised setting. We propose an unsupervised framework to improve the reconstruction quality by training a self-supervised contrastive encoder on healthy images to extract meaningful anatomical features. These features are used to condition a diffusion model to reconstruct the healthy appearance of a given image, enabling interpretable anomaly localization via pixel-wise comparison. We validate our approach through a proof-of-concept on a facial image dataset and further demonstrate its effectiveness on four brain MRI datasets, outperforming baseline methods in anomaly localization on the NOVA benchmark.

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    2026

    Enhancing Medical Image Analysis: A Pipeline Combining Synthetic Image Generation and Super-Resolution

    Authors
    Sousa, P; Campai, D; Andrade, J; Pereira, P; Goncalves, T; Teixeira, LF; Pereira, T; Oliveira, HP;

    Publication
    PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2025, PT II

    Abstract
    Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field's capabilities.

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